U.S. patent application number 10/731816 was filed with the patent office on 2010-03-25 for methods and apparatus for monitoring consciousness.
Invention is credited to David Burton, Eugene Zilberg.
Application Number | 20100076333 10/731816 |
Document ID | / |
Family ID | 23148619 |
Filed Date | 2010-03-25 |
United States Patent
Application |
20100076333 |
Kind Code |
A9 |
Burton; David ; et
al. |
March 25, 2010 |
METHODS AND APPARATUS FOR MONITORING CONSCIOUSNESS
Abstract
The systems of the present invention provide improved accuracy
in monitoring, analysing, detecting, predicting and/or providing
alerts and alarms associated with depth of anaesthesia, depth of
consciousness, hypnotic state, sedation depth, fatigue or vigilance
of a subject, with as few as 3 surface electrodes. The systems
incorporate real-time phase, amplitude and frequency analysis of a
subject's electro-encephalogram. The systems weight outputs of
various types of analyses to produce an integrated analysis or
display for precise indication or alert to users of the systems
including anaesthetists, nurses and other medical personnel,
transport drivers and machine workers. The systems weight the
outputs of one or more analysis algorithms including combinations
of simultaneous, real-time R&K analysis, AEP spectral
analysis-SEF-MF, Bi-coherence analysis, initial wave analysis,
auditory response, arousal analysis, body movement analysis, 95%
spectral edge analysis and anaesthetic phase and spectral energy
variance measurement in association with a subject's state of
consciousness.
Inventors: |
Burton; David; (Victoria,
AU) ; Zilberg; Eugene; (Victoria, AU) |
Correspondence
Address: |
SUGHRUE MION, PLLC
2100 PENNSYLVANIA AVENUE, N.W.
SUITE 800
WASHINGTON
DC
20037
US
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20040193068 A1 |
September 30, 2004 |
|
|
Family ID: |
23148619 |
Appl. No.: |
10/731816 |
Filed: |
December 9, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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PCT/AU2002/000776 |
Jun 13, 2002 |
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10731816 |
Dec 9, 2003 |
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Current U.S.
Class: |
600/544 ;
128/920; 600/595 |
Current CPC
Class: |
A61B 5/021 20130101;
A61B 5/16 20130101; A61B 5/398 20210101; A61B 5/7207 20130101; A61B
5/14551 20130101; A61B 5/7264 20130101; A61B 5/6821 20130101; A61B
5/7257 20130101; G16H 50/20 20180101; A61B 5/318 20210101; A61B
5/411 20130101; A61B 5/7239 20130101; A61B 5/369 20210101; A61B
5/374 20210101; A61B 5/4809 20130101; A61B 5/4812 20130101; A61B
5/11 20130101 |
Class at
Publication: |
600/544 ;
600/595; 128/920 |
International
Class: |
A61B 5/04 20060101
A61B005/04; A61B 5/103 20060101 A61B005/103 |
Claims
1. A method of monitoring consciousness of a sentient subject and
automatically detecting whether the subject is in a transition from
a conscious state to a less conscious state or vice versa, by
reducing effects of frequency based changes in neurological data
from the subject, said method comprising: (i) obtaining an EEG
signal from the subject; (ii) performing a frequency based analysis
of the EEG signal to obtain a frequency-based signal; (iii)
performing a phase based analysis of the EEG signal to obtain a
phase-based signal ; (iv) detecting by comparing the frequency
based signal and the phase based signal whether the subject is in
transition from said conscious state to said less conscious state
or vice versa; and (v) providing a warning signal when said subject
is in said transition to said conscious state.
2. The method according to claim 1 wherein said frequency based
analysis includes depth of sleep analysis and said phase-based
analysis includes at least one of optimized bicoherence, bispectrum
or triple product analysis.
3. The method according to claim 2 wherein said depth of sleep
analysis includes real-time optimized R&K analysis.
4. The method according to claim 1 wherein said step of detecting
is augmented with optimized AEP analysis.
5. The method according to claim 1 further comprising a means for
adapting to parameters specific to said subject including body mass
index, age and sex of said subject.
6. A method of processing a non-stationary signal including
segments having increasing and decreasing amplitude representing
physiological characteristics of a sentient subject, said segments
including portions in which said signal changes from increasing to
decreasing amplitude or vice versa, said method comprising: (i)
detecting each segment by determining time instants when a time
derivative of said signal is substantially equal to zero; (ii)
performing syntactic analysis for each segment including assigning
height, width and error parameters; (iii) identifying noise
segments present in said signal by comparing said width parameter
to a preset threshold and said error parameter to said height
parameter; (iv) removing said noise segments by replacing each
identified noise segment with a substantially straight line; (v)
sorting the remaining segments into a plurality of wavebands based
on their width parameters; and (vi) classifying said signal as
belonging to one of predefined sleep states based on relative
frequency of occurrence of said segments in said wavebands.
7. A method of monitoring physiological characteristics of a
sentient subject comprising: applying a first surface electrode to
said subject to provide a first electrical signal to a remote
monitoring apparatus; applying a second surface electrode to said
subject to provide a second electrical signal to said remote
monitoring apparatus; monitoring quality of said first electrical
signal; in the event of a degradation in said quality of first
signal, automatically substituting said second electrical signal
for said first electrical signal; in the event of a degradation in
said quality of said second electrical signal and in said quality
of said first electrical signal, providing a warning signal.
8. The method according to claim 7 wherein said second electrode is
spaced from said first electrode.
9. An apparatus for processing a non-stationary signal including
segments having increasing and decreasing amplitude representing
physiological characteristics of a sentient subject, said segments
including portions in which said signal changes from increasing to
decreasing amplitude or vice versa, said apparatus comprising: (i)
means for detecting each segment by determining time instants when
a time derivative of said signal is substantially equal to zero;
(ii) means for dividing said signal into said segments including
data over three consecutive time instants when said time derivative
is equal to zero; (iii) means for assigning to each segment,
height, width and error parameters; (iv) means for identifying
noise segments in said signal including means for comparing for
each segment said width parameter to a preset threshold and said
error parameter to said height parameter; (v) means for removing
said noise segments including means for substituting a straight
line connecting first and third time instants when the time
derivative of said signal is substantially equal to zero and means
for reassigning segments and their parameters after the
substitution; (vi) means for sorting the remaining segments into a
plurality of wave bands based on the value of their width
parameter, each wave band being defined by upper and lower
frequencies corresponding to lower and upper values for the width
parameter respectively; and (vii) means for classifying a time
interval of the signal data as belonging to one of predefined sleep
states based on relative frequency of occurrence of said segments
in said wave bands.
10. The apparatus according to claim 9 wherein said time derivative
is equal to zero when said signal changes its direction from
positive to negative or from negative to positive.
11. The apparatus according to claim 9 wherein each height
parameter is assigned by calculating an average of the signal's
variations between the first and second time instants when the time
derivative of said signal is substantially equal to zero, and the
second and third time instants when the time derivative of said
signal is substantially equal to zero.
12. The apparatus according to claim 9 wherein each width parameter
is assigned by calculating an average time interval between any
data point within the segment and a second time instant when the
time derivative of said signal is substantially equal to zero, said
intervals being weighted according to the signal's variation
between each respective data point and an adjacent data point
nearest to the second time instant when the time derivative of said
signal is substantially equal to zero.
13. The apparatus according to claim 9 wherein said error parameter
is assigned by calculating an average deviation between current
signal data and past signal data over a signal time interval.
14. The apparatus according to claim 9 wherein said means for
identifying noise segments includes means for testing each segment
to determine if its width parameter is less than said preset
threshold and its error parameter is less than its height parameter
by at least a preset ratio.
15. The apparatus according to claim 9 wherein said means for
reassigning repeats a procedure of reassigning segments and their
parameters and said means for substituting performs a substitution
until no noise segments are identified in said signal.
16. The apparatus according to claim 9 wherein said means for
classifying includes means for comparing to a preset threshold
values of weighted combinations of occurrences of said segments in
said wavebands.
17. The apparatus according to claim 9 including means for
detecting and processing artefact patterns in said signal,
including one or more of: means for detecting flat intervals in
said signal; means for detecting intervals in said signal having a
relatively sharp slope, being intervals in which variation in said
signal exceeds a first threshold over a time interval equal to or
shorter than a second threshold; means for detecting intervals in
said signal having a relatively narrow peak, being intervals in
which the width parameter is equal to or less than a third
threshold and the height parameter is equal to or greater than a
fourth threshold; and means for detecting other non-physiological
pattern in said signal, being combinations of segments having a
width and height of one, the segments in the combination being less
than the respective total duration and signal variation of the
combination by at least preset ratios.
18. The apparatus according to claim 9 including means for
detecting and processing wave patterns characterized by minimum
amplitude and minimum and maximum durations, including: means for
detecting a core interval of the wave pattern as a sequence of one
or more segments which starts at a first time instant of a first
segment when a time derivative of said signal is substantially
equal to zero and ends at a second time instant of the last segment
when a time derivative of said signal is substantially equal to
zero, or starts at the second time instant of the first segment
when the time derivative of said signal is substantially equal to
zero and ends at a third time instant of the last segment when the
time derivative of said signal is substantially equal to zero, with
the total signal variation of at least the minimum amplitude,
duration of at least a preset share of the minimum duration, less
than the maximum duration and the maximum deviation from a
monotonous change of at least a preset share of the total
variation.
19. The apparatus according to claim 9 including means for
detecting a start and end of a main wave of the wave pattern by
subsequent comparison with a preset threshold of a deviation of the
slope of respective components of segments preceding and following
the core interval from the slope of the core interval, and means
for updating the core interval if the deviation of the slope and
maximum deviation from the monotonous change do not exceed
respective preset thresholds, and a total updated duration is equal
to at least a preset share of the minimum duration and is less than
the maximum duration.
20. The apparatus according to claim 19 including means for
detecting one or two side waves of the wave pattern by subsequent
testing of sequences of combinations of segments preceding and
following the main wave for the signal duration conditions.
21. The apparatus according to claim 9 wherein said means for
sorting into a plurality of wave bands is based on the detected
wave patterns.
22. The apparatus according to claim 9 wherein said means for
classifying includes means for comparing to preset threshold values
of weighted combinations of occurrences of said segments in said
wave bands, artefact patterns and wave patterns.
23. The apparatus according to claim 9 including means for
detecting periodic patterns with specified minimum and maximum
frequencies, minimum amplitude and minimum number of waves
including: means for selecting combinations of a specified number
of segments; means or an assigning component for assigning for each
combination, an average, minimum and maximum amplitude and an
average, minimum and maximum period; means for testing if the
average amplitude exceeds a specified minimum amplitude for a
periodic pattern; means for testing if the maximum amplitude
exceeds the minimum amplitude by not more than a specified ratio;
means for testing if the frequency corresponding to the average
period is equal to or greater than the minimum frequency of the
periodic pattern and is equal to or less than the maximum frequency
of the periodic pattern; means for testing if the maximum period
for a combination of segments exceeds the minimum period by not
more than a specified ratio; means for joining combinations of
segments, which comply with the above criteria; and means for
classifying a time interval of the signal data as belonging to one
of predefined states on the basis of a comparison of the value of a
weighted combination of durations of a plurality of wave bands,
artefact patterns and wave patterns with a threshold which is set
to a different value depending on the total relative duration of
periodic patterns within the time interval.
24. The apparatus according to claim 10 including means for
classifying a time interval of the signal data as belonging to one
of predefined states on the basis of a comparison of the value of a
weighted combination of durations of a plurality of wave bands,
artefact patterns and wave patterns with a decision boundary which
is set to a different value depending on the total relative
duration of periodic patterns within the time interval, if the
difference between the value and the decision boundary is equal to
or greater than a specified margin, or otherwise, on the basis of a
comparison of this value with the respective value for the
preceding or following time interval providing that that interval
is already classified and the difference between the respective
values is equal or less than the specified margin, or otherwise, if
after subsequent passes through the data, an interval is still not
resolved, on the basis of comparison of this value with a threshold
which is set to a different value depending on the total relative
duration of periodic patterns within the time interval.
25. A sensor for detecting position of an eye lid comprising: first
means adapted to move substantially with said eye lid and relative
to a reference component; and means for providing an electrical
signal indicative of the position of said first means relative to
said reference component, such that said signal includes a measure
of position and/or degree of opening of said eye lid.
26. The sensor according to claim 25 wherein said first means and
said reference component are electrically coupled such that said
coupling provides said measure of position and/or degree of opening
of said eye lid.
27. The sensor according to claim 25 wherein said first means and
said reference component are provided by respective arms connected
for relative movement.
28. The sensor according to claim 27 wherein said arms are
pivotably connected to each other.
29. The sensor according to claim 27 wherein each arm includes a
capacitive element arranged such that the extent of overlap between
the arms determines the coupling between the capacitive
elements.
30. The sensor according to claim 29 wherein each capacitive
element includes one plate of a capacitor.
31. The sensor according to claim 29 including means for measuring
capacitance between said capacitive elements.
32. A sensor according to claim 27 wherein each arm includes an
inductive element arranged such that the extent of overlap between
the arms determines the coupling between the inductive
elements.
33. A sensor according to claim 31 wherein each inductive element
include a coil.
34. A sensor according to claim 32 including means for measuring
inductive coupling between said inductive elements.
35. Apparatus for processing a non-stationary signal including
segments having increasing and decreasing amplitude representing
physiological characteristics of a sentient subject, said segments
including portions in which said signal changes from increasing to
decreasing amplitude or vice versa, said apparatus including: (i) a
detector which detects each segment by determining time instants
when a time derivative of said signal is substantially equal to
zero; (ii) a divider which dividing said signal into said segments
including data over three consecutive time instants when said time
derivative is equal to zero; (iii) an assigning component which
assigns to each segment, height, width and error parameters; (iv)
an identifier which identifies noise segments in said signal
including a comparing component which compares which each segment
said width parameter to a preset threshold and said error parameter
to said height parameter; (v) a removing component which removes
said noise segments including a substituting component which
substitutes a straight line connecting first and third time
instants when the time derivative of said signal is substantially
equal to zero and a reassigning component which reassigning
segments and their parameters after the substitution; (vi) a sorter
which sorts the remaining segments into a plurality of wave bands
based on the value of their width parameter, each wave band being
defined by upper and lower frequencies corresponding to lower and
upper values which the width parameter respectively; and (vii) a
classifier which classifies a time interval of the signal data as
belonging to one of predefined sleep states based on relative
frequency of occurrence of said segments in said wave bands.
36. The apparatus according to claim 35 wherein said identifying
component includes a testing component which tests each segment to
determine if its width parameter is less than said preset threshold
and its error parameter is less than its height parameter by at
least a preset ratio.
37. The apparatus according to claim 35 wherein said reassigning
component repeats a procedure of reassigning segments and their
parameters and said substituting component performs a substitution
until no noise segments are identified in said signal.
38. The apparatus according to claim 35 wherein said classifying
component includes a comparing component which compares to a preset
threshold values of weighted combinations of occurrences of said
segments in said wavebands.
39. The apparatus according to claim 35 including a first detecting
and processing component which detects and processes artefact
patterns in said signal, including one or more of: a second
detector which detects flat intervals in said signal; a third
detector which detects intervals in said signal having a relatively
sharp slope, being intervals in which variation in said signal
exceeds a first threshold over a time interval equal to or shorter
than a second threshold; a fourth detector which detects intervals
in said signal having a relatively narrow peak, being intervals in
which the width parameter is equal to or less than a third
threshold and the height parameter is equal to or greater than a
fourth threshold; and a fifth detector which detects other
non-physiological pattern in said signal, being combinations of
segments having a width and height of one, the segments in the
combination being less than the respective total duration and
signal variation of the combination by at least preset ratios.
40. The apparatus according to claim 39 including a sixth detector
and processing component which detects and processes wave patterns
characterized by minimum amplitude and minimum and maximum
durations, including: a seventh detector which detects a core
interval of the wave pattern as a sequence of one or more segments
which starts at a first time instant of a first segment when a time
derivative of said signal is substantially equal to zero and ends
at a second time instant of the last segment when a time derivative
of said signal is substantially equal to zero, or starts at the
second time instant of the first segment when the time derivative
of said signal is substantially equal to zero and ends at a third
time instant of the last segment when the time derivative of said
signal is substantially equal to zero, with the total signal
variation of at least the minimum amplitude, duration of at least a
preset share of the minimum duration, less than the maximum
duration and the maximum deviation from a monotonous change of at
least a preset share of the total variation.
41. The apparatus according to claim 40 including an eighth
detector which detects a start and end of a main wave of the wave
pattern by subsequent comparison with a preset threshold of a
deviation of the slope of respective components of segments
preceding and following the core interval from the slope of the
core interval, and an updating component which updating the core
interval if the deviation of the slope and maximum deviation from
the monotonous change do not exceed respective preset thresholds,
and a total updated duration is equal to at least a preset share of
the minimum duration and is less than the maximum duration.
42. The apparatus according to claim 41 including a ninth detector
which detects one or two side waves of the wave pattern by
subsequent testing of sequences of combinations of segments
preceding and following the main wave which the signal duration
conditions.
43. The apparatus according to claim 35 wherein said sorter
receives detected wave patterns.
43. The apparatus according to claim 35 wherein said classifier
includes a component which compares to preset threshold values of
weighted combinations of occurrences of said segments in wave
bands, artefact patterns and wave patterns.
44. The apparatus according to claim 42 including a tenth detector
which detects periodic patterns with specified minimum and maximum
frequencies, minimum amplitude and minimum number of waves
including: a selecting component which selects combinations of a
specified number of segments; an assigning component which
assigning for each combination, an average, minimum and maximum
amplitude and an average, minimum and maximum period; a first
testing component which tests if the average amplitude exceeds a
specified minimum amplitude for a periodic pattern; a second
testing component which tests if the maximum amplitude exceeds the
minimum amplitude by not more than a specified ratio; a third
testing component which tests if the frequency corresponding to the
average period is equal to or greater than the minimum frequency of
the periodic pattern and is equal to or less than the maximum
frequency of the periodic pattern; a fourth testing component which
tests if the maximum period for a combination of segments exceeds
the minimum period by not more than a specified ratio; a joining
component which joins combinations of segments, which comply with
the above criteria; and a first classifying component which
classifies a time interval of the signal data as belonging to one
of predefined states on the basis of a comparison of the value of a
weighted combination of durations of a plurality of wave bands,
artefact patterns and wave patterns with a threshold which is set
to a different value depending on the total relative duration of
periodic patterns within the time interval.
45. The apparatus according to claim 44 including a second
classifying component which classifies a time interval of the
signal data as belonging to one of predefined states on the basis
of a comparison of the value of a weighted combination of durations
of a plurality of wave bands, artefact patterns and wave patterns
with a decision boundary which is set to a different value
depending on the total relative duration of periodic patterns
within the time interval, if the difference between the value and
the decision boundary is equal to or greater than a specified
margin, or otherwise, on the basis of a comparison of this value
with the respective value for the preceding or following time
interval providing that that interval is already classified and the
difference between the respective values is equal or less than the
specified margin, or otherwise, if after subsequent passes through
the data, an interval is still not resolved, on the basis of
comparison of this value with a threshold which is set to a
different value depending on the total relative duration of
periodic patterns within the time interval.
46. A sensor which detecting position of an eye lid including: a
movable component adapted to move substantially with said eye lid
and relative to a reference component; and a signal providing
component which providing an electrical signal indicative of the
position of said movable component relative to said reference
component, such that said signal includes a measure of position
and/or degree of opening of said eyelid.
47. The sensor according to claim 46 wherein said movable component
and said reference component are provided by respective arms
connected for relative movement.
48. The sensor according to claim 47 wherein said arms are
pivotably connected to each other.
49. The sensor according to claim 47 wherein each arm includes a
capacitive element arranged such that the extent of overlap between
the arms determines the coupling between the capacitive
elements.
50. The sensor according to claim 49 wherein each capacitive
element includes one plate of a capacitor.
51. The sensor according to claim 49 including a measuring
component which measures the capacitance between said capacitive
elements.
52. A sensor according to claim 47 wherein each arm includes an
inductive element arranged such that the extent of overlap between
the arms determines the coupling between the inductive
elements.
53. A sensor according to claim 52 wherein each inductive element
include a coil.
54. A sensor according to claim 53 including a measuring component
which measure the inductive coupling between said inductive
elements.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to diverse methods and
apparatus including systems incorporating same, for selectively
monitoring the state of mind, or state of consciousness of human
and other sentient subjects. More particularly the present
invention relates to novel sensors and suites of sensors for
accurately monitoring, sensing, tracking, analysing, storing,
logging and/or displaying data related to combinations of
physiological senses of a sentient subject. The physiological
senses may include mind state and arousal of the subject including
frequency, phase, amplitude and/or activity of one or more
electro-encephalogram (EEG) signals.
[0002] The apparatus may be used in various configurations for
applications including, inter alia, depth of consciousness, depth
of unconsciousness, depth of anaesthesia, state of a subject's
alertness, depth of sedation, hypnotic state, state of
concentration, state of vigilance and state of attention. In a
particular application, the system of the present invention may be
adapted to monitor a subject for depth of anaesthesia and/or
present state of consciousness during anaesthesia administration so
that eg. the subject may be properly sedated during a medical
procedure. In addition, various data collecting and processing
techniques are described pursuant to the systems of the present
invention, as well as dynamic, re-configurable and adaptable
display configurations for such data. An operator may reference
such data as most optimally relates to the application (or
applications) set forth herein in readily understandable format
including suitable alarm signalling, threshold monitoring and the
like.
[0003] The systems may utilize sleep analysis, EEG bispectral
analysis (incorporating bi-coherence) and audio evoked potential
(AEP) analysis in an integrated fashion for improved monitoring of,
inter alia, a subject's consciousness, audio sensory systems,
movement, arousal, muscle activity, eye movement, eye opening,
stress and anxiety levels, vital sign parameters, and/or
audio-visual recall. The monitoring systems preferably are arranged
such that associated physiological electrode attachments are
minimized.
[0004] The present invention is related to systems disclosed in PCT
application AU99/01166 filed on 24 Dec. 1999 entitled "Vigilance
Monitoring System", the disclosure of which is incorporated herein
by cross reference.
BACKGROUND OF THE INVENTION
[0005] William Thomas Gordon Morton first demonstrated what is
today referred to as surgical anaesthesia. However, a comprehensive
or detailed understanding of how anaesthesia works is still unknown
today. It is known that anaesthesia acts upon the central nervous
system by reacting with membranes of nerve cells in the brain in
order to shut down responses such as sight, touch and awareness,
but the precise mechanisms and affects of this sensory process are
still a subject of research.
[0006] In Australia about 1 million people a year undergo general
anaesthesia. Of these 1 million people about 5 people die each
year, as a direct result of the anaesthesia, while about 3000 more
will be inadequately anaesthetised. These inadequately
anaesthetised people will experience a range of symptoms from
hearing recall while undergoing a medical procedure, sight recall
from premature recovery and the early opening of eyes, stress and
anxiety from experiencing paralysis. Some degree of mental
awareness to the medical procedure being instigated, memory recall
from having some degree of consciousness, and operation mishaps can
occur in cases where the subject's state of paralysis is not
adequate leading to movement of the subject's body during incision,
for example.
[0007] A typical general anaesthetic procedure may involve a
pre-medication or sedative, after which the patient is wheeled into
the operating theatre where the anaesthetist applies a
blood-pressure measurement cuff to the patient's arm, an oximeter
probe to the patient's finger for the measurement of oxygen
saturation, and ECG or electrocardiogram leads to a patient's chest
for monitoring of heart-rate.
[0008] An intravenous cannula is then inserted into the patient's
arm, and a mixture of drugs are infused into the blood-stream in
order to put the patient to sleep, control pain and relax muscles.
Within about 30 seconds the patient will typically transition from
a state of consciousness to unconsciousness. Once the patient is
unconscious, the anaesthetist typically reverts the patient to a
gas delivery mask, which contains an "inhalation" anaesthetic that
is breathed, by the patient through the mask. The patient may also
be attached to a ventilator that will assist or support the
patient's ventilation during the operation. The surgeon's intent is
to commence the medical operation procedure when the patient is
unconsciousness and can feel no pain.
[0009] The current state of the art provides an array of systems to
monitor a patient whilst undergoing anaesthetic drug delivery, but
none of these accommodate monitoring and validation of the range of
sensory parameters satisfactory to monitor for "shut-down" or
unconscious state of neural recall (including state of hypnosis,
unconsciousness and sleep), auditory recall state (including Audio
Evoked Potential and complex frequency and sensitivity state),
muscle paralysis, movement and arousal state (including arousal and
body movement analysis), visual recall state, (including eye
opening and eye movement analysis state), anxiety and stress state
(including temperature, blood-pressure, oxygen saturation-SA02,
heart-rate variability, skin galvanometry resistance analysis).
[0010] Some prior art systems provide analysis of unconsciousness
state (Aspect Monitoring) and other systems analyse
electro-encephalograph signal activity (Physiometrix). Moreover
experiments have been conducted and apparatus devised to monitor
audio response (Audio Evoked Potential) together with a range of
neurological analysis. However, the working of the brain's
responses to anaesthetics and subsequent "shut-down" of the body's
sensory systems still remains a mystery.
[0011] The system of the present invention may measure not only the
state of consciousness of the sentient subject but also various
states of sensory systems. In particular emphasis may be applied to
measurement and monitoring of the sensory systems that are
potentially most vulnerable to incidence of recall during an
anaesthetic procedure. The HCM system of the present invention may
provide a primary measure or guide to a clinician for optimal
anaesthetic drug dosage by monitoring consciousness (such as
associated with EEG and BSAEP parameter measurement), while also
providing a "last line of defence" by monitoring the subject's
sensory systems including sight, hearing, movement, taste and
sound, for minimizing risk of recall associated with an
anaesthetic/medical procedure.
[0012] Allan Rechtschaffen and Anthony Kales, describe in "A Manual
of Standardized Terminology, Techniques and Scoring System for
Sleep Stages of Human Subjects", Brain Information Service/Brain
Research Institute, University of California Los Angeles, Calif.
90027, (R&K) (34) a method of scoring human sleep physiology.
Further descriptions of the behaviour of the brain's electrical
energy in terms of half-period amplitude analysis are disclosed by
Burton and Johns in AU Patent 632932, the disclosure of which is
incorporated herein by cross reference (45).
[0013] These earlier techniques were utilised for defining stages
of a human's sleep and were predominantly applied to a subject in
sleep, as recognised by conventional stages of sleep including
stage 1, stage 2, stage 3, stage 4 and REM sleep (as distinct from
hypnotic or in-depth of anaesthesia states). In particular the
first stage of sleep detection with R&K standardised sleep
staging techniques relies upon specific physiological sequences of
events, such as the subject's rolling of the eyes or slow moving
electro occulogram and changes in the electro-encephalogram
frequency spectrum. It is apparent that significant changes in
human physiology leading to the subject entering stage one of sleep
represent a dramatic change in a subject's state of consciousness.
This dramatic state of consciousness may be too late in detection
where the aim is, for example, to determine onset of a lack of
vigilance for a pilot of an aircraft or other critical job
function. In other circumstances a subject could enter a hypnotic
state where the driver of a car, for example, lapses into a type of
"trance" and the state of vigilance and the subject's environment
could become critical and highly dangerous. The phases of human
physiology periods (leading up to stage 1) of non-sleep are not
specifically described in R&K teachings.
[0014] Even hospitals such as Melbourne's Alfred Hospital, which
demonstrated one of the world's lowest reported incidences of
consciousness under general anaesthesia, still have an incidence
rate of 1 in 1000 patients (91). The chances of being aware and
experiencing pain are even lower but the consequences can be
devastating. Side effects of consciousness while under anaesthesia
can range from nightmares to recall of pain, stress, visual and
audio recall during a medical procedure.
[0015] The HCM system of the present invention may address these
limitations by providing specialised R&K and bicoherence
monitoring during application of general anaesthesia. The HCM
system may also provide methods of artefact rejection to allow more
precise monitoring and analysis of neurological and other
bicoherence and sleep variables from the subject.
[0016] Until now there has been no way to determine whether a
patient is asleep during a medical procedure, according to
University of Sydney-Australia's Web site, introductory paper on
anaesthesia (92).
[0017] In 1942 Canadian anaesthetists discovered that neuromuscular
blocking drugs could be developed. Sir Walter Raleigh had known in
1596 that the indigenous people of Bolivia had been using an
American plant derivative called curare to cause paralysis. Since
1942 these drugs have revolutionised surgery, particularly
abdominal and chest operations where muscle contraction had made
cutting and stitching almost impossible.
[0018] By deactivating the muscles, anaesthetists can make lighter
and safer anaesthetic drugs whilst still keeping the patient
unconscious. These muscle blocking drugs are now used in up to half
of all operations. However, the downside of the application of
these muscle drugs is that a patient is paralysed so that conscious
or unconscious movement is impossible. In circumstances where a
patient is awakening or is in a state of consciousness during a
medical procedure, the patient is unable to move and defend
him/herself or alert anyone of a potentially horrific experience
that the patient may be encountering.
[0019] Anaesthetists tend to overestimate the amount of anaesthetic
drug usage by up to 30%. This overestimation has consequences in
relation to a patient's health, recovery time and financial costs
to health services (94).
[0020] The HCM system of the present invention may address the
limitations of the prior art by providing an apparatus and method
for monitoring and analysing arousal and body movement of a patient
throughout anaesthesia. Furthermore the HCM system may provide
means to position electrodes and sensors for monitoring arousal and
body movements from any location on the patient's body. If, for
example, a chest operation requires extreme absence from movement
due to a critical incision procedure, electrodes or sensors may be
placed around sensitive chest muscles non-invasively or via
inter-operative methods.
[0021] The challenge to monitor for appropriate or optimum
anaesthesia is demonstrated with classic experiments such as that
of psychiatrist Bernard Levin in 1965, when 10 patients who were
read statements during anaesthesia, later had no recall of the
statements when questioned after surgery. However, of the same
patients under hypnosis four could quote the words verbatim and
another four could remember segments, but became agitated and upset
during questioning (95). An adequately anaesthetised patient should
not "feel", "smell", "see" or "taste" anything until they regain
consciousness (96).
[0022] In 1998 Dr David Adams of New York's Mount Sinai Medical
Centre replayed audio tapes of paired words (boy/girl,
bitter/sweet, ocean/water . . . ) to 25 unconscious heart surgery
patients. Approximately four days after the operation, the patients
listened to a list of single words. Some of these words had been
played while they were unconscious during their former operation.
The patients were asked to respond to each word with the first word
that came into their minds. The patients were found to be
significantly better at free-associating the word pairs they had
already encountered than those they had not. It was apparent that
the patients had heard the information and remembered it (97).
[0023] It appears that while a smaller number of patient's have
conscious memories of their experiences on the operating table, a
larger number have unconscious recollections. While positive
messages during surgery may have desired consequences others can
have undesirable results (98).
[0024] The HCM system of the present invention addresses the
limitations of the prior art by providing in one form an apparatus
and method for monitoring auditory sensory system while the patient
is undergoing anaesthesia. Furthermore the HCM system may provide a
comprehensive means of analysing both frequency response and
sensitivity response of one or both auditory sensory systems of the
patient during anaesthesia. This may provide monitoring and a means
of replay as evidence of the state of the subjects auditory system
throughout anaesthesia to reduce the risk of auditory recall.
[0025] The HCM system of the present invention may provide a method
and apparatus for monitoring and/or analysing a patient's eye
movement and eye opening to minimise or eliminate the risk of
visual recall after anaesthesia.
[0026] The HCM system may provide a method and apparatus for
monitoring a patient's stress and anxiety levels together with a
range of vital parameters to minimize the risk that the patient is
undergoing undue stress, anxiety and health conditions during
anaesthesia, and subsequently reducing or eliminating the incidence
of these states.
[0027] Previous studies present a relationship between human
treatment and changes in physiological states, as associated with
anxiety or stress. In particular such studies link respiration
rate, skin resistance and finger pulse volume to anxiety (53).
Other studies present relationships between salivary cortisol
levels and activities accompanying increased cardiovascular
activity (54).
[0028] Studies also present relationships between heart rate
variability (HRV), and people reporting anxiety and perceived
stress and between a subject's blood pressure and heart rate, and
activities associated with increased stress (55, 56, 57). Vagal
modulation of heart-rate period was found to be sensitive to a
person's emotional stress. Other studies present relationships
between a subject's. blood pressure and heart rate, and activities
associated with increased stress (58).
[0029] The HCM system of the present invention may measure, analyse
and display in near real-time graphical or numerical representation
of skin resistance, oxygen saturation, pulse-transit-time arousal,
blood pressure, heart rate, heart rate variability and temperature.
Furthermore, the HCM system may measure, monitor and analyse these
variables and present an index and/or other graphical and tabular
display means, to assist an anaesthetist or other medical personnel
in the assessment of a subject's depth of anaesthesia.
[0030] The HCM system of the present invention may record, monitor
and analyse in near real-time effects of cortisol salivary content
and changes thereof as an indicator of stress or anxiety, as may be
associated with increased heart rate as may occur with premature
awaking during anaesthesia.
[0031] The HCM system of the present invention may also measure,
analyse and display in near real-time graphical or numerical
representation of vagal modulation of heart-rate period.
Furthermore, the HCM system may measure, monitor and analyse this
variable which may be represented in terms of HRV frequency
de-composed into various frequency components; ie. LF-0.05-0.15Hz,
HF-0.15-0.5Hz, using spectral analysis; and may present an index
and/or other graphical and tabular display means, to assist an
anaesthetist or other medical personnel in assessing a subject's
depth of anaesthesia.
[0032] The HCM system of the present invention may record, monitor
and analyse in near real-time effects of blood pressure and heart
rate, and changes thereof as an indicator of stress or anxiety as
may be associated with changes in blood pressure and heart rate, as
may occur with premature awaking during anaesthesia.
[0033] The current field of sleep medicine is not precise in
scoring or quantifying human sleep physiology. The degree of
"inter-scorer" agreement in determining sleep classification of
human physiology is of the order of 80 to 90%. Monitoring and
analysing the state of a patient during anaesthesia treatment, and
subsequent accurate determination of the patient's state depth of
anaesthesia at any point in time is important to ensure efficacy of
the patient's anaesthetic treatment. To this end, accurately
defining the mechanisms, sequence or sensitivity of the sentient
mind "shutting down" or re-awakening as associated with vigilance
or response to administration of anaesthetics including the mind's
recall of such events is important for ensuring optimal
administration of anaesthetic agents. The science and knowledge
associated with sleep staging or scoring of human sleep is still
relatively primitive in terms of understanding the mechanisms of
sleep and consciousness. In particular it appears that the science
and knowledge associated with details and the sequence of "shutting
down" of consciousness and human sensory systems including sight,
hearing, smell, consciousness and muscle activity or arousal
necessary to avoid potential recall of a patient's experiences
associated with anaesthesia, is still relatively young and
inexperienced.
[0034] The HCM system of the present invention recognizes the prior
art limitations, and addresses them by providing a system which may
be configured to monitor and analyse combinations of a subject's
sensory systems during, inter alia, an anaesthesia procedure.
[0035] The HCM system may improve the probability of determining a
subject's consciousness by applying two or more independent methods
of analysis including bi-coherence based analysis and Brain Stem
Audio Evoked Potential or Steady State Evoked Potential based
analysis, and arbitrating, cross-checking and integrating results
of the two or more methods of analysis using a further independent
method of EEG analysis such as spectral based EEG analysis,
including optimised bi-spectral analysis and optimised R&K
sleep-wake analysis, to improve accuracy in determining the
consciousness status of the subject. In conjunction with
determining the consciousness status of the subject, the system may
analyse consciousness/hypnosis/vigilance with the aid monitoring
and analysis of brain waves together with various combinations of
sensory monitoring and analysis including auditory, muscle movement
and/or arousal including micro-arousal, eye opening & eye
movement.
[0036] Other parameters, which may optionally be included in
depth-of-anaesthesia monitoring and analysis determination, include
anxiety & stress levels, heart rate variability, galvanomic
skin resistance, temperature, respiration rate variability and
blood pressure and/or oxygen saturation.
[0037] The HCM system of the present invention may include an
apparatus for monitoring, analysing, recording and replaying a
subject's consciousness state in conjunction with critical
physiological sensory status of the subject. In this context
critical refers to sensory systems that are critical for minimising
the risk of recalling the experience or senses, associated with a
medical procedure while under anaesthesia.
[0038] The combinations of multiple sensory monitoring and analysis
may include a provision for a user to configure, select or operate
the system with one or more channels of input data from a subject
together with a range of system set-ups or montages, consistent
with the complexity of signal attachment to the subject, the
critical nature of the monitoring including the duration of an
operation and risk associated with administration of anaesthesia or
muscle paralysis medication to the subject, the skill and training
or experience of the user, the sensitivity of the subject to
anaesthetic or muscle paralysis medication, and variability of
different subjects in relation to susceptibility to premature
awakening or consciousness including recall of auditory or visual
stimuli, anxiety or arousal.
[0039] The HCM system of the present invention may provide unique
wireless connected electrode systems to reduce conventional wiring
and risk of entanglement
[0040] In some instances patient or subject specific data may
substantially affect monitoring or analysis methods associated with
the monitoring system. To the applicants knowledge, no one has
linked critical parameters such as weight, age and sex of a patient
to sensitivity and weighting of depth of anaesthesia monitoring.
The HCM system of present invention may include a capability to
adapt weighting or sensitivity of the analysis to the physiological
parameters being monitored. An example of this may include the
manner in which the weight or sex of a subject affects the optimal
band of concentration of an anaesthetic agent.
[0041] The HCM system of the present invention may utilise data
associated with the subject, such that its sensitivity or important
thresholds may be adjusted from one subject to the next. In this
context "utilisation" of data refers to compensation of critical
display threshold levels and sensitivity of various user displays.
In other-words the user displayed thresholds and associated
variations in sensitivity may be changed in accordance with
critical (for example, in depth of anaesthesia monitoring)
sensitivity to certain anaesthetic agents.
[0042] Surface electrode connections have been applied in the past
to monitoring applications associated with various physiological
parameters. However one problem with surface electrode connections
is that the quality of the connection to the subject can
deteriorate due to a number of conditions including patient sweat,
movement or drying out of the connecting electrolyte solution
between electrode and subject. The problem of electrode quality may
be more critical in applications such as those associated with
intensive care and operating theatre environments, than is the case
with depth of anaesthesia monitoring systems. To date, no one has
used connection of redundant electrodes, automatic validation of
electrode connection quality and validation by way of routine
impedance measurements and other signal validation techniques
(refer FIG. 18--MFD Block 7) including automatic substitution of
poor electrode connections with redundant or spare electrode
connections (refer FIG. 35--IAMES or FIG. 37--ISES). The system of
the present invention may include redundant electrodes together
with integrated electrode-sensors and wireless/rechargeable
electrode-sensors to minimize the number of electrodes and sensors
(as few as 3 sensor-electrodes in some embodiments) for depth of
anaesthesia monitoring and analysis (where the quantity,
reliability and simplicity of electrode-sensor attachments may be
highly critical) including monitoring and analysis of physiological
states such as mind-state, auditory sensory, visual sensory,
arousal sensory, anxiety sensory and vital states.
[0043] Eye movement sensors (such as piezo or PVD movement sensors)
and electrodes (such as EOG) have been used in the past for
detecting eye movement or eye-lid movement respectively. However
one problem associated with depth of anaesthesia monitoring is that
some patients awaken prematurely during a medical procedure and
opening of the eyes can lead to distressing views and subsequent
recall or nightmare occurrences. A further problem exists where the
patient may litigate in such instances, in which case an objective
and accurate recording of the patient's state and amount of eye
opening may be important. A system that allows the user to
calibrate such an eye-opening sensor would also be of value. The
HCM system of the present invention may provide such a sensor
(refer FIG. 34--EOS) for detecting in a calibrated manner a degree
of eye opening of a subject.
[0044] In accordance with general literature a predominant prior
art method for detecting anaesthesia is bi-coherence analysis of
EEG waveforms. Aspect Monitoring, which is a main supplier of
in-depth anaesthesia monitoring systems deploys this technique.
Aspect Monitoring has trademark applications for BIS and
Bi-spectral Index. Bi-spectral Index is based on the technique of
bi-coherence analysis.
[0045] Functioning of the brain in the transition of states from
consciousness to subconsciousness and from unconsciousness to
consciousness is recognised as a non-linear transition in relation
to the generation of electrical brain activity. Accordingly, the
bi-coherence method of monitoring EEG has been shown to be an
affective method for predicting the state of consciousness and the
subsequent state of depth of anaesthesia.
[0046] However, even with improved analysis of EEG data as
described above, another prior art limitation exists. This
limitation is related to the fact that while the combined frequency
and phase analysis of EEG data may provide an improved method for
monitoring a patient's state of consciousness, it has been found
(4) that Audio Evoked Potential (AEP) provides a more informative
measure of a subject's transition from unconsciousness to
consciousness, while EEG based bi-spectrum analysis provides a more
informative measure from consciousness to unconsciousness.
Accordingly, the HCM system of the present invention may
automatically detect whether the patient is transitioning from
consciousness to unconsciousness or visa versa and may apply or
weight bispectrum analysis (bicoherence/bispectrum/triple product)
or AEP analysis (such as Brain Stem Auditory Evoked Potential-BAEP)
respectively.
[0047] The HCM System addresses the limitations of the prior art by
applying R&K analysis as a type of "independent arbitration"
agent for determining which analysis type is optimal, based on the
context and sequence of analysis change or transitions. For
example, R&K detection of wake state, suggests a probable
transition from consciousness to unconsciousness, which in turn
suggests that the optimal or higher weighting of consciousness
state determination should be derived from BIC (bi-spectral
analysis incorporating bi-coherence) analysis. In contrast, R&K
detection of a sleep state (stage 1, 2, 3, 4, REM, for example)
suggests a probable transition from unconsciousness to
consciousness, which in turn suggests that optimal or higher
weighting of consciousness state determination should be derived
from AEP analysis.
[0048] Barr and colleagues describe in British Journal of
Anaesthesia June 2000 (1), a Coherence index (CHI) used to assess
depth of anaesthesia during fentanyl and midazolam anaesthesia for
coronary bypass surgery in which BIP decreased during anaesthesia,
but varied considerably during surgery. Schraag and colleagues
describe in Anesth Analg April 2000 (2), "that both BIP and AEPi
are reliable means for monitoring the level of unconsciousness
during propofol infusion. However, AEPi proved to offer more
discriminatory power in the individual patient. The implication is
that both the coherence index of the electroencephalogram and the
auditory evoked potentials index are good predictors of the level
of sedation and unconsciousness during propofol infusion. However,
the auditory evoked potentials index offers better discriminatory
power in describing the transition from the conscious to the
unconscious state in the individual patient."
[0049] Gajraj R J describes in British Journal of Anaesthesia May
1999 (3), "Comparison of bi-spectral EEG analysis and auditory
evoked potentials for monitoring depth of anaesthesia during
propofol anaesthesia." In this study, Gajraj & colleagues
compared the auditory evoked potential index (AEPindex) and
bi-spectral index (BIS) for monitoring depth of anaesthesia in
spontaneously breathing surgical patients." "The average awake
values of AEP-Index were significantly higher than all average
values during unconsciousness but this was not the case for BIS.
BIS increased gradually during emergence from anaesthesia and may
therefore be able to predict recovery of consciousness at the end
of anaesthesia. AEP-Index was more able to detect the transition
from unconsciousness to consciousness."
[0050] Gajraj R J, describes in Br J Anaesth Jan 1998 (30),
"Analysis of the EEG bispectrum, auditory evoked potentials and the
EEG power spectrum during repeated transitions from consciousness
to unconsciousness." In this study, Gajraj & colleagues
describe: "We have compared the auditory evoked potential (AEP)
index (a numerical index derived from the AEP), 95% spectral edge
frequency (SEF), median frequency (MF) and the bi-spectral index
(BIS) during alternating periods of consciousness and
unconsciousness produced by target-controlled infusions of
propofol." "Our findings suggest that of the four
electrophysiological variables, AEP index was best at
distinguishing the transition from unconsciousness to consciousness
and therefore may be able to predict the transition unconsciousness
to consciousness."
[0051] The HCM system of the present invention may address the
limitation of prior art methods of EEG sleep analysis, by applying
multiple independent methods of analysis and processing including
methods based on auditory evoked potential (AEP) index (a numerical
index derived from the AEP), 95% spectral edge frequency (SEF),
median frequency (MF) and coherence index (CHI) and R&K sleep
staging, together with a unique method of context analysis to
provide improved decision making with respect to which of the
multiple analysis processes are most suitable for optimal tracking
of each phase of the monitored stages of consciousness.
[0052] Witte H, describes in: Neurosci Lett Nov 1997 (5), "Analysis
of the interrelations between a low-frequency and a high-frequency
signal component in human neonatal EEG during quiet sleep." In this
study, Witte and colleagues describe: "It can be shown that
dominant rhythmic signal components of neonatal EEG burst patterns
(discontinuous EEG in quiet sleep) are characterised by a quadratic
phase coupling (coherence analysis). A so-called `initial wave`
(narrow band rhythm within a frequency range of 3-12 Hz) can be
demonstrated within the first part of the burst pattern. The
detection of this signal component and of the phase coupling is
more successful in the frontal region. By means of amplitude
demodulation of the `initial wave` and a subsequent coherence
analysis the phase coupling can be attributed to an amplitude
modulation, i.e. the envelope curve of the `initial wave` shows for
a distinct period of time the same qualitative course as the signal
trace of a `lower` frequency component (0.75-3 Hz)."
[0053] The HCM system of the present invention may address the
limitation of categorisation of neonatal neurological patterns by
including within the decisions of sleep-wake categorisation
information such as the age of a subject. In turn this information
may be used to weight analysis processes within the neurological
data. In the above case the age of the subject may prompt the
analysis processes to recognise unique markers such as `initial
wave` and to use recognition of these unique markers to provide
improved accuracy for categorising and detecting EEG patterns and
associated sleep staging of neonatal human subjects.
[0054] It is apparent that no one singular method for determining a
subject's state of vigilance is appropriate. R&K standardised
criteria for sleep staging can be important in recognizing a
subject's sleep state, coherence analysis can accurately describe a
patient's transition from wake to sleep, auditory response can
describe a subject's transition from sleep to wake, "initial wave"
can assist in detecting a subject's transition into hypnotic state,
and movement detection can describe a subjects state of rest or
relaxation. Furthermore, accuracy in. detecting and tracking a
subject's vigilance state can be improved by recognizing a
subject's age and in appropriate cases utilizing a subject's
personalised calibration and learning functions. While conventional
methods of vigilance analysis as described above, each have
specific benefits associated with various forms of sleep state,
hypnotic or vigilant state, the HCM system of the present invention
is designed to incorporate concurrent or selective combinations of
analysis in accordance with the users specific requirements.
[0055] The HCM system of the present invention recognises that the
linear amplitude and spectral analysis methods utilised by R&K
for sleep state analysis of a subject are indifferent to the
non-linear coherence analysis method more suited for entry and exit
from sleep or hypnotic states of the subject.
[0056] The HCM system of the present invention may utilise any
combination of spectral edge frequency analysis, Coherence
analysis, R&K standardised sleep staging criteria, auditory
response monitoring, initial wave monitoring, arousal analysis and
specialised input parameters derived from the calibration or
specific subject configuration and system configurations such as
the subject's sex and age data. A learning function and application
of neural networks may provide a means for the system to weight the
vigilance analysis format in a manner which is most appropriate for
a specific subjects vigilant state such as wake, sleep, and
transition from wake to sleep or sleep to wake.
[0057] The HCM system of the present invention may analyse a
subject's neurological data for purpose of coherence analysis and
R&K spectral analysis that may also include electro-occulogram
and electro-myogram physiological data. In particular the HCM
system may process transition stages of the subject's vigilance to
determine the most appropriate method of analysis and display of
the subject's hypnotic, sleep or vigilance state.
[0058] For example, the subject may be detected as being in wake
state by means of R&K analysis (preferred method for sleep/wake
detection), followed by on-set of hypnotic state (preferred method
of monitoring and analysing exit of hypnotic/sleep state) as
detected by the coherence index, enter sleep state by means of
R&K analysis stage 1 detection (preferred method for sleep/wake
detection), exit sleep state by means of firstly R&K wake state
detection, and then tracking depth of hypnotic state by means of
AEP index and auditory response (preferred method of monitoring and
analysing exit of hypnotic/sleep state).
[0059] The HCM system of the present invention may automatically
allocate an optimal processing means for determining a subjects
transition of consciousness state or sleep state by applying
simultaneously one or more processing techniques for determining
the most appropriate measure of the subjects state in accordance
with the transition of the subjects consciousness.
[0060] Furthermore the HCM system may include frequency analysis
(R&K analysis) (34) spectral analysis-SEF-MF, 1/2 period
analysis (46), (FFT) as a means to determine the transition and the
current state of a subject in order to determine which method of
consciousness analysis (BIP, AEP for example) is the most accurate
and subsequent indicator for identifying and tracking the subject's
vigilant state.
[0061] An ideal embodiment of the present invention may provide an
independent measure of both sleep state and brain activity in both
wake and sleep states. Furthermore the ideal embodiment may detect
when a non-valid sleep state was recognised (per International
standard R&K) so that brain activity or consciousness measures
should be utilised (BIP and AEP index). Furthermore the ideal
embodiment may include a simple non-ambiguous readout for users of
the system.
[0062] The HCM system of the present invention includes improved
analysis of depth of anaesthesia/consciousness/patient state with
optimised sleep-wake R&K analysis, optimised bi-spectral
analysis and optimised AEP analysis. Phase based analysis may be
combined with frequency band--amplitude analysis (spectral
analysis) to provide an improvement on phase only or frequency
based analysis (refer FIGS. 16, 17, 18, 34, 35, 37, 41, 42,
45).
[0063] To the applicants knowledge no one has used combinations of
Sleep-wake 1/2 period analysis or other forms of R&K or
modified R&K analysis, unique artefact processing (refer FIG.
18--MFD block 21) combined with specially weighted (in accordance
with empirical clinical data) and optimised bi-coherence, triple
product and bi-spectral index (refer FIG. 18--MFD Block 10), and
AEP analysis to improve the accuracy in determining the state of a
subject's consciousness.
[0064] The HCM system may, within a single monitoring device and
single electrode device, simultaneously provide a combination of
analysis types (and displays thereof including BIS analysis, AEP
index analysis, estimated R&K analysis, arousal analysis, eye
movement analysis and eye opening analysis.
[0065] A common problem with frequency-based analysis methods (be
it sleep-wake or bicoherence/bispectrum/triple product) in
analysing neurological data, is that the results of the
aforementioned types of analyses can change significantly with
seemingly stable physiological conditions. For example, substantial
increases in EEG activity in the 12 to 18 Hz (theta) frequency band
may be observed with administration of anaesthetic agents in the
low to medium concentrations, but high doses of the same agents may
lead to sudden reduced activity in the 12-18 Hz frequency band and
increased activity in the 0.5-3.5 Hz band, followed by burst
suppression at extremely high concentrations. Similarly,
bicoherence/bispectrum/triple product analysis relies upon
"relatively new principles" for determining the subject's state of
consciousness. In contrast, a well documented and validated method
for sleep staging such as presented by R&K, utilises analysis
techniques which, although being highly validated, are subject to
misleading frequency effects, as described above.
[0066] Apparatus based on the R&K method combines real-time
optimised (34, 45) R&K analysis with optimised bi-spectral
analysis to increase accuracy beyond conventional Bi-spectral
Index.TM. (52). Application of optimised spectral analysis may
provide a meaningful basis for determining consciousness state,
where R&K analysis has been formulated to provide sleep stage
(or depth of sleep) or wake state (referred to herein as sleep-wake
analysis) as opposed to varying degrees of subconsciousness, as a
subject approaches sleep or an unconscious state. R&K analysis
on the other hand may provide a well validated method for
determining a subject's depth of sleep. Furthermore modified
R&K analysis (refer FIG. 18--MFD Block 10) may improve artefact
rejection, making determination of the patient state more reliable
or less dependent on artefacts or noise, often evident during
monitoring of a patient. The artefacts may include sweat artefact,
amplifier blocking artefact, and mains noise signal intrusion, for
example. The HCM system of the present invention may weight
optimised R&K and optimised bi-spectral analysis in accordance
with the strengths and weaknesses of each of these processes to
provide overall improved accuracy and probability of determining
the subject's depth or state of anaesthesia.
[0067] The HCM system of the present invention may reduce the
effects of over reliance on frequency based changes of neurological
data from a patient, by utilising both frequency based EEG
(sleep-wake analysis) and phase based EEG analysis
(bicoherence/bispectrum/triple product).
[0068] The HCM system may provide automatic selection or weighting
of BIC and AEP analysis by means of R&K or similar frequency
based analysis as an arbitration agent in the decision path for
weighting analysis types.
[0069] The HCM System may be adapted to automatically detect
whether the patient is transitioning from consciousness to
unconsciousness or visa versa and to apply or weight bi-spectrum
analysis (bi-coherence/bi-spectrum/triple) or audio evoked
potential analysis (such as Brain Stem Auditory Evoked
Potential-BAEP) respectively.
[0070] The system of the present invention may monitor and detect
the state of the subjects consciousness. In particular real-time
and concurrent processes ideally suited to both non-linear and
linear analysis techniques may be applied. The system may include
bi-coherence (non-linear) analysis for depth of consciousness
monitoring in conjunction with Audio Evoked Potential (more linear
based) analysis for monitoring transition of a subject between
conscious and unconscious states. The system may provide improved
monitoring and analysis for application in detection, system alerts
and alarms associated with depth of anaesthesia, hypnotic state,
sedation depth, fatigue or vigilance of a subject, with as few as 3
surface electrodes. Combined or separate indexes or display methods
may provide accurate tracking of the subject's state of
consciousness and transition of conscious state. The system of the
present invention may assign patient states of sleep, wake, depth
of consciousness, depth of anaesthesia and vigilance in accordance
with analysis states derived from a combination of analysis types,
including in particular BIC and AEP based analysis. Prior art
systems (such as Aspect Monitoring) are limited as they are not as
precise or responsive as an AEP, arousal or EEG activity based
system for detecting transition and AEP responsiveness to
transition but not as gradual a measure (as BIC) for predicting
consciousness state.
[0071] However a limitation of this prior art method is that the
gradual change of the bicoherence measure may, by nature of the
type of the non-linear analysis prevent a clear or significant
emphasis of the subject's transition state. The transition state is
when the subject changes from consciousness to unconsciousness or
visa versa. This is a critical state when monitoring a subjects
depth of anaesthesia as a subject who is on the verge of waking up
may need urgent administration of anaesthesia in order to avoid a
serious incident such as the subject awakening during a surgical
operation.
[0072] For example, a time based curve or graph of the bi-coherence
processed signal can produce a relatively gradual and consistent
change when compared to other validated methods of consciousness
monitoring, such as Audio Evoked Potential (AEP) monitoring
techniques.
[0073] In the case of AEP monitoring, a subject wears a headphone
attachment and is presented with audio stimulus clicks, while at
the same time the auditory nerve is monitored. By monitoring the
amplitude of the response of the monitored (via non-invasive
surface electrodes attached to a subject's near ear) auditory nerve
signal and averaging this signal by summing a sequence of overlaid
traces of this auditory signal, it is possible to measure a degree
of the subject's consciousness. In this particular example
consciousness may be determined by a measure of the subjects
hearing responses. One advantage of this method is that it is
recognised to provide superior transition state information, where
the transition state is the actual determinant of whether the
subject is in a state of consciousness or unconsciousness. A
disadvantage of this method is that the state of transition based
on AEP analysis is relatively sudden due to the sudden response of
the auditory nerve during the transition of a subject's state from
unconsciouness to consciouness (30). However, an advantage is the
explicit or obvious nature of the data curve transition between the
two states.
[0074] Therefore the recognised methods of tracking consciousness
and unconsciousness of a subject each have different advantages and
disadvantages (33).
[0075] However the applicant is not aware of any prior art system
or method that is able to provide an ideal solution. Such solution
would need to have non-linear gradual measurement and prediction
abilities associated with bi-coherence analysis, together with
immediate indication associated with the transition state as
depicted by AEP analysis.
[0076] The HCM system of the present invention may automatically
detect whether the patient is transitioning from consciousness to
unconsciousness or visa versa and apply or weight bi-spectrum
analysis (bi-coherence/bi-spectrum/triple product) or audio evoked
potential analysis (such as Brain Stem Auditory Evoked
Potential-BSAEP) respectively. The HCM system may address prior art
imitations by applying R&K analysis as a type of "independent
arbitration" agent for determining which analysis type is optimal,
based on the context and sequence of analysis change or
transitions. For example, R&K detection of wake state, suggests
a probable transition from consciousness to unconsciousness, which
in turn suggests that optimal or higher weighting of consciousness
state determination should be derived from the BIC (bi-spectral
analysis incorporating bi-coherence) analysis. In contrast, R&K
detection of a sleep state (stage 1, 2, 3, 4, REM, for example)
suggests a probable transition from unconsciousness to
consciousness, which in turn suggests that optimal or higher
weighting of consciousness state determination should be derived
from AEP analysis.
[0077] An ideal system for monitoring depth of anaesthesia or
vigilance or depth of sedation or hypnotic state should be able to
present a single or simple index, display reference or monitoring
technique which clearly depicts both a prediction of depth of
anaesthesia and a current state and transition of states of a
subject. In particular the ideal system should be able to utilise a
method of combining AEP and bi-coherence analysis techniques into a
single monitoring measure. The HCM system of the present invention
may achieve this scenario by weighting the AEP transition state and
the bi-coherence analysis value so that a single combined reference
is obtained.
[0078] The HCM system may weight the transition state heavily when
a subject transitions his/her mind-state from unconsciousness to
consciousness (AEP, arousal and eye opening wake analysis is
heavily weighed) so that an anaesthetist can have a guide in
predicting the depth of anaesthesia utilising the bi-coherence
factor, but if the subject changes or approaches a change in state
as indicated via AEP analysis, the anaesthetist may be given
immediate and obvious display indication and can avert a
potentially serious incident such as the subject awakening during a
surgical operation.
[0079] The HCM system of the present invention may assign patient
states of sleep, wake, depth of consciousness, depth of anaesthesia
and vigilance in accordance with analysis states derived from a
combination of analysis types including R&K analysis (34), AEP
(30), spectral analysis-SEF-MF (4), Bi-coherence (BIC) analysis
(33), initial wave analysis (5), auditory response (4,30), arousal
analysis (35) and body movement analysis (34,26), 95% spectral edge
analysis (36) and anaesthetic phase and spectral energy variance
measurement in association with a subject's state of consciousness
(30), Pulse Transient Time (PTT) based arousal detection (31), PTT
measure and PTT based blood-pressure reference measure, PTT based
heart rate and blood pressure with simple non-invasive oximeter
(31, 32), PAT analysis for sympathetic arousal detection (104-108),
EEG spike-Kcomplex-wave-activity-event categorisation (47) and
bio-blanket-heart-temperature-PTT
blood-pressure-respiration-breathing sound (49).
[0080] The HCM system of the present invention may include
automatic consciousness state context determination (refer FIGS.
16, 17, 18, 34, 35, 37, 41, 42, 45). The HCM system may provide
trend or sequence analysis with improved qualification of a
subject's depth or level of various mind states by incorporating
preliminary analysis or preview analysis context determination. In
particular the HCM system may apply concurrently and in real-time
EEG frequency (26,30,36,47), EEG phase (33) and EEG amplitude
analysis (30).
[0081] For the purpose of "context" determination, the HCM system
may apply concurrently and in real-time a combination of methods of
analysis including R&K analysis (34, 45,46), AEP (30), spectral
analysis-SEF-MF (4,30), Bi-coherence (BIC) analysis (33), initial
wave analysis (5), Auditory Evoked Response (30), arousal analysis
(35) and body movement analysis (34), 95% spectral edge analysis
(36) and anaesthetic phase and spectral energy variance measurement
in association with a subject's state of consciousness. (36), Pulse
Transient Time (PTT) based arousal detection (31, 32), PTT measure
and PTT based blood-pressure reference measure, PTT based heart
rate and blood pressure with simple non-invasive oximeter, PAT
analysis for sympathetic arousal detection (104-108), EEG
spike-K-complex-wave-activity-event categorisation (47) and
bio-blanket-heart-temperature-PTT
blood-pressure-respiration-breathing sound (49), to determine the
context of a subject's state of mind. In particular the "context"
may include that a subject is in a state of wake or consciousness
and whether or not the subject is entering or approaching a state
of unconsciousness or sleep, for example. Where a subject is in a
state of unconsciousness or sleep, an ideal depth and state of
consciousness monitoring system may emphasise or highly weight a
change of state where (for example), this change of state could
represent a subject awakening during an operation procedure, for
example.
[0082] There are a number of limitations associated with current
standards for staging human sleep (R&K standardised sleep
criteria) (34). Some of these limitations arise, for example, from
the fact that it has been found that infants exhibit higher
amplitude of EEG frequency bands such as deltawave than do more
elderly patients. It has also been found that in infants
conventional methods of scoring sleep are not an accurate
indication of the child's sleep physiology.
[0083] The HCM system of the present invention may address the
limitation of prior art methods of EEG sleep analysis with an
ability to concurrently analyze and process a selection of, or
combination of methods of sleep/hypnosis/arousal/vital signs
monitoring including: [0084] R&K analysis (34), [0085] EEG
pattern recognition [0086] AEP (30), [0087] spectral
analysis-SEF-MF (4), [0088] Bi-coherence (BIC) analysis (33),
[0089] initial wave analysis (5), [0090] auditory response (30),
[0091] arousal analysis (35), [0092] body movement analysis (34),
[0093] 95% spectral edge analysis (36), [0094] anaesthetic phase
and spectral energy variance measurement in association with a
subject's state of consciousness. (30), [0095] Pulse Transient Time
(PTT) based arousal detection (31), [0096] PTT measure and PTT
based blood-pressure reference measure (31,32), [0097] PTT based
heart rate and blood pressure with simple non-invasive
oximeter(31,32) [0098] PAT analysis for sympathetic arousal
detection (104-108), [0099] EEG spike-K-complex-wave-activity-event
categorization (47), [0100] bio-blanket-heart-temperature-PTT
blood-pressure-respiration-breathing sound (49).
[0101] In addition to the above analysis techniques the HCM system
of the present invention may access any combination of one or more
of the above analysis techniques concurrently and determine the:
[0102] context, [0103] physiological vigilance or sleep or wake or
consciousness transition; and [0104] predict "probability of
transition" of a subject's vigilance state.
[0105] The "context and predictive" analysis includes providing a
validation of the subject's sleep or hypnotic state by referencing
a combination of the above analysis techniques in terms of the
current vigilance phase and a trend or sequence vigilance phase.
If, for example the HCM system determines that the subjects current
vigilance state does not qualify for classification under
conventional rules as depicted by R&K analysis (34), but was
detected by way of BIC coherence analysis (33) as progressing to a
deeper stage of hypnotic state or a deeper state of unconsciousness
(as with deeper state of in-depth anaesthesia state), then the HCM
system may make a more accurate decision based on predictions from
the context of the R&K and BIC analysis past and current trend
data. In this particular case the prediction may be that the
subject is entering a phase of deeper unconsciousness or hypnotic
state (by way of no R&K state and BIC analysis), and
accordingly has a higher probability of predicting that the subject
is more likely to be approaching a transition of unconsciousness to
consciousness. This aforementioned prediction may alert the HCM
system that the most accurate method of analysis in the phase from
unconsciousness to consciousness is likely to be Auditory Evoked
Potential response. The HCM system present may "self-adapt" the
analysis method in accordance to the sequence of the subject's
vigilance state transitions in order to provide improved accuracy
for monitoring a subjects vigilance or to more appropriately
classify same into a sleep, hypnotic or consciousness state of the
subject being monitored. "Self adaptation" in this context refers
to the capability of the HCM system to initially weight vigilance
analysis towards BIC as the preferred method for analysing a
subject's transition from wake to unconsciousness, and then
subsequently weight Audio Evoked Potential response as the
preferred method of analysing a patient's transition from
unconsciousness to consciousness.
[0106] The HCM system of the present invention may determine the
most probable transition states by evaluating the trend or sequence
of data output from more than one analysis type. Example of
vigilance transition states include: [0107] consciousness to
unconsciousness [0108] unconsciousness to consciousness [0109]
sleep to wake [0110] wake to sleep [0111] deepening of
unconsciousness (or hypnotic) state [0112] exiting of
unconsciousness (or hypnotic) state
[0113] Examples of analysis types that may be automatically
allocated based on a subject's current vigilance transition state
and current state include: TABLE-US-00001 AUTOMATIC PREFERRED
TRANSITION STATES ANALYSIS TYPE Consciousness to unconsciousness
BIP Unconsciousness to consciousness AEP Sleep to wake 1)R&K,
2)BIC Wake to sleep 1)R&K, 2)BIC Deepening of unconsciousness
BIC (or hypnotic) state Exiting of unconsciousness AEP (or
hypnotic) state AUTOMATIC PREFERRED ANALYSIS TYPE FOR CURRENT STATE
STATE CLARIFICATION Consciousness or wake 1)BIC, 2)R&K
Unconsciousness AEP Sleep state R&K Wake state or consciousness
1)BIC, 2)R&K
[0114] The HCM system of the present invention may take into
account the instantaneous and trend analysis outputs from one or
more analysis type to determine a subjects most probable transition
state and may then select the most qualified or accurate analysis
type as the primary decision weighting of a subject's state of
consciousness (hypnotic state), wake, sleep or vigilance.
[0115] The HCM system of the present invention may include a
learning capability and pattern recognition to enable different
combinations of analysis type and different combinations of trends
of analysis, to determine the most appropriate analysis type for
determining the patient's vigilance.
[0116] Furthermore the HCM system of the present invention may
recognise combinations of analysis output to improve accuracy of
detecting a subject's vigilant state or transition of the subject's
vigilant state.
[0117] The HCM system of the present invention may apply both FFT
and 1/2 period amplitude analysis in consecutive 1 second intervals
(can be set to greater values, particularly where lower frequency
response characteristics are being utilized). The FFT analysis
(i.e. 95% spectral edge (36)) has an advantage of providing power
distribution of the EEG signal frequencies but the disadvantage of
not presenting mixed frequency EEG signals for assessment under
scoring criteria such as per R&K analysis EEG (34,45,46). An
example of where 1/2 period amplitude analysis may provide an
advantage over frequency analysis is where a 30 second epoch
contains a high amplitude Delta wave and the Delta wave does not
constitute greater than 50% of the 30 second epoch, but due to
excessively high amplitude of the Delta wave, would appear to
dominate the 30 second epoch. In this case use of FFT would suggest
that this epoch is, say stage 4 (greater than 50% of the epoch time
with high amplitude Delta wave in accordance with R&K analysis
(34,45,46). However if for example, the epoch consisted of greater
than 50% of the epoch in Alpha EEG waves as would be more evident
(than FFT analysis) with 1/2 period amplitude analysis then this
epoch should in accordance with R&K human sleep scoring
criteria, not be scored as stage 4 of sleep. In other words the 1/2
period amplitude analysis more correctly represents the method of
scoring sleep in accordance with R&K than FFT in such instances
and utilization of FFT and 1/2 period analysis (45) may provide
improved accuracy for determining a subject's consciousness state
(33) and sleep state (34) in the HCM system.
[0118] The HCM system of the present invention may include
automatic Input Signal Validation, Optimisation & Compensation
(ASVC) including automatic substitution of poor quality electrode
connections (refer FIGS. 17, 18, 34, 35, 37, 41, 42, 45). This
function may enable the system to automatically validate input
signals (physiological variables in the present application but
applicable to other industries involving monitoring or analysis of
signals in general) of a subject's monitored variables. Validation
may be by way of automatic impedance measurement, frequency
response, mains interference, signal to noise and signal distortion
and other measured signal characteristics as part of the analysis
algorithm for monitoring, detecting or predicting a subject's state
of consciousness, sedation or vigilance.
[0119] Furthermore the HCM system of the present invention may
automatically determine signal conditions during operation of the
system, and invoke subsequent signal processing to compensate or
reduce artefacts caused by unwanted signal distortion or
interference such as noise. Furthermore, in order to allow the
system to display to the user on-going signal validation and signal
quality issues, signal status and subsequent compensation (or
signal correction), signal trends or progressive deterioration of
signal quality and existing signal quality issues, both current and
trend signal status may be displayed in real-time and stored, with
both modified and compensated signal data.
[0120] The HCM system of the present invention may provide trace
ability (or an audit trail) of all signal modifications so that the
system user can validate any automatic signal compensation
decisions both in real-time and in later study review. A further
feature of the HCM system is a capability to provide the user
qualification, at all times, relating to detected signal
deterioration and subsequent signal compensation. A further
capability may allow the user of the HCM system to automatically or
manually (upon the user's discretion or agreement with
qualification of signal deterioration and proposed compensation)
invoke signal compensation for optimising or improving signal
quality. Due to time synchronised (with recorded signals) trace
ability (audit trail) of signal validation and subsequent signal
compensation, modified signals may be revoked (unmodified) to
original signal format where required.
[0121] Furthermore, signal validation may provide a means to allow
the system to optimise signal quality for improved application of
various signal-processing algorithms.
[0122] The system of the present invention may adapt or re-assign
redundant or spare electrode channels in substitution of identified
poor quality signal channels. In particular the system may
automatically alert a user of the quality of all attached
electrodes and sensors. Where any poor signal quality is detected
the system may advise the user of recommendations or hints to
quickly identify and resolve signal quality problems.
[0123] Surface electrode connections have been applied in the past
for various physiological parameters and monitoring applications.
However one problem associated with surface electrode connections
is that the quality of the connection to the patient can
deteriorate as a result of a number of conditions including patient
sweat, movement, or the drying out of the connecting electrolyte
solution between electrode and patient. In particular the problems
of electrode quality may be particularly critical in applications
such as those associated with intensive care and operating theatre
environments, as is the case with depth of anaesthesia monitoring
systems.
[0124] To the applicant's knowledge, no one has used connection of
redundant electrodes, automatic electrode connection quality and
validation by way of routine impedance measurements and other
signal validation techniques (refer FIG. 18--MFD Block 7) and
automatic substitution of poor electrode connections with the
redundant (spare) electrode connections (refer FIG. 35 (IAMES) or
37 ISES)). The system of the present invention may utilise
redundant electrode systems together with integrated
electrode-sensors and wireless/rechargeable electrode-sensors to
minimize the quantity of electrodes and sensors (as few as 3
sensor-electrodes) for depth of anaesthesia monitoring (where the
quantity, reliability and simplicity of electrode-sensor
attachments is very critical) and analysis including mind-state,
auditory sensory, visual sensory, arousal sensory, anxiety sensory
and vital signs physiological states.
[0125] The system of the present invention may include automatic
Analysis Validation, Compensation, Optimisation, Adaptation of
Format and Analysis and Probability Assignment (AAVCOAFA)(refer
FIGS. 16, 17, 18, 34, 35, 37, 41, 42, 45). The system may adapt
algorithms for determining the subject's state of consciousness
(and vulnerability to anaesthesia procedure recall) while
simultaneously in substantively real-time allowing the system to
determine and display to the user the signal analysis methods being
deployed (such as R&K derived from optimised BIC-outer malar
bone surface electrodes-as opposed to C3 EEG signal) signals
status, trends or progressive deterioration of signals (such as
detailed in (AVCOADSP), or analysis quality caused by, for example,
input signal connection deterioration, or connection of improved
signal inputs. In other words the system may determine the most
appropriate (accurate and reliable) analysis method (algorithm
type) by way of validating input signal quality and automatically
or manually activate a changed analysis method or format that is
the most suitable for the validated signal channels. The analysis
methods may be determined according to presence, status and quality
of the patient signals being monitored.
[0126] A further capability of automatic analysis validation is
that the system may adapt or re-assign variants or substitute
analysis formats where the existing analysis format requires
change, such as when an input channel connection(s)
deteriorate.
[0127] The system may automatically alert the user of the quality
and probability of the applied analysis processes. The system may
also advise the user of recommendations or hints to quickly
identify and resolve analysis validation deterioration or
issues.
[0128] The HCM system of the present invention may display to the
user on-going analysis validation status, progressive deterioration
of analysis quality and subsequent analysis variation or analysis
compensation due to signal deterioration, for example.
[0129] Furthermore once analysis types have been activated,
weighting techniques may be applied in order to determine the
probability associated with different analysis methods. For
example, BIC (outer malbar bone, surface electrode placement)
derived R&K EEG analysis does not produce as high a probability
as C3 (surface electrode) derived R&K EEG analysis.
[0130] The HCM system of the present invention may provide an
automatic analysis format linked to signal validation, such as in
the case of sleep and wake analysis where the analysis parameters
applied may depend on the validated signals. If, for example, only
EEG outer malbar electrodes are validated, then frequency optimised
EEG outer malbar signals may be utilized for analysis, as opposed
to more complex analysis signal combinations including EMG and EOG
signals.
[0131] The system of the present invention may include Patient
Data-Linked Analysis (PDA)(refer FIGS. 16, 17, 18, 34, 35, 37, 41,
42, 45). The system may adapt the analysis algorithms used for
determining a subject's state of consciousness (and vulnerability
to anaesthesia procedure recall) in accordance with critical data
such as the subject's body mass index (weight, height), sex and
age. Such Patient Data-Linked (PDA) analysis may enable patient
specific data such as the subject's body mass index, age, sex,
medical history and other relevant information to be utilised in
analysis algorithms for monitoring, detecting or predicting the
state of consciousness, sedation or vigilance of the subject.
[0132] Patient specific data is entered in prior art patient
monitoring systems. However in some instances patient specific data
can substantially affect monitoring or analysis methods associated
with the monitoring system. To the applicants knowledge, no one has
linked critical parameters such as weight, age and sex of a patient
to the sensitivity and weighting of depth of anaesthesia
monitoring. The HCM system of the present invention may change the
weighting or sensitivity of analysis of the physiological
parameters being monitored. An example of this is where the weight
or sex of a subject affects (in accordance with empirical clinical
data), the optimal band of operation of a given concentration of an
anaesthetic agent, due to the effects that sex and weight have on
these parameters.
[0133] The HCM system of the present invention may utilise certain
patient data, which may vary the sensitivity or important
thresholds associated with variations between one patient and the
next. The "utilisation" of this data refers to compensation, for
example, of critical display threshold levels and sensitivity of
various user displays. These user display thresholds and associated
sensitivity variations may change in accordance with critical
applications, for example when using the system to monitor
sensitivity of depth of anaesthesia to certain anaesthetic
agents.
[0134] Table A below shows one example of Patient Specific Data
Entry Parameters: TABLE-US-00002 TABLE A PATIENT SPECIFIC INPUT
DATA Age: Weight: Height: SEX: BMI: History file: Calibration file:
Calibration-file anesthetic type:
[0135] The system of the present invention may include
Calibration-Linked Analysis (refer FIGS. 16, 17, 18, 34, 35, 37,
41, 42, 45). The system may adapt the analysis algorithms used for
determining a subject's state of consciousness (and vulnerability
to anaesthesia procedure recall) in accordance with the subject's
critical calibration data, such as how the subject responds to
various preliminary or pre-test studies. This "calibration data"
may include thresholds and parameters derived from a specific
patient's preliminary study, in order to determine the
characteristics of the subject's physiological parameters for more
accurate consideration of variations between different
subjects.
[0136] This capability may be important where, for example, a
subject undergoes a critical operation. To minimise the risk
associated with anaesthesia administration, a preliminary
calibration study can be conduced. This study may include a
capability to store tables of values or specific drug
administration versus analysis state (BIC/AEP/R&K/95% spectral
edge or other) coefficients or specific analysis values associated
with varying degrees of drug administration. The system of the
present invention may include localized or general motor and
sensory nerve and muscle response and arousal analysis (refer FIGS.
16, 17, 18, 34, 35, 37, 41-45). The system may adapt algorithms
used for determining a subject's state of consciousness (and
vulnerability to anaesthesia procedure recall) in accordance with
monitoring and detection of the subject's arousals (typically
detected from shifts in frequency and amplitude in monitored
signals) or muscle responses (for example during an operation or
medical procedure). The system may apply this data as an alert or
detection means for the subject's transition state or physiological
and mind-state response to a medical procedure and a means of
consciousness state detection. In other words the muscle changes or
arousal events may be indicative of muscle responses of the
subject, which in turn may indicate the subjects localised
anaesthesia effectiveness or the subject's state of consciousness
and local muscle response.
[0137] In particular localised monitoring and detection of muscle
movement or activity may provide a means to localise the arousal
and muscle monitoring, relative to the responsive or sensitive
areas associated with a medical procedure, and consequently may
provide immediate feedback where an anaesthetised area of a subject
indicates muscle or nerve responses consistent with inadequate
anaesthetic drug administration. The system may include accurate
monitoring and recording of the effect of local anaesthetic by
detecting the subject's motor and sensory responses in conjunction
or time-linked with an incision or other medical procedure. The
latter feature may provide a means of monitoring and analysing both
the state of a subject's mind and the response from selected ear
related (cochlear) procedures where a subject's state of
anaesthesia and performance or response of the auditory system can
be monitored and analysed throughout an operation procedure.
Industry standard techniques (for example, Canadian Task Force)(35)
for detecting arousals may be utilized in the system of the present
invention.
[0138] The HCM system of the present invention may include an
electrical stimulus pulse (evoked potential) and test of the nerve
or muscle response of a subject while undergoing an operation or
medical procedure. The electrical stimulus pulse may be applied at
a selected excitation location on the subject's body, and the
response (nerve or muscle) can function in a dual-monitoring mode
whereby determining the subjects state of consciousness or
vigilance (as in depth of anaesthesia monitoring) and determining
the response and performance of selected muscles or nerves of the
subject may be performed simultaneously. This "dual-monitoring"
function may be particularly useful when a subject is undergoing a
delicate and precise medical operation or procedure.
[0139] The system of the present invention may include an
Integrated Anaesthesia Monitoring Electrode System (IAMES)(refer
FIGS. 16, 17, 18, 34, 35, 37, 38, 41-45). IAMES may be wired or
wireless. IAMES may include a simple, low cost and minimally
intrusive electrode system, which may be disposable or reusable
with a connector interface to a replaceable EAS. Alternatively EAS
may be integrated with a Wireless Electronic Module (WEM). A
version which is completely disposable would typically be lower in
cost and may not in some lower cost options, include a wireless
interface. The lower cost completely disposable versions may
include a low cost data logging system with low cost display means.
Low cost display means for completely disposable versions, may
include once of display output for index measure, for example, or
digital interface or data card for information retrieval.
[0140] The IAMES system may be divided into two components
including an electrode attachment system (EAS) and the WEM section.
Completely disposable systems may include integrated WEM and EAS
sections for further cost reduction.
[0141] The EAS system is a remote patient attached electrode
transceiver monitoring station, which contains a means of inputting
patient data to the WEM module (refer below). EAS includes a code
identification system allowing system configuration to be set up in
accordance with the specific electrode type (ie.EEG, EOG, EMG, EEG
or other).
[0142] EAS includes conductive surfaces which may be easily
attached to a patient's skin surface for electrical pick-up of
physiological variables associated with a subject including a
combination of left and right, outer malbar placed electrodes for
detecting typical bicoherence EEG variables, left and right outer
carantheous eye electrodes for detecting EOG electrical signal
associated with eye movements, chin sub-mental EMG electrodes for
detecting the subject's chin muscle activity and state of
restfulness, A1 or A2 electrodes (dependent on the format of the
electrode system) for providing an electro-physiological reference
signal and eye lid position sensors for detecting eye opening
activity and percentage of eye opening.
[0143] A combination (hybrid) system may provide R&K and/or
bicoherence signal attachment in one wireless hybrid device, thus
opening up avenues for large scale home monitoring of sleep
disorders, more critical applications such as medical procedures
and operations or vigilance monitoring of workers or air/land/sea
transport personnel. Options may include sub-mental EMG and/or
auditory sound output devices (ear-piece, headphones or speaker)
and/or auditory signal pick-up devices (surface
electro-physiological electrode).
[0144] A Wireless Electronic Module (WEM) system may include a
small, low power and lightweight module designed to snap connect to
an EAS module. The WEM module may provide the following functions:
[0145] interface for one or more channels of patient data emanating
from the EAS module; [0146] electrode and sensor amplification (DSP
and/or analogue methods); [0147] filtering (DSP and/or analogue
methods); [0148] calibration testing including generation of one or
more (different wave-shapes, frequency and amplitude) local test
waveforms; [0149] impedance measurement; [0150] signal quality
measurement; [0151] input DC offset measurement; [0152] wireless
data transceiving and DSP or micro-controller data processing
capabilities; and [0153] reference code identification detailing
electrode type (eg.EEG,EOG, EMG,EEG or other).
[0154] The WEM transceiver module may transmit physiological
signals and various test data such as the impedance value across
the electrode signals, quality measure of signal or data such as a
reference code detailing electrode type (ie. EEG, EOG, EMG, EEG or
other). The EAS transceiver module may also receive various control
and test commands such as requests to measure impedance, generation
of test or calibration waveforms, a measure of signal quality and
other data.
[0155] The WEM system may be powered via any combination of
rechargeable or single use batteries, self powered electrodes with
a capability of charging via RF or EMF induction during use or as a
charging procedure.
[0156] A WEM module may be directly attached to an EAS module, or
it may be attached to an EAS module via an intermediate wireless
link or wired attachment. Alternatively, patient worn or patient
attached device(s) such as headband, head-cap or hat, wrist-worn or
other devices may incorporate an EAS and/or WEM module.
[0157] The WEM module may be self powered with Radio Frequency or
Electromagnetic frequency providing a power supplement. The latter
system may utilise radio or electromagnetic signals as a means for
recharging the power source in the WEM module.
[0158] The IMES device may be wirelessly linked to close proximity
or distant monitoring systems equipped with a wireless data
interface capability to IMES. Close proximity monitoring devices
may include the headrest of a car seat where a self-powered IMES
system (typically EMF power recharge system) may be wirelessly
linked to a transceiver device contained within the driver's seat
headrest or other convenient or appropriate location(s). The WEM
may be wirelessly linked to remote computer devices wherein WEM
data may be stored, displayed and/or analysed. The remote WEM
device may also provide a controlled interface to the WEM module
for calibration and impedance testing. WEM may also be wirelessly
linked to mobile phones or wireless modems or a network interface
including an Internet connection.
[0159] The IMES device, when incorporated with local (incorporated
in WEM module) or remote (wireless or wire-linked) BIC analysis may
provide analysis for detecting vehicle or machine operator
vigilance with a wireless electrode option.
[0160] The system of the present invention may include an Eye
Opening Sensor (EOS)(refer FIGS. 34, 35, 37, 42). The EOS system
may provide an improved device for sensing and measuring Eye
Opening. Eye movement sensors (such as piezo or PVD movement
sensors) and electrodes (such as EOG) have been used in the past
for detecting eye movement or eye-lid movement respectively.
However one problem associated with depth of anaesthesia monitoring
is the fact that some patients awaken prematurely during a medical
procedure and opening of the eyes can lead to distressing views and
later recall or nightmare occurrences. A further problem is the
patient may litigate in such instances. An objective and accurate
recording of the patient's state and amount of eye opening is
therefore desirable. A system that allows the user to calibrate
such an eye-opening sensor may also be of value. The HCM system of
the present invention may include such a sensor (refer FIG. 34) for
detecting in a calibrated manner the degree of opening of a
subject's eye.
[0161] The EOS system includes an eyelid position monitor and an
EOG sensor. The EOS system may include conventional surface
electrode electro-physiological signal sensing in conjunction with
a capability to detect the position of a subject's eyelid at any
point in time. Combined sensing of eye movement and eye opening may
provide a simple, minimally invasive sensing system ideally suited
to a subject's eye region to provide eye blink details and rate,
eye open percentage and eye movement information. The sensor can be
wire or wireless connected to a monitoring system. The EOS system
may also be provided in an embodiment, whereby EOG sensing is
achieved within the same sensor attachment system. Special design
variations may provide simple self-applied sensors, which can be
safely and easily applied in a manner similar to attaching a
band-aid.
[0162] A further option exists using self-applied electrodes where
the electrodes may include a low cost disposable component and a
more expensive reusable component. For example the connector and
electronics circuit may be reusable, while the applied section of
the sensor may be disposable.
[0163] The HCM system may also provide an improved capability for
calibrating eye position at commencement or at any stage during a
subject's use of the EOS sensor. Calibration may be applied by
determining (measuring, storing and determining calibration data
versus corresponding eye opening status) the output of the EOS
sensor under varying conditions, eg. by asking a subject to close
their eyes, and storing the responding EOS signal. The EOS system
may incorporate the format of the WEM and the EAS.
[0164] The system of the present invention may include an
Integrated Sleep Electrode system (ISES)(refer FIGS. 35, 37, 42).
The ISES device may provide a self-applied electrode system for
sleep/wake analysis of a subject. The electrode system may attach
outer malbar or any two EEG electrodes to a subject's forehead as
part of a monolithic self-adhesive and self-applied electrode
system. An analysis method may be applied to the ISES device's
signal output to provide sleep/wake or bicoherence analysis. A
flexible insert may facilitate elasticity to accommodate different
patient sizes. Electrodes may include varieties including an
attachable version and disposable dot surface re-usable electrodes
(such as from 3M) and reusable/disposable electrodes. The ISES
system may include the format of the Wireless Electrode Module
(WEM) and the Electrode Attachment System (EAS).
[0165] The system of the present invention may include a user
programmable device with real-time display of integrated analysis
index and incorporating at least two weighted and combined modes of
analysis (refer FIGS. 16, 17, 18, 34, 35, 37, 41-45). The apparatus
may include a capability to output one or more analysis algorithms
including a combination of simultaneous, real-time analysis of
R&K analysis (34), AEP (30), spectral analysis-SEF-MF (4),
Bi-coherence (BIC) analysis (33), initial wave analysis (5),
auditory response (30), arousal analysis (35) and body movement
analysis (34), 95% spectral edge analysis (36) and anaesthetic
phase and spectral energy variance measurement in association with
the subject's state of consciousness (30), Pulse Transient Time
(PTT) based arousal detection (31), PTT measure and PTT based
blood-pressure reference measure, Pulse oximetry SA02, PTT based
heart rate and blood pressure with simple non-invasive oximeter,
PAT analysis for sympathetic arousal detection(104-108), EEG
spike-K-complex-wave-activity-event categorisation (47) and
bio-blanket heart-temperature-PTT
blood-pressure-respiration-breathing sound (49). The specific types
of analyses can be determined by way of signal validation, user's
selection of analysis requirement (such as depth of anaesthesia,
vigilance, sleep-wake and other) and electrodes input to the
system.
[0166] The HCM system of the present invention addresses the
limitation of the prior earlier art by presenting a simple mode of
display to the user which represents a simple measure of the
subject's current state of consciousness or hypnotic state. This
particular aspect of the HCM system may communicate to the end-user
a simple measure of the subject's consciousness despite a vast
range of complex analysis measurements, as detailed herein. In
addition to providing a simple overall measurement and display
method the HCM system may also provide a means of storing and
displaying all recorded raw data and outputs of each analysis
method for complete system verification and trace ability relating
to any display of conscious or vigilant state of a subject. The raw
data and analysis data may be stored and available for later
review, reporting and printing, as is required from time to time to
verify system performance and operation.
[0167] The HCM system of the present invention may improve accuracy
of prediction of the state of consciousness, or a subject's
vigilance by comparing actual EEG amplitude variations with
predicted EEG amplitude variations where predicted EEG behaviour
may include predictions of EEG amplitude variation during
anaesthesia drug administration against depth of anaesthesia
prediction (29)(refer FIGS. 16, 17, 18, 34, 35, 37, 41-45). The HCM
system may recognize EEG amplitude variations associated with
physiological phenomena such as EEG bursts as opposed to EEG
amplitude variations associated with movement or other forms of
artefact, such as excessive beta frequencies.
[0168] The HCM system of the present invention may apply amplitude
analysis to the EEG signals. By analysing monitored EEG amplitudes
from a subject and comparing this signal to a pre-known amplitude
trend or signal behaviour, it may enhance accuracy of prediction of
anaesthetic drug administration. The "pre-known" behaviour trend
may provide a means to predict the state of the depth of
anaesthesia by referencing a known or predicted sequence or trend
of EEG amplitude variation (behaviour) with the subject's actual
EEG amplitude or patterns of EEG amplitude variation whilst under
sedation or anaesthesia, for example.
[0169] The HCM system of the present invention, may reference
amplitude trend predictions and signal modelling such as described
by Moira L. Steyne-Ross and D. A. Steyne-Ross, of Department of
Anaesthetics, Waikato Hospital, Hamilton, New Zealand (29) in a
paper entitled "Theoretical electroencephalogram stationary
spectrum for white-noise-driven cortex: Evidence for a general
anaesthetic-induced phase transition". This paper describes an
increase in EEG spectral power in the vicinity of the critical
point of transition into comatose-unconsciousness. In similar
context to the above-mentioned weighting methods, the HCM system of
the present invention may weight the analysis output from amplitude
analysis of the EEG signal. The EEG analysis may include comparison
of actual monitored EEG signal and trends and predicted signal or
trend associated with the subject's transition from consciousness
to consciousness and visa versa.
[0170] The output of amplitude processing may be input to a
weighting table for final consideration in the monitoring,
detection and alerts associated with depth of anaesthesia, hypnotic
state, sedation depth, fatigue or vigilance of the subject.
[0171] The system of the present invention may include a
Programmable Electrode Interface System (PEIS) (refer FIGS. 16, 17,
18, 34, 35, 37). The PEIS apparatus may provide a means for
intuitive user guidance and operation. The user of the HCM system
can select a desired function (for example depth of anaesthesia
monitoring, vigilance monitoring, sedation monitoring) and the
system may illuminate by way of LED, LCD or other display system,
the required electrode connections and recommended position on
subject such as the location of various surface electrodes.
[0172] The PEIS apparatus may provide a prompting capability,
indicating to the user, which electrodes require attention, eg.
surface electrode may require re-attachment due to excessive
impedance.
[0173] In a preferred embodiment the PEIS apparatus may include a
touch screen programmable electrode attachment guidance system.
[0174] The system of the present invention may include a Biological
Blanket Sensor (BBS). The BBS may enable a wired or wireless
interface providing a range of measurements for assistance in
determining arousal movements, body movement, breathing sounds,
heart sounds, respiration, heart rate, Pulse Transient Time, Blood
pressure and temperature.
[0175] The BBS apparatus may be sensitised with sensor elements
whereby the sensor reacts to subject movement causing a change in
impedance of a resistive element, piezo or PVD element (49).
[0176] The system of the present invention may include a Biological
Sensor Oximeter with Integrated and Wireless-Linked ECG and Pulse
Transient Time (PTT) Monitoring and Analysis (refer FIG. 33). The
latter apparatus may monitor a subject's. blood pressure variation,
micro-arousal detection for detecting sleep or consciousness
fragmentation (particularly useful but not limited to depth of
anaesthesia consciousness monitoring and analysis), oximetry,
temperature, ECG waveform and analysis, heart rate variability and
cardio-balistogram respiratory monitoring output and respiratory
event detection.
[0177] Prior art non-invasive blood pressure devices utilise
techniques such as finger attachment probes. These finger
attachment systems apply pressure to a patient's finger and can
become uncomfortable after a period of attachment.to the patient.
Other non-invasive blood pressure measurements have been presented
including qualitative methods. One such qualitative method is a
qualitative derivation of Pulse Transit Time (PTT) by means of a
calculation utilising the electrocardiograph (ECG) waveform and the
pulse waveform of the subject. The ECG waveform is typically
derived from a chest located ECG surface electrode attachment. The
pulse waveform may be derived from the plethysmograph pulse
waveform of a pulse oximeter probe attachment at a location such as
a patient's finger. The calculation for deriving qualitative blood
pressure value is based on the relationship, which exists between
PTT and Blood pressure. Plethysmograph data may also be used to
establish sympathetic arousal conditions (104), which may be
related to stress or anxiety and which are physiological signs of
premature awakening.
[0178] However a number of patient monitoring applications require
continuous and close to real-time blood pressure measures of the
subject to detect a significant physiological blood-pressure change
or related event.
[0179] Furthermore existing minimum invasive methods for
blood-pressure measurement typically involve a cuff device placed
around the subject's upper arm. The cuff device may be inflated and
deflated to measure blood pressure. This method of measuring blood
pressure may be applied to a patient on a periodic basis. Other
methods for minimally invasive blood-pressure measurement include
wristband cuffs with similar inflatable and deflateable bands.
Whilst these wristband cuff blood pressure systems, are potentially
less invasive than upper arm cuff type systems, it is apparent that
measurement reliability of wrist systems is more vulnerable to
sensitivity of positioning and difficulty in obtaining a consistent
and reliable measurement. Both cuff type systems are not used
routinely for real-time and continuous blood pressure monitoring
applications (such as depth of anaesthesia, respiratory disorder
and sleep disorder monitoring) due to obvious discomfort and
complexity and inconvenience of such measurement techniques.
[0180] An object of real-time blood pressure, measurement technique
is to apply a 3-point wireless localised network (raw data and
analysis results may be transmitted to a remote computer, if
required) to provide a minimally non-invasive, minimally obtrusive
blood pressure measurement apparatus. One aspect of this apparatus
is that the clinically accepted standard for upper-arm cuff
inflation/deflation measurement may provide calibration and
absolute blood pressure measurement, while the oximeter finger (for
example--another location for oximeter pulse) SAO2 measurement
together with plethysmograph (provides pulse waveform for
measurement of pulse transit time) and ECG surface electrode may
provide a reference heart signal to be used in conjunction with the
oximeter finger pulse signal to produce a calculation in real-time
for pulse transit time. Pulse transit time is recognised as a means
of qualitative blood pressure measurement (31, 32).
[0181] In contrast to the prior art the HCM system of the present
invention may apply periodic cuff attached (arm, wrist or other
patient attachment location) blood-pressure measurement system, in
conjunction with an oximeter pulse waveform and ECG waveform (for
PTT calculation). The method of utilising the PTT (by way of
oximeter pulse wave and ECG waveform) together with periodic cuff
based blood-pressure measurement may provide a means to derive a
quantitative blood-pressure measurement from the cuff value, and a
qualitative blood-pressure measurement from the PTT calculated
signal. In other words the baseline quantitative blood-pressure
value may be derived from the cuff blood-pressure value, while a
continuous and qualitative blood pressure value may be derived from
the PTT value. The benefit of this type of system is accuracy and a
continuous blood pressure monitoring capability, while maintaining
patient comfort by implementing cuff inflation and deflation only
at periodic time intervals.
[0182] Furthermore the system may simplify user operation with
application of wireless interconnection of the pulse oximeter, ECG
electrode and blood pressure cuff. Wireless interconnection may
allow calculation of continuous blood pressure at a remote wireless
or wire-linked site (such as a patient monitoring device), at the
ECG electrode attachment site, at the oximeter finger probe site or
the blood pressure cuff site.
[0183] The system of the present invention may include an
audiovisual recall and speech sensory validation system (refer FIG.
43). The latter may provide audiovisual recall or replay and time
synchronisation with depth of anaesthesia analysis data and raw
data. Audiovisual recall may provide a means to correlate
physiological or analysis data associated with depth of anaesthesia
monitoring.
[0184] The audiovisual system may be configured in several options.
One option may include a capability to store more than one audio
channel synchronised with the subject's measured physiological
data. The stored and monitored (and optionally analysed or
condensed) audio channel may include sound or speech associated
with the subject, to accommodate monitoring and detection
associated with the subject's speech sensory system. This function
may be deployed as a last line of defence where a partly
anaesthetised patient is attempting to notify the medical team, in
case of partial or complete consciousness associated with potential
undesired recall of a medical procedure.
[0185] The system user may select physiological events or
combinations of physiological events as event markers. The event
markers may form the basis of time markers pointing to significant
or relevant events. The event markers may be associated with
specific audio and/or video related events. The "audio" and/or
"video" related events include physiologically related or
environmental related events. Physiologically related events
include combinations of or single patient data changes which may be
related to the patient's significant (i.e. the level exceeds a
certain threshold condition) or relevant (to the users or the
system's programmed detection threshold) changes in consciousness
state. The system's time synchronisation between video, audio,
physiological data and analysis data may provide a means for audio
and video to be recalled and analysed in conjunction with the
subject's state of consciousness as indicated by the status of eye
opening, AEP, arousal, bi-coherence analysis, and other analysed
states.
[0186] One example of an audio and/or video "relevant" event may be
where a threshold level (user set or system default set) is
exceeded indicating a potential for onset of consciousness.
Detection that the audio evoked threshold is exceeded may be linked
to detection of "environmental" and/or system generated audio
threshold being exceeded, where "environmental" audio denotes audio
recorded in the operating theatre from music, speech or other
sources of noise. "System- generated audio" refers to the audio
stimulus click, which may be applied to the patient's ear or ears
during an operating procedure.
[0187] The system may detect incidence of exceeding a preset
environmental audio threshold in conjunction with a physiological
event such as audio evoked potential amplitude exceeding a certain
threshold condition (typically a certain averaged amplitude
measured with a certain time delay from a trigger point). This
"capability" may provide an efficient (subject to system or user
threshold programming) method for validating or evidence of a
likely incidence of audio recall associated with a procedure
involving application of anaesthesia. The system may present in a
condensed graphic or numeric form an association between the
subject's hearing status (as detected from an audio sensory nerve
monitoring signal) associated with incidence of environmental sound
(as detected from the recording of audio within the operating
theatre environment). This "association" may allow the system user
to efficiently investigate correlation of a patient's hearing
response and actual alleged audio recall. For legal purposes this
facility may detect whether a subject's audio sensory nerve was
indeed active (as opposed to inactive during an unconscious state)
and whether the alleged audio recall of specific music or words was
indeed probable. The "environmental audio" recording may be
achieved by means of a patient attached microphone, such as a
microphone attached to an outward side of the patient's earpiece or
headset speaker system (as applied for generating an audio click
for Auditory Evoked Potential). This type of method has an
advantage of providing a dual-purpose sensor/speaker system, while
also providing specific and directional audio pick-up associated
with the patient's hearing system.
[0188] Similarly, where a subject claims visual recall during an
operation, an appropriately placed theatre camera that is time
synchronised with physiological data and analysis may record the
alleged vision. Vision recall may be compared to Systems detection
(manual, automatic or computer assisted) of a subject's eye opening
for example. For legal purposes this facility may detect whether a
subject's alleged vision recall was indeed possible as opposed to
impossible, such as when the patient's eyes are both closed.
[0189] In other words, the system may allow audio validation- i.e.
if the subject's AEP data indicated that the alleged audio recall
was coincident with inactive auditory evoked potential, for
example, this may support data for medical defence against audio
recall operation claims. Similarly video of the patient could
disclose whether or not visual recall claims coincided with patient
eye open status.
[0190] In another example, bi-coherence analysis of importance such
as where specific threshold conditions are exceeded may be
validated by reviewing it in a time-synchronised format with video
and audio recorded during a subject's operation. This validation
may allow quantitative data to substantiate claims such as audio or
visual recall associated with an operation procedure.
[0191] The system may optionally include means for recording the
subject's taste (some patient's claim taste recall, such as taste
which may be associated with anaesthetic gas delivery), utilising
taste biochips and again providing an association between
consciousness state physiological and analysis parameters with
taste and/or physiological taste sensors. In some cases the medical
specialist may deem monitoring of taste sensor sensory system
status as a requirement.
[0192] A further option may be to use two simultaneously acquired
images, where each image is acquired at a different wavelength of
light. Reflections from the patient's face may then be identical
except for reflections of the eyes. By subtracting these two
images, a third image consisting of the subject's eyes may be
created. Finally, the image of the patient's eyes may be measured
to provide a non-invasive and non-obtrusive measure of eyes opening
and blink rates of the subject (99). This data utilising PERCLOS
methods may be used as a relatively reliable measure in the HCM
system, to ensure that a subjects eye openings particularly when
the subject should be anaesthetised and unconscious (100).
[0193] The eye opening value may provide a simple measure of the
percentage of eye opening of a patient and may clearly indicate
risk of visual recall or potential awakening of the subject, during
an anaesthesia procedure.
[0194] The system of the present invention may include a patient
alarm alert system for limb-controlled alarm (refer FIG. 44). The
HCM system may include a wire or wireless remote device connected
to or accessible by any patient limb or other location near or
attached to the patient's body. This remote device may contain at
least a means for detecting or alarming system users or healthcare
workers that the patient is in distress or requires attention. This
remote device may allow the subject or patient a form of "final
line of defence" to premature wakening or consciousness onset. If,
for example, a patient is undergoing a local anaesthetic procedure,
which does not allow verbal notification of pain experience by the
patient, the HCM system's remote device may allow the patient to
signal experience of pain level to the system operator(s). Various
forms of pain or consciousness level notification may be possible.
One such form is where the patient is provided a simple squeeze
control such as a rubber ball, and where the pressure resulting
from squeezing, signals pain experience and the level of such pain
experience. Other forms (subject to type of medical procedure and
anaesthetic application, for example) may include, for example, an
attachment for detecting foot movement, eye movement or other
appropriate means of pain or consciousness signalling.
[0195] The system of the present invention may include a Wireless
Electrode system with automatic quality verification, redundant
electrode substitution, and minimal sensor-electrode attachment
system (refer FIGS. 34, 35, 37). The HCM system may provide a
minimally invasive method and apparatus for monitoring vigilance of
people, using 2 or 3 (or as many electrodes as required in a given
application) forehead located surface electrodes, wireless
monitoring connection, active electrode for dry electrode minimal
electrode preparation, automatic electrode impedance measurement
for detecting potential electrode quality problems, redundant
electrode substitution for substituting back-up electrodes for poor
quality electrode connections and dynamic signal quality for
detecting current or pending electrode problems (refer
drawings).
[0196] Paths of data storage may include localised condensed data
or secondary (analysis results) data storage, or remote raw data
(minimal or no compression or condensing data techniques).
[0197] A specialised identification connection system may allow
automatic identification and channel characterisation (system
configuration to suit particular channel type) for matching between
electrode application types. "Electrode application" types may
include ECG, EMG, muscle activity, piezo movement detection,
bi-coherence EEG, and EOG. "Characterisation" may include sample
rates, data analysis types, data condensing formats, data storage
rates, data storage format, optimal power management, and
electrical and processing optimisation. Data format may include
on-board electrode data storage, versus remote patient worn data
storage or remote linked data storage.
[0198] Characterisation may also include aliasing filter
requirements, high-pass/low-pass and notch signal biological signal
filtering requirements, and calibration requirements (for DC
stability and gain requirements). A further embodiment of the
system includes a low-cost disposable wireless electrode device
such as may be required for monitoring sounds provided by a PVD
sensor integrated with a "band-aid" style of attachment to a
subject's face for monitoring the subject's snoring or other
breathing sounds. The apparatus may include a means to incorporate
the microphone sensor, amplification, filtering, storage and CPU
either as a throwaway disposable system or with the more expensive
electronics being part of a re-usable part of the apparatus. In the
case where the apparatus is provided as a totally disposable unit,
a means for sensing monitoring and recording and analysing the data
may be provided for in addition to a means for displaying the
analysed data results. The means for displaying the analysed data
results may include a low cost means such as a permanent graphical
chemical reaction associated with markers, coding or other visual
based system. Alternatively a digital wired connection, optical
connection or magnetic means of connection may be used to download
the stored data results. A device may provide a means for recording
airflow or bruxism events (via vibration or cheek muscle electrical
activity) either as a disposable or re-useable device or a
combination of a disposable electrode section and a re-useable
electronics and wireless section. The apparatus may include means
to simultaneously sense (with electrodes or transducer), monitor,
record and analyse bio-physiological data within a "local"
(electrode device module) memory device, while transmitting data to
a "remote" (wrist watch or remote computer device) device. The
"local" device may provide limited storage due to size, cost and
power restraints, while the "remote" device may provide a means of
transmitting and storing less condensed and more comprehensive
data, as may be required for clinical or research diagnosis or
validation of diagnosis.
[0199] The system may offer any combination of very low power
"self-powered" system operation. Very low power operation is
possible by utilising transmitted EMF or radio energy, from a
remote source, as a means to supply or supplement a source of power
for the system. The apparatus may be provided in a form, which is
reusable or disposable.
[0200] In a form in which the electrode is disposable the device
may be configured in a form, which can process and condense data
such that the data can be stored in the device and may display
various forms of index or output summary. This display may be in a
form where the index can represent an amount of time detected in a
sleep or wake state (could be any stage or combinations of state
including REM, non-REM, stage 1, stage 2, stage 3, stage 4, wake)
by means of say a pair of bi-coherence electrodes. Accordingly, the
apparatus may record data representing the subject's sleep
efficiency or related to the subjects sleep efficiency to inform a
patient or healthcare worker whether the subject is receiving
appropriate rest or quality of rest or quality of sleep. Similarly,
a combination of a wristwatch based activity monitoring (86) and
wireless electrode (such as for bi-coherence electrode monitoring)
to wristwatch storage and processing may provide a low cost,
minimally invasive and potentially highly accurate means of sleep,
drowsiness or depth of anaesthesia monitoring.
[0201] The system may utilise special re-usable or disposable
electrodes in conjunction with a miniature active electrode and
transceiver device.
[0202] A combination of an active electrode and transceiver may
provide a unique combination within the apparatus. The active
electrode interface may provide a localised amplifier (close to or
directly connected to the subject's surface electrode contact) to
reduce stringent electrode application requirements of conventional
electrode systems. The fact that the electrode amplifier is
relatively close to the electrode (and thus the electrical signal
derived from the said subject's skin surface) avoids noise pickup
normally associated with conventional electrodes. Conventional
electrodes have wires of up to 1 metre length, with the electrode
amplifier being located some distance from the end of this wire. By
buffering or amplifying the patient electrode directly at the point
of patient skin surface attachment, a higher impedance may be used.
Conventional (passive) electrode systems, on the other hand, have
longer wires connected between the electrode and the electrode
amplifier creates a pick-up zone for external noise. Accordingly, a
lower electrode impedance is required to minimise this otherwise
large external noise and artefact interference. An example of the
benefits of an active electrode system in this application is that
the driver of a vehicle may apply an electrode to his/her forehead
with little or no preparation, similar to the application of a
band-aid.
[0203] An electrode application with little or no preparation may
result in an impedance of say 40 K to 100 K (thousand) ohms, as
opposed to a well prepared (thorough skin cleansing and some-times
light abrasion) or "conventional" electrode application impedance
which would be typically 5 K-10 K ohms impedance. A 40 K to 100 K
ohms impedance would result in such large interference (in
conventional passive electrode systems) that the desired monitored
physiological signal could be rendered useless or unusable, while
in an active electrode system a 40 K to 100 K ohms impedance could
produce acceptable results.
[0204] A wireless protocol may include a capability to continually
scan for new devices and allocated bandwidth requirements to
accommodate incremental or decremental demands upon the number of
system channels and aggregated data bandwidth requirements.
Similarly, where system bandwidth has reached or approaches system
limitations, the user may be alerted. In this way the physiological
electrode wireless system is a virtual plug and play system, with
simple and user friendly application. The wireless protocol may
also manage functions such as relaying both physiological data and
commands for continuous electrode impedance checking, calibration
validation or associated adjustments, signal quality checking and
associated adjustments, electrode substitution and other
functions.
[0205] The system may include Spread-spectrum based wireless,
active electrode system suitable for in-vehicle EEG monitoring and
depth of anaesthesia monitoring amongst other applications (refer
FIGS. 33, 34, 37, 42, 45).
[0206] Utilisation of an active electrode system for vigilance
in-vehicle monitoring, in conjunction with a wireless and battery
or self-powered electrode system, may provide a self-applied driver
vigilance electrode monitoring system. In one embodiment, for
example, a driver could apply a self-adhesive active wireless
linked forehead electrode system.
[0207] The electrode system may include a re-usable section that
contains the more expensive active electronics and wireless
circuitry, and a disposable section that contains the surface
electrodes and some form of interconnection to the re-usable
section. Such apparatus may be suitable for a minimally invasive
in-vehicle vigilance system where (for example) a wireless
electrode monitoring device such as a forehead attached wireless
electrode system may be optionally input to an existing driver
drowsiness measurement system. In this manner a driver may choose
to increase reliability of driver drowsiness detection by using
minimally invasive EEG bi-coherence signal monitoring and analysis.
This type of function may supplement or replace other on-board
vehicle real-world driver drowsiness monitoring technologies
associated with measurement of driver-movement and activity sensors
(Burton, 1999) and eye opening measurement.
[0208] The system of the present invention may include
physiological data time delay and analysis time lag compensation.
The latter may be applicable where anaesthesia drug administration
can be monitored in real time against actual display changes and
the apparatus is able to predict changes instantly for the user to
avoid over or under drug administration associated with natural
hysteresis or delay factors such as delay between the instant of
drug administration and the human body's physiological parameters
(as monitored by the apparatus) responding to the drug
administration.
[0209] The latter is applicable to parameters such as oxygen
saturation where the physiological data reading is typically
delayed by between 15 and 20 seconds due to the nature of the
monitoring method and the body's time delay in blood-oxygen colour
change.
[0210] The system of the present invention may include a
Biofeedback loop providing automatic anaesthesia drug rate or
concentration of delivery (refer FIG. 48).
[0211] The HCM system may interface to various types of drug
delivery systems to provide varying degrees and types of
biofeedback control affecting the drug administration process. The
drug delivery systems may include but are not limited to gas
ventilation or ventilation or gas delivery systems, drug perfusion
systems, amongst other drug delivery systems. "Varying degrees" of
drug delivery may include a capability to limit drug delivery or
provide degrees of drug delivery or drug delivery mixture in
accordance with predetermined monitoring or analysis parameters
associated with the HCM System.
[0212] The system of the present invention may include a Wireless
Patient Electrode Identification and Characterisation function
(IDCF). This function may provide a means for the system to
automatically identify the electrode type selected by the user.
Automatic identification may be by way of wireless module scanning
or electrically interfacing to some resident data (contained on the
disposable or reusable sensors or electrodes, which are attached to
the subject) or optical or magnetic code sequence, where a unique
code is associated with each unique electrode type. Various
electrode types may be identified for groups of physiological
variables, which share the same characteristics and processing
requirements. If a user selects an ECG electrode for example, the
IDSC may alert the system of optimal gain, signal range filter
conditioning, aliasing filter values and types, sample-rate and
data bandwidth requirements for the wireless module interface,
processing, acquisition, analysis, display and other functional
requirements related to the electrode channel type.
[0213] This automatic identification system may greatly simplify
system application and minimise potential user errors. An example
of an application and embodiment of this system may be where a
nurse applies a series of clearly labelled electrodes and the rest
of the system operation is automatically configured as the patient
is wired up in accordance with the selected electrode types.
[0214] The IDCF is also useful if the application for the wireless
electrode system is a wireless EEG electrode system that is
self-applied to a vehicle driver's forehead for simple "fool-proof"
EEG signal monitoring. The combined application of the wireless
module with automatic signal characterisation in accordance with
detection of the electrode type, active electrode signal handling
and later analysis techniques incorporating BIC (including
bi-coherence and bi-spectral analysis) may provide a unique
wireless, artefact reduced and precise method for in-vehicle or
other application of cognitive performance or vigilance/fatigue
monitoring.
[0215] This function may be particularly useful for depth of
anaesthesia or a vehicle based vigilance system where the user
needs to have a system that is as minimal and "fool-proof" as
possible.
[0216] The IDCF system may also help to ensure that only known
re-usable or disposable electrodes are used with the system and
that optimal characterisation and system set-ups are automatically
applied in accordance with the selected electrode types.
SUMMARY OF THE INVENTION
[0217] The HCM system of the present invention may provide improved
accuracy in monitoring, analysis, detection, prediction, system
alerts and alarms associated with, inter alia, depth of
anaesthesia, depth of consciousness, hypnotic state, sedation
depth, fatigue or vigilance of a subject, with as few as 3 surface
electrodes. The HCM system may incorporate real-time phase,
amplitude and frequency analysis of a subject's
electro-encephalogram. The HCM system may provide a means to weight
the output of various types of analysis and produce a combined
analysis or display for precise indication or alert to various
users of the system.
[0218] In particular the HMC system may monitor, store and display
two or more sets of physiological data parameters or analyse one or
more combinations or calculations associated with the data to
display, store, condense and summarise data for a range of
applications associated with monitoring human consciousness. The
HMC system may analyse two or more of the physiological data to
produce condensed data summaries, or indexed data (such as arousals
per hour and other indexes) or tabular and graphic displays and
reports associated with monitoring human consciousness. The HMC
system may correlate two or more sets of the physiological data or
analysis results to produce tertiary analysis results associated
with monitoring human consciousness.
[0219] The HMC system may be applied to monitoring depth of
anaesthesia for optimal administration of anaesthetic drugs, to
sedation in tracking the subject's level of sedation for nurses or
other medical professionals, to monitoring fatigue and hypnotic
state for drivers, to monitoring vigilance for transport and
machine workers and to controlling delivery systems for
administering therapeutic treatment such as drugs, gas or the like
to the subject.
[0220] The HMC system may weight the outputs of one or more
analysis algorithms including combination of simultaneous,
real-time analysis of R&K analysis (34, 45, 46), AEP (30),
spectral analysis-SEF-MF (30), Bi-coherence (BIC) analysis (33),
initial wave analysis (5), auditory response (30), arousal analysis
(35) and body movement analysis (34), 95% spectral edge analysis
(36) and anaesthetic phase and spectral energy variance measurement
in association with a subject's state of consciousness (29), Pulse
Transient Time (PTT) based arousal detection (31), PTT measure and
PTT based blood-pressure reference measure, Pulse oximetry SAO2,
PTT based heart rate and blood pressure with simple non-invasive
oximeter (31,32), PAT analysis for sympathetic arousal detection
(104-108), EEG spike-K-complex-wave-activity-event categorisation
(47) and bio-blanket for monitoring of heart, temperature,
respiration (49), breathing sound and PTT blood-pressure. Inclusion
of sympathetic arousal may provide a unique measure of stress or
mental anxiety, despite the state of a patient's state of paralysis
or "apparent unconsciousness".
[0221] According to one aspect of the present invention there is
provided a method of monitoring consciousness of a sentient subject
and automatically detecting whether the subject is in a transition
from a conscious state to a less conscious state or vice versa, by
reducing effects of frequency based changes in neurological data
from the subject, said method including: [0222] (i) obtaining an
EEG signal from the subject; [0223] (ii) performing a frequency
based analysis on the EEG signal to obtain a frequency based
signal; [0224] (iii) performing a phase based analysis on the EEG
signal to obtain a phase based signal; [0225] (iv) detecting by
comparing the frequency based signal and the phase based signal
whether the subject is in transition from said conscious state to
said less conscious state or vice versa; and [0226] (v) providing a
warning signal when said subject is in said transition to said
conscious state.
[0227] According to a further aspect of the present invention there
is provided a method of processing a non-stationary signal
including segments having increasing and decreasing amplitude
representing physiological characteristics of a sentient subject,
said segments including portions in which said signal changes from
increasing to decreasing amplitude or vice versa, said method
including: [0228] (i) detecting each segment by determining time
instants when a time derivative of said signal is substantially
equal to zero; [0229] (ii) performing syntactic analysis for each
segment including assigning height, width and error parameters;
[0230] (iii) identifying noise segments present in said signal by
comparing said width parameter to a preset threshold and said error
parameter to said height parameter; [0231] (iv) removing said noise
segments by replacing each identified noise segment with a
substantially straight line; [0232] (v) sorting the remaining
segments into a plurality of wavebands based on their width
parameters; and [0233] (vi) classifying said signal as belonging to
one of predefined sleep states based on relative frequency of
occurrence of said segments in said wavebands.
[0234] According to a still further aspect of the present invention
there is provided a method of monitoring physiological
characteristics of a sentient subject including: [0235] applying a
first surface electrode to said subject to provide a first
electrical signal to a remote monitoring apparatus; [0236] applying
a second surface electrode to said subject to provide a second
electrical signal to said remote monitoring apparatus; [0237]
monitoring quality of said first electrical signal and in the event
of a degradation in said quality of first signal; [0238]
automatically substituting said second electrical signal for said
first electrical signal and in the event of a degradation in said
quality of said second electrical signal and in said quality of
said first electrical signal, providing a warning signal.
[0239] According to a still further aspect of the present invention
there is provided an apparatus for processing a non-stationary
signal including segments having increasing and decreasing
amplitude representing physiological characteristics of a sentient
subject, said segments including portions in which said signal
changes from increasing to decreasing amplitude or vice versa, said
apparatus including: [0240] (i) means for detecting each segment by
determining time instants when a time derivative of said signal is
substantially equal to zero; [0241] (ii) means for dividing said
signal into said segments including data over three consecutive
time instants when said time derivative is equal to zero; [0242]
(iii) means for assigning to each segment, height, width and error
parameters; [0243] (iv) means for identifying noise segments in
said signal including means for comparing for each segment said
width parameter to a preset threshold and said error parameter to
said height parameter; [0244] (v) means for removing said noise
segments including means for substituting a straight line
connecting first and third time instants when the time derivative
of said signal is substantially equal to zero and reassigning
segments and their parameters after the substitution; [0245] (vi)
means for sorting the remaining segments into a plurality of wave
bands based on the value of their width parameter, each wave band
being defined by upper and lower frequencies corresponding to lower
and upper values for the width parameter respectively; and [0246]
(vii) means for classifying a time interval of the signal data as
belonging to one of predefined sleep states based on relative
frequency of occurrence of said segments in said wave bands.
[0247] The so-called "segments" are the principal building blocks
of EEG and EOG analysis. A "segment" includes a sequence of
consecutively increasing and decreasing or consecutively decreasing
and increasing intervals of the signal under analysis.
[0248] All "segments" may be initially detected by applying
syntactic analysis to the signal, ie. detecting all local maxima
and minima. As a data structure a "segment" is represented by its
orientation (ie. "upward" or "downward"), width, height and error.
In the context of visual signal interpretation, the last three
parameters have a clear meaning. Width relates to the dominant
frequency of the signal under analysis at this particular time
interval, height relates to the magnitude of the signal variation
and error, which is a measure of signal variation from a straight
line connecting the start and end of the "segment", relates to the
magnitude of noise in the signal if the "segment" is a part of the
noise rather than a part of the actual signal that is under
analysis.
[0249] After all "segments" are originally detected using a
syntactic algorithm, those segments which are likely to be noise
rather than the signal under analysis must be removed, and new
signal "segments" must be reconstructed. To achieve this an
iterative procedure of identifying noise "segments" and generating
new signal "segments" may be employed. A "segment" may be
classified as noise if its width is relatively small (which in the
case of EEG signal indicates alpha, sigma and beta bands--where
high frequency noise is typically prominent) and the error is
relatively small (which ensures that genuine visible EEG high
frequency components are retained). Various rules may be generated
to represent meaningful conditions of small width and small error.
This "segment" may then be approximated as a straight line and a
new "segment" constructed as a result of this approximation. This
procedure may be performed iteratively until no noise "segments"
are detected. The described approach has a significant advantage
over prior art FFT methods (which cannot discriminate between
high-frequency noise and sharp slopes of genuine EEG patterns) and
zero-crossing methods (which rely on DC offset and do not remove
noise).
[0250] All remaining "segments" may then be sorted according to the
value of their width parameter among conventional EEG frequency
bands. This sorting may be performed for both "downward" and
"upward" "segments" to enable accurate interpretation of
asymmetrical "segments". Once the "segments" are sorted for an
interval equal to one sleep study epoch, a simplified sleep/wake
discrimination may be performed by calculating a total duration of
"sleep-like" "segments" (sum of durations of all delta and theta
"segments") and comparing it with the half epoch duration. This
approach in fact represents a mathematical model of sleep/wake
discrimination based on visual interpretation of an EEG epoch.
[0251] Various means for fine-tuning this technique to achieve more
accurate detection of important EEG patterns and subsequently more
accurate sleep/wake discrimination are disclosed below. These
include algorithms for EEG artifact detection, delta wave
detection, periodic pattern detection and modified sleep/wake
discrimination rules which take into account a major role of EEG
periodic patterns (which may vary beyond alpha band), role of
context based decisions and the uncertainty associated with
artifacts.
[0252] The apparatus may include means for detecting and processing
artefact patterns in said signal including one or more of: [0253]
means for detecting flat intervals in the signal; [0254] means for
detecting intervals in the signal having a relatively sharp slope,
being intervals in which variation in the signal exceeds a first
threshold over a time interval equal to or shorter than a second
threshold; [0255] means for detecting intervals in the signal
having a relatively narrow peak, being intervals in which the width
parameter is equal to or less than a third threshold and the height
parameter is equal to or greater than a fourth threshold; and
[0256] means for detecting other non-physiological pattern in the
signal, being combinations of segments having a width and height of
one, the segments in the combination being less than the respective
total duration and signal variation of the combination by at least
preset ratios.
[0257] The apparatus may include means for detecting and processing
wave patterns characterised by minimum amplitude and minimum and
maximum durations, including: [0258] means for detecting a core
interval of the wave pattern as a sequence of one or more segments
which starts at a first time instant of a first segment when a time
derivative of the signal is substantially equal to zero and ends at
a second time instant of the last segment when a time derivative of
the signal is substantially equal to zero, or starts at the second
time instant of the first segment when the time derivative of the
signal is substantially equal to zero and ends at a third time
instant of the last segment when the time derivative of the signal
is substantially equal to zero, with the total signal variation of
at least the minimum amplitude, duration of at least a preset share
of the minimum duration, less than the maximum duration and the
maximum deviation from a monotonous change of at least a preset
share of the total variation.
[0259] The apparatus may include means for detecting a start and
end of a main wave of the wave pattern by subsequent comparison
with a preset threshold of a deviation of the slope of respective
components of segments preceding and following the core interval
from the slope of the core interval, and for updating the core
interval if the deviation of the slope and maximum deviation from
the monotonous change do not exceed respective preset thresholds,
and a total updated duration is equal to at least a preset share of
the minimum duration and is less than the maximum duration.
[0260] The apparatus may include means for detecting one or two
side waves of the wave pattern by subsequent testing of sequences
of combinations of segments preceding and following the main wave
for the signal duration conditions.
[0261] The means for sorting into a plurality of wave bands may be
based on the detected wave patterns. The means for classifying may
include means for comparing to preset threshold values of weighted
combinations of occurrences of the segments in the waveband,
artefact patterns and wave patterns. The apparatus may include
means for detecting periodic patterns with specified minimum and
maximum frequencies, minimum amplitude and minimum number of waves
including: [0262] means for selecting combinations of a specified
number of segments; [0263] means for assigning for each
combination, an average, minimum and maximum amplitude and an
average, minimum and maximum period; [0264] means for testing if
the average amplitude exceeds a specified minimum amplitude for a
periodic pattern; [0265] means for testing if the maximum amplitude
exceeds the minimum amplitude by not more than a specified ratio;
[0266] means for testing if the frequency corresponding to the
average period is equal to or greater than the minimum frequency of
the periodic pattern and is equal to or less than the maximum
frequency of the periodic pattern; [0267] means for testing if the
maximum period for a combination of segments exceeds the minimum
period by not more than a specified ratio; [0268] means for joining
combinations of segments, which comply with the above criteria; and
[0269] means for classifying a time interval of the signal data as
belonging to one of predefined states on the basis of a comparison
of the value of a weighted combination of durations of a plurality
of wave bands, artefact patterns and wave patterns with a threshold
which is set to a different value depending on the total relative
duration of periodic patterns within the time interval.
[0270] The apparatus may include means for classifying a time
interval of the signal data as belonging to one of predefined
states on the basis of a comparison of the value of a weighted
combination of durations of a plurality of wave bands, artefact
patterns and wave patterns with a decision boundary which is set to
a different value depending on the total relative duration of
periodic patterns within the time interval, if the difference
between the value and the decision boundary is equal to or greater
than a specified margin, or otherwise, on the basis of a comparison
of this value with the respective value for the preceding or
following time interval providing that that interval is already
classified and the difference between the respective values is
equal or less than the specified margin, or otherwise, if after
subsequent passes through the data, an interval is still not
resolved, on the basis of comparison of this value with a threshold
which is set to a different value depending on the total relative
duration of periodic patterns within the time interval.
[0271] According to a still further aspect of the present invention
there is provided a sensor for detecting position of an eye lid
including: [0272] first means adapted to move substantially with
said eye lid and relative to a second means; and [0273] means for
providing an electrical signal indicative of the position of said
first means relative to said second means, such that said signal
includes a measure of position and/or degree of opening of said
eyelid.
[0274] The first and second means may be electrically coupled such
that the coupling provides the measure of position and/or degree of
opening of the eyelid. The first and second means may be provided
by respective arms connected for relative movement. The arms may be
pivot ably connected to each other. Each arm may include a
capacitive element arranged such that the extent of overlap between
the arms determines the coupling between the capacitive elements.
Each capacitive element may include one plate of a capacitor.
Alternatively each arm may include an inductive element arranged
such that the extent of overlap between the arms determines the
coupling between the inductive elements. Each inductive element may
include a coil. The sensor may include means such as a wien bridge
for measuring the capacitive/inductive coupling between the
capacitive/inductive elements.
BRIEF DESCRIPTION OF THE DRAWINGS
[0275] Preferred embodiments of the invention will now be
illustrated and described with reference to the accompanying
drawings wherein:
[0276] FIG. 1 shows an overview flow diagram of one form of ADMS
according to the present invention;
[0277] FIG. 2 shows a graphical representation of typical AEPi and
Bi functions versus time for a patient undergoing general
anaesthesia;
[0278] FIG. 3 shows a flow chart of one form of bicoherence, real
triple product and bispectral index analysis;
[0279] FIG. 4 shows one form of sleep staging analysis;
[0280] FIG. 5 shows a block diagram of sample AEP:BIC analysis
(Mode 1) associated with weighting arbitration;
[0281] FIG. 6 shows a flow diagram of sample AEP:BIC analysis (Mode
1) associated with weighting arbitration;
[0282] FIG. 7 shows a simplified overview of scaling factor and
transition curve functions associated with the ADMS;
[0283] FIG. 8 shows a graphical representation of CIAi,TCU &
TUC values;
[0284] FIG. 9 shows a graphical representation of absolute value of
Ai+Bi, TCU & TUC;
[0285] FIG. 10 shows a graphical representation of AEPi;
[0286] FIG. 11 shows a graphical representation of Bi;
[0287] FIG. 12 shows a graphical representation of Ai;
[0288] FIG. 13 shows a graphical representation of BMi;
[0289] FIG. 14 shows a graphical representation of Bi with the
colour of the background changing to indicate transition of
consciousness state;
[0290] FIG. 15 shows a flow chart of an improved system for
monitoring consciousness according to a preferred embodiment of the
present invention;
[0291] FIG. 16 shows a simplified functional system overview (FSO)
of a preferred embodiment of apparatus according to the present
invention;
[0292] FIG. 17 shows a more detailed functional system overview
(MDFSO) of a preferred embodiment of aparatus according to the
present invention;
[0293] FIG. 18 shows a main flow diagram (MFD) of the HCM system
according to a preferred embodiment of the present invention;
[0294] FIG. 19 shows a flow diagram of one form of EEG analysis
format validation in Block 8 of FIG. 18;
[0295] FIG. 20A shows a flow diagram of computation of bicoherence,
real triple product and bispectral index in Block 10 of FIG.
18;
[0296] FIG. 20B shows a graphical representation of bispectrum,
bicoherence and real triple product in Block 10 of FIG. 18;
[0297] FIG. 21A shows a sample signal applied to a patient's
ear(s);
[0298] FIG. 21B shows a signal similar to FIG. 21A at a lower
sensitivity;
[0299] FIG. 21C shows a block diagram of hardware for generating
the signals in FIGS. 21A and 21B;
[0300] FIG. 21D shows one form of hardware for collecting AEP
sensory data from a subject;
[0301] FIG. 21E shows an example of the signal from the subject's
ear sensory nerve when receiving signals as shown in FIGS. 21A and
21B;
[0302] FIGS. 21F and 21G show examples of AEP output graphs for a
range of input frequency sweeps;
[0303] FIG. 21H shows a sample of response curves from AEP input
electrodes;
[0304] FIG. 22A shows a bar graph of Context Analysis Method and
FIG. 22a shows the corresponding display validation status;
[0305] FIG. 22B shows a bar graph of Context Analysis Probability
and FIG. 22b shows the corresponding display validation status;
[0306] FIG. 22C shows a bar graph of Transition Analysis Method and
FIG. 22c shows the corresponding display validation status;
[0307] FIG. 22D shows a bar graph of Transition Analysis
Probability and FIG. 22d shows the corresponding display validation
status;
[0308] FIG. 22E shows a bar graph of Movement Analysis Method and
FIG. 22e shows the corresponding display validation status;
[0309] FIG. 22F shows a bar graph of Movement Analysis Probability
and FIG. 22f shows the corresponding display validation status;
[0310] FIGS. 23A to 23C show graphical representations of system
output alarms, indicators and displays associated with Block 15 of
FIG. 18;
[0311] FIG. 24 shows a flow diagram of arousal detection in Block
16 of FIG. 18;
[0312] FIG. 25 shows a flow diagram of the process of detecting
zero derivative time instants and elementary maximum segments in
Block 21 of FIG. 18;
[0313] FIG. 26 shows a flow diagram of the process of detecting
zero derivative time instants and elementary minimum segments in
Block 21 of FIG. 18;
[0314] FIG. 27 shows a flow diagram of the process of sleep/wake
analysis and BIC EEG artefact removal in Block 21 of FIG. 18;
[0315] FIG. 28 shows weighted and display normalized BIC and AEP
data;
[0316] FIG. 29 is a sample of combined and weighted BIC and AEP
data with critical threshold and patient state display;
[0317] FIGS. 30A and 30B are tables showing examples of weighting
for combined (1,2,3,4,5) analysis index in Block 35 of FIG. 18;
[0318] FIG. 31 shows an example format for transition weighting
based upon context analysis in Block 37 of FIG. 18;
[0319] FIG. 32 shows a flow diagram for determining
consciousness/unconsciousness using combined AEP and BIC index and
R & K in decision context in Block 37 of FIG. 18;
[0320] FIG. 33 shows one form of apparatus for wireless linked
continuous blood pressure measurement;
[0321] FIG. 34A shows one form of sensor device for sensing and
measuring eye opening;
[0322] FIGS. 34B and 34C show alternative forms of the electronic
interface shown in FIG. 34A;
[0323] FIG. 35 shows one form of electrode system for integrated
anaesthesia monitoring;
[0324] FIG. 36 shows one embodiment of a wire connected sensor
device including bi-coherence, EOG, chin EMG and eye opening;
[0325] FIG. 37 shows one embodiment of a wireless integrated
electrode system including bi-coherence, EOG chin EMG and eye
opening;
[0326] FIG. 38 shows a preferred embodiment of a wireless
electrode;
[0327] FIG. 39 shows a flow chart of master firmware;
[0328] FIG. 40 shows a flow chart of slave firmware;
[0329] FIG. 41 shows an overview of primary, secondary and tertiary
analysis;
[0330] FIG. 42 shows one form of vehicle bicoherence wireless
system;
[0331] FIG. 43 shows a flow diagram of one form of audio and video
apparatus used for validating and replay in an in-depth anaesthesia
system;
[0332] FIG. 44 shows one form of pain level or consciousness level
remote indicator;
[0333] FIG. 45 shows a spread spectrum based wireless, active
electrode system;
[0334] FIG. 46 shows an indirect connected wireless module;
[0335] FIG. 47 shows one embodiment of a wireless based active
electrode system;
[0336] FIG. 48 shows a drug delivery system linked to a
consciousness monitoring device; and
[0337] FIG. 49 shows a power spectral curve of sample data.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0338] One form of Anaesthesia Depth Monitoring System (ADMS),
which utilizes a combination of Bispectral index (Bi), Audio Evoked
Potential index (AEPi) and Sleep Staging Analysis (SSA) for
improved Depth of Anaesthesia monitoring will now be described with
reference to FIGS. 1 to 14. The ADMS may present to the user a
single Index of 1 to 100, where 100 represents the highest state of
a monitored patient's consciousness and 0 represents the lowest
index of a monitored patient's state of consciousness.
[0339] AEPi, in the context of prior art depth of anesthesia
monitoring systems, has been reported as being more sensitive (than
Bi alone, for example) in the detection of the transition from
unconsciousness to consciousness. AEPi has also been reported to be
more responsive than Bi to patient movement in response stimuli.
However, Bi has been reported to increase gradually during
emergence from anesthesia and therefore may be able to predict
recovery of consciousness at the end of anesthesia (Gajraj et. al.
1999).
[0340] Prior art depth of anesthesia monitoring systems typically
deploy either AEPi, Bi or both indexes as separate measures. The
monitoring of AEPi and Bi as a combined index (CIAi) is preferable
in terms of a comprehensive depth of anesthesia monitoring system
with the ability to detect transition from unconsciousness to
consciousness (TUC), patient movement (AEPi) and the benefits of a
measure for the gradual emergence from anesthesia (utilizing
Bi).
[0341] A depth of anesthesia monitoring system should be simple and
unambiguous in its' use. This presents a problem because while a
single Bi or AEP index presents a simple user-friendly system, the
scope of a single measure (Bi or AEP) limits the accuracy of
measurement of depth of anesthesia. On the other hand, relying on
two separate measures (ie. AEPi and Bi) can complicate system
operation by producing confusion, such as an ambiguity as to which
of the two measures should be followed at any one point in
time.
[0342] One problem with conventional depth of anesthesia systems
that are dependent only upon Bi, for example, is an inability to
detect a transitional change from unconsciousness to
consciousness.
[0343] As can be seen in FIG. 2, the gradual change in the
Bicoherence in Zone C presents the anesthetist with a more gradual
indication of the transition from unconsciousness to consciousness.
In contrast to the Bi the AEPi does not present a gradual change
during emergence from unconsciousness but does provide a more clear
depiction of the transition point from unconsciousness to
consciousness. Concerns may exist in that the gradual changes in Bi
may not distinguish clearly or quickly enough sudden transition
changes, such as would be required to detect instances of a subject
"prematurely" emerging from unconsciousness. These "instances" of
early awakening (from anesthesia) can lead to potentially traumatic
occurrences of memory recall and other associated effects. If, for
example the TCU and TUC phases are not immediately apparent then
the chances of instances such as audio recall will increase.
Furthermore, the nature of the Bi EEG data is less likely to
distinguish the audio sensory nerve shutdown and awakening as
effectively as AEPi, due to the more direct hearing sensory
analysis associated with AEPi analysis. Thus claims of audio recall
with Bi based systems may render these technologies under greater
scrutiny in future depth of anesthesia monitoring applications.
[0344] The ADMS addresses these disadvantages by integrating the
strength of both AEPi and Bi, while still providing a single
user-friendly Comprehensive Integrated Anesthesia index (CIAi)
(refer STEP 23 and STEP 24).
[0345] A further difficulty exists with the current state of the
art depth of anesthesia monitoring in that variations between
different patients can alter the depth of anesthesia monitoring
parameters with each individual patient. Examples of variations
that can change the monitored parameters between different patients
include different levels of hearing performance between the
different patients. This variation can be of particular importance
where the comparison of AEPi transition thresholds and critical
thresholds, for example, is of importance. Other examples of
variations between patients, which can affect depth of anesthesia
monitoring outcomes between different patients, include gender,
body mass different sleep architectures, amongst other factors.
[0346] The ADMS of the present invention may alleviate these
difficulties by incorporating an automatic patient calibration
function. (refer STEP 5 and STEP 6).
[0347] A further difficultly exists where a depth of anesthesia
system requires ease of use but at the same time may be required to
accommodate the flexibility associated with providing a range of
system configurations. This range of different system
configurations can exist due to the fact that some Depth Of
Anesthesia (DOA) applications may not be practical, for example, to
attach multiple sensor or electrode systems (such as both AEP and B
sensors and electrodes). These situations may occur where the
simplest electrode or sensor configuration is required. Further
examples of where the system configuration may need to change
include situations where various electrodes or sensors may not be
performing reliably or to a minimal standard suitable for the
monitoring and subsequent analysis or various parameters.
[0348] In situations where the user elects to use one or more
different parameters or where electrodes or sensors are detected to
not be performing appropriately, it would be desirous for the DOA
monitoring system to allow the user to select or change the sensors
or electrode status, or alternatively for the DOA system to notify
the user and automatically compensate for the current electrode and
sensor configuration or performance.
[0349] The ADMS may address these difficulties by incorporating
automatic sensor and electrode scan (STEP 2) and MODE configuration
(STEP 4).
[0350] A further difficulty which arises with the task of
integrating AEPi and Bi is that in order to determine which of the
AEPi or Bi is most appropriate at any given point in time, an
independent method of arbitrating may be required to determine
which of the two methods (AEPi or Bi) should be utilized or given
higher weighting at any particular point in time.
[0351] The ADMS may address these difficulties by incorporating
Sleep Staging Analysis (SSA) as a means of independent arbitrating
for weighting of AEPi versus Bi. SSA analysis may provide a
determination of the context of a patient's depth of anesthesia
monitoring state and the state of a subject's consciousness based
upon SSA spectral based analysis, in contrast to Bi analysis basis
of phase difference and AEPi basis of averaging BSAEP amplitude
signals synchronized to the patient's hearing response. Where
"context analysis" refers to whether the patient's is entering a
state of consciousness or emerging from unconsciousness (refer STEP
16 and STEP 24).
[0352] The ADMS of the present invention may integrate into a
simple and singular index the benefits of both AEPi and Bi for the
optimal monitoring of a patient during anesthesia. The new system
uses an independent (to AEP or Bi) method of analysis being
spectral based or 1/2 period amplitude analysis (34, 35, 45, 46) as
a means to arbitrate which of the Bi and AEPi methods are most
optimal at any point in time. Furthermore, ADMS can provide
weighting to these said methods in order to combine or integrate
the monitoring of a subjects depth of anesthesia into a simple but
accurate single index.
[0353] Other difficulties with prior art technologies include an
inability to respond to a subject who may be paralyzed due to
muscle sedation and paralysis drugs administered in the course of
an operation to prevent unwanted body movements during the
operation. There have been incidents reported where patients were
indeed conscious or partially conscious during an operation and due
to the influence of paralyzing drugs were unable to alert medical
staff.
[0354] ADMS may alleviate these difficulties with the use of
Arousal index, Body Movement index and associated sensors, allowing
the patient movement status and/or arousal status alert,
independent of CIAi (STEP 23). A further difficulty that exists
with the current state of the art depth of anesthesia monitoring
systems is the inability to calibrate the sensitivity of the
monitoring device with each patient's individual variations and
sensitivity to anesthesia. ADMS may alleviate this difficulty by
incorporating a means of using Calibrated Patient (CALPAT) values
to modify or adjust these said transition threshold values on an
individual patient by patient basis (refer STEP 5 and STEP 6). The
means of this Automatic Calibration method are based around
measuring the patient's AEPi, Bi and SSA during the patient's first
occurrence of transitioning from Consciousness to Unconsciousness.
Thereafter Calibration of Patient transition values and display
zone values can be allocated specific to the individual patient's
sensitivity to AEPi and Bi.
[0355] A further difficulty with prior art AEPi based depth of
anesthesia monitoring systems is the difficulty to compensate or
accommodate for the varying hearing performance (or response to
audible stimuli) between different patients and also between
different audio stimulus apparatus and attachment of the audio
stimulus apparatus thereof (Lippincott-Raven, 1997). These
variations can be caused by factors such as the means used to
generate the audio stimulus, the attachment method and device type
(typically a single or pair of ear pieces are used to generate
audio click stimulus in ear piece), or physiological variations in
hearing performance evident between different subjects. The ADMS of
the present invention may adjust the frequency spectrum, amplitude
and phase of the audio click in order to provide optimal
compensation for different audio stimulus device and attachment
types and hearing variations (113).
[0356] It has been reported in recent publications (Vuyk 2002) that
prior art depth of anaesthesia monitoring systems suffer several
concerns. One concern relates to the use of the bispectral index
monitor (Aspect Medical Systems, Inc., Natick, MA). This device
relies upon the bispectral index (BIS) to monitor
consciousness-sedation-unconsciousness levels. However it has been
reported that various anaesthetic agents on the bispectral index
scale appear to be agent specific. In general, it has been reported
that agents such as propofol, midazolam or thiopental have a strong
depressant effect on BIS. It has also been reported that inhalation
anaesthetic agents propagate an intermediate depressant effect on
BIS. However, it has been reported that the opoids have little or
no influence on the BIS at clinically relevant concentrations. Also
disconcerting is the fact that nitrous oxide and ketamine appear to
have paradoxical effects on the BIS. Accordingly it has been
suggested that BIS may relate well to sedation and hypnosis levels
but does not properly reflect level of analgesia or depth of
anaesthesia.
[0357] One inherent difficulty with the prior art depth of
anaesthesia monitoring systems relying upon BIS (Aspect) as the
main index or measure of depth of consciousness is the risk that a
patient may lapse into a state of consciousness or indeed not enter
or continue their state of unconsciousness, during critical times
of an operation procedure.
[0358] The ADMS system of the present invention may alleviate or
reduce risk of this difficulty by incorporating a Brain Stem Audio
Evoked Potential Middle Latency (BSAEPML) signal as a precise
indicator of a patient's transition from consciousness to
unconsciousness and transition from unconsciousness to
consciousness. This added factor of monitoring may operate in
real-time and simultaneously with the measure of bispectral
analysis. Thus different effects that can relate to different
anaesthetic agents are reduced or alleviated by a measure of
BSAEPML which is directly related to thermus and temporal lobe
generators which in turn are directly related to the patients state
of consciousness, and most importantly by definition of the
function of (BSAEPML), to risk of memory recall associated with
critical times within operation procedures.
[0359] The prior art utilises BSAEP, typically being 1/2 second
frequency audio stimulus clicks as a method of auditory sensory
stimulation. The ADMS may incorporate a steady state Audio Evoked
potential capacity, including a capacity to provide higher
continuous frequency of audio clicks. The range of audio frequency
capacity may be from 1/2 Hz to 100 Hz. The steady state BSAEP may
provide greater sensitivity in that more subtle changes may be
measured from the subject's BSAEP. Responsiveness may also be more
precise due to the fact that there is less time between consecutive
stimuli clicks and therefore there is less delay and less
likelihood in missing physiological responses.
[0360] The ADMS may include a capacity to provide interactive
SSAEP. The latter may provide click stimulus sequences with a
different or varying rate according to the patient state detected.
This dynamic or programmable click stimuli may allow the system to
"validate" more accurately and precisely patient status under
various stimuli frequency and amplitude conditions, where these
conditions can vary the stimuli test sequence.
[0361] The ADMS may include a capacity to provide multiple
frequency (typically dual frequency) steady state BSAEP. In
particular 40 Hz and 80 Hz continuous click stimuli frequencies may
be used, where the 40 Hz and 80 Hz click rates are sequentially
toggled or switched between. A typical sequence may be 5 seconds of
each continuous 40 Hz audio click stimuli followed by 5 seconds of
continuous 80 Hz audio click stimuli, in a continuous sequence
toggling between 40 Hz and 80 Hz. The 40 Hz (or similar lower
frequency rate) audio stimuli allows middle latency testing of the
AEP, which may provide a graduated measure or monitoring in
accordance with the subject's state of consciousness but more
importantly, a precise transition state from consciousness to
unconsciousness and unconsciousness to consciousness. The 80 Hz
audio stimuli and corresponding AEP may allow a graduated measure
and monitoring method for brain stem cortical response which is an
important signal for detecting a patient's risk of neurological
damage or risk of serious or fatal over sedation of anaesthesia,
for example. This 80 Hz (or similar higher frequency rate) may
provide an ideal alert or warning measure for the ADAMS to prevent
or reduce risk of over-sedation or excessive depth of anaesthesia.
This 80 Hz Continuous State and is BSAEP signal is a key measure
for brain life or death status.
[0362] The ADMS integrated index and integrated monitoring
capability may vastly improve upon the prior art by incorporating
an important measure of hypnotic consciousness state (utilising
bispectral analysis), incorporating effective transient state
measure from consciousness to unconsciousness and unconsciousness
to consciousness utilising lower continuous frequency such as 40 Hz
click stimuli, and also incorporating critical brain stem cortical
status warning and alert capability, particularly for reduced risk
of over administration of anaesthetic or sedation drugs.
[0363] Multiple frequency (typically dual frequency) Brain Stem
Stead State Audio Evoked Potential monitoring and subsequent
electrode attachment, and bispectral monitoring and subsequent
electrode attachment may be achieved with as few as one and as
simple as one self-adhesive electrode attachment strip, requiring
little or no electrode preparation. This is due to the ADMS
system's capability to deploy the above mentioned wireless and/or
battery powered audio stimulus earpiece and also a single electrode
substrate with a unique combination of 2 inbuilt forehead EEG
electrodes (for bispectral EEG signal-attached near patient's
forehead outer malbar bones) and one further inbuilt electrode
which may be positioned about 1 to 2 cm from the patients centre
ear towards the patient's nose.
[0364] This single self-adhesive substrate with as few as three
inbuilt electrodes may optionally include a disposable electrode
format. A further option may allow a self sealed outer sterile
cover typically plastic or foil to be removed to access the new
electrode. The sensor may optionally contain an in-built electrode
and a use by date of the electrode may be clearly marked on the
outer electrode packaging. This system of inbuilt battery and due
date labelling may avoid conventional technology issues and risk of
both cross infection and flat batteries during critical use. A
light, durable and non-obtrusive electronics module may optionally
be clipped to the electrode substrate providing wireless interface
to the ADMS measuring device.
[0365] The ADMS may include active electrodes, whereupon an
electronics module (separate or inbuilt and disposable) may provide
close location of amplifier buffering to the inbuilt electrodes.
This closely located electronic buffering circuitry may provide an
electrode system, which is less vulnerable to stray capacitance and
external noise pick-up. Electrode impedances may be higher and may
avoid issues of prior art electrode systems, whereupon extra
preparation is required to clean and abrade the patient skin
surface, to achieve acceptably low impedance between patient
connections. Typical impedances with non-active electrodes may be 5
kilo-ohms compared to 50 Kilo-ohms or more, which is acceptable
with active electrode configurations.
[0366] The electrode buffer (and/or amplification and filtering)
electronics may be embedded within the substrate of the electrodes
directly near the in-built electrodes, using flexible printed
circuit techniques, such as circuit tracks printed on or within the
electrode substrate.
[0367] A further feature of the ADMS electrode connection is that
it may not require direct electrical connection to the patient. A
new approach to human electrical activity detection has been made
possible by recent advances in ultra-low-noise, ultra-high-input
impedance probes. These probes do not require a real current
conducting path and operate on the general principle of induction
of a signal from a non-contact source.
[0368] This technology may provide a unique application for the
ADMS electrode configuration options. Electrophysiological
connection of the forehead EEG connections allowing monitoring and
analysis for AEP and bispectral analysis may be implemented by
electrical probes, which are embedded into the electrode substrate
device and thus allow signal monitoring with minimum invasion.
[0369] The electrode and wireless systems may be used in a
configuration of only forehead electrode provision for
predominantly bispectral analysis. This type of simplified
configuration may be especially suited to driving, operator or
other vigilance monitoring and may be life saving for detection of
fatigue onset (change in hypnotic and consciousness states can be
detected, for example). The vehicle driver using this system may
simply opt to open the disposable electrode packet, remove a
battery start enable paper tag and self-attach the discrete and
virtually undetectable wireless electrode to the forehead under the
hairline, while a wireless mounted dash (or cigarette lighter
connected) device monitors and alerts the fatigued or drowsy
driver, potentially preventing a fatal road accident.
[0370] A further concern of prior art depth of anaesthesia
monitoring systems which do contain some form of (BSAEPML) is that
the attachment of an earpiece or other means of auditory stimulus
systems to a patient during a surgical operation or other medical
procedure can be disconcerting, too invasive and wires and cable
can indeed cause unnecessary or potentially distracting concerns of
entanglement or other adverse effects.
[0371] The ADMS system may address these difficulties and
limitations by utilising a real-time wireless connected audio
stimulus device, which is designed to avoid reliance on wires, may
be as small as the tiniest hearing aid and may be attached to the
patient with a simple non-invasive insertion process. Furthermore
the device may include a disposable cover system designed to avoid
cross infection while allowing the more expensive audio stimulus
device to be re-used.
[0372] Furthermore the speedy disposable changeover cover system
may include a unique protective-disposable-cover option providing
high reliability and convenience of a wireless BSAEPML audio
stimulator whilst being highly user friendly and attractive for
critical environments such as operating theatres. This provides a
protective cover with an integrated disposable battery and an
industrial design which allows a totally fool proof attachment of
the cover with a "snap in" battery function (either rechargeable or
single use). The "snap in" function, denotes that when the user
opens a sealed pack which displays the use by date for the
cover-battery (protective cover for audio stimulus device with
integrated battery), the user has a simple battery activation means
such as removal of a paper or cardboard tag labelled for example
"remove when ready to start. This type of methodology may ensure
that the user never needs to contend with flat battery issues while
the protective cover with integrated battery avoids cross
infection. The "fool-proof" battery with cover method at attachment
and use may be by way of the battery sliding, clipping, magnetic
slotting, or slotting only into or onto or part of the wireless
audio stimulus device.
[0373] The method of incorporating an anti-cross-infection and
battery management system into a foolproof cover system alleviates
two major issues namely, risk of the battery going flat and
cross-infection and may be adopted in all sensors and electrode
applications.
[0374] FIG. 1 shows an overview flow diagram of an ADMS according
to a preferred embodiment of the present invention. Steps 1 to 26
in the flow diagram are described below.
Step 1--Start up ADMS and Attach Patient Electrodes and Sensors
[0375] For ease of use and minimal electrode configuration a
typical electrode attachment system may include a single
self-adhesive forehead electrode system. Alternatively a single
sensor device extending over the patient's forehead and chin may be
used, to allow the forehead EOG, EEG and reference connections/AEPi
connections (where AEPi reference may include a mastoid
connection), while also allowing EMG via chin surface and mastoid
reference near patients ear and optionally wire or wireless
connected earpiece for audio stimulus connection. The electrode
device could contain 3 electrodes whereupon Bi and SSA EEG signals
are derived from the forehead electrode connections (outer malbar),
EOG signals are also estimated from the forehead connections and
reference is derived from the central forehead connection.
Alternatively the electrode device could contain 6 electrodes,
being the abovementioned electrode with the addition of 2
electrodes for detection of chin EMG (for SSA EMG and arousal
detection), mastoid electrode connection for bipolar reference
signal where the aforementioned forehead connection provides the
BSAEPi signal, and optional earpiece interface or connection for
AEP audio stimulus. SSA EEG and EOG signals would be derived as
estimations of conventionally placed (per sleep monitoring clinical
standards (34, 114)) for EEG, EOG and EMG signal monitoring.
Estimations are required in order to allow the minimal and
simplified ADMS configuration, while still providing SSA.
[0376] Incorporating the self-adhesive forehead attached electrode
system as a single attached substrate would provide simplification
and ease of use. A wire or wireless connected ear-phone can be
applied to one or both of the patient's ears for the purpose of
generating the AEPi stimulus click sound. Where wireless
configuration is used the earpiece could be connected to the
electronics module for power and wireless control interface. One of
the unique aspects of the ADMS system is the ability to adjust the
volume of the stimulus click beyond a default "normal" (or standard
value per empirical data (ref 1)), in order to compensate for
hearing performance variations between different patients.
[0377] The electronics module can clip or attach to the outer
surface of the disposable electrode substrate and provide wireless
interconnection and active electrode functions. Embedded within the
disposable electrode substrate could optionally be a disposable
battery, thus avoiding the need otherwise for recharging of
electronics module battery source (electrode may be subject of
separate patent). Optionally the electrode system can be designed
as a re-usable device.
[0378] The Electronics Electrode and Sensor Module (EESM) can be a
fast charge system with charge capability electrically connected
(for example, via electrode press-stud connections), slow charge
system (via induction or RF interface to EESM recharge circuits),
or a combination of both systems. Clear EESM indication (ie. LEDs
status) of remaining battery life and remote warning of pending
battery flat alert is always active. An easy to clean container
neatly holds EESM module and sensors, while at the same time
providing ongoing charge function. One or more LEDS provide a clear
status at any time for the remaining hours of charge energy.
[0379] Psuedo Code Sample (Psuedo code may be expanded upon or
deleted [0380] dependent upon whether the preferred embodiment
demands such detail); ADMS Initialization; [0381] System
initializes with STARTADMS in deactivated (switch-up) position;
ADMS=O [0382] System initializes in the uncalibrated patient mode;
CALPAT =O (calibrated uninitialised). [0383] STARTADMS=0 when start
switch is de-activated (up position). [0384] STARTADMS=1 when start
switch is activated (down position). Step 2. Automatic Sensor and
Electrode Check and Status
[0385] Connected electrodes are detected for purpose of automatic
system configuration and notification of automatic sensor and
electrode quality status check.
[0386] Periodic impedance scanning of all electrophysiological
electrodes provides the ADMS the capability to detect a
deterioration of signal quality at any point in time. The operator
is then provided the option to correct the poor electrode
connection. Alternatively the ADMS is automatically re-configured
to accommodate a revised configuration, designed to monitor a
subject's depth of anesthesia in the absence of the disconnected or
poor electrode contact(s).
Step 3. AEP Automatic Calibration
[0387] One of the impediments of previous art systems using BSAEPi
as a marker for monitoring depth of anesthesia is the difficulty to
compensate or accommodate for the variatations in hearing
performance (or response to audible stimuli) between different
patients (Lippincott-Raven, 1997).
[0388] These variations can be caused by factors such as the
generation of the audio stimulus attachment method and device type
(typically a single or pair of ear pieces are used to generate
audio click stimulus in ear piece), or the physiological variations
in hearing performance evident between different subjects. The ADMS
is capable of adjusting the frequency spectrum, amplitude and phase
of the audio click in order to provide the optimal compensation for
variations between different audio stimulus devices, variations due
to different attachment methods (of audio stimulus device ie. ear
piece or headphones), or variations due to different hearing
performance between individual patients (112).
[0389] An object of the present invention is to provide a means to
calibrate the ADMS AEPi monitoring function for each specific
patient's hearing response. This capability, within the ADSM, is
achieved by providing a range of audio calibration stimuli signals
and measuring the AEPi response to this said range of stimuli.
While measuring the AEPi response the data is compared to empirical
clinical data. The Sound Pressure Level (SPL) of the audio stimulus
can be adjusted until the desired response, as comparable to the
normal standard hearing patients, as referenced from the empirical
data.
[0390] Factors such as polarity of the stimulus delivery apparatus
can be checked and compensated for, as required. At all times safe
SPL levels can be verified to ensure safe audio stimulus
conditions.
[0391] One of the unique aspects of the ADMS system is the ability
to adjust the volume of the stimulus click beyond the default
"normal" value, in order to compensate for hearing performance
variations between different patients.
[0392] In the circumstances, where patient's BSAEP signal does not
respond to the AEP threshold levels as expected from "normal"
patient hearing performance, the ADMS system provides a servo gain
control function. The servo system is achieved by adjusting the AEP
audio stimulus click amplitude level (while ensuring safe levels
are not exceeded at any time) until the AEPi signal derived from
the patient's data is similar to the levels as expected from normal
patients. Further calibration of the AEPi signal can be achieved by
detecting a particular patients hearing performance at different
frequencies or combinations of frequencies and different levels
thereof, and optimization of spectral content of the audio stimulus
signal in order to compensate for each patient's individual hearing
variations. In this manner the most reliable or efficient hearing
performance conditions can be determined for each particular
patient to ensure the AEPindex is derived for that patient under
the most stable and reliable AEP stimulus frequency and amplitude
conditions on an individual patient by patient basis. These same
principals can be used for the automatic and optionally remote
servo control of hearing aids (a subject of separate patent).
Step 4. Automatic ADMS Configuration
[0393] * The ADMS system is capable of providing user adjustable or
factory default MODES. A library of MODE configurations can be
configured for different patient types or user specific
requirements.
[0394] Subject to connected electrodes and sensors and the status
of each of the said sensors and electrodes (per above step) the
ADMS system determines the system configuration. [0395] MODE
1--Integrated AEPi and Bi. [0396] MODE 2--Bi only. [0397] MODE
3--AEPi only. [0398] Option 1--Body Movement Multi-zone movement
Biomat sensor. [0399] Option 2--Body Movement Single-zone movement
Biomat sensor. [0400] Option 3--Electrophysiological Arousal
Detection (derived AEP and/or B EEG electrodes).
[0401] ADMS MODES 1 to 6 can be configured (automatically or with
manual assistance) with options 1, 2 or 3. The ADMS system will
detect the presence of a mattress sensor and type as being single
or multi-zone. The ADMS system will also, by default, detect the
forehead EEG electrodes and the chin EMG electrodes for arousal
analysis and event detection. A logical OR function will, by
default, display an arousal event if an arousal is detected from
the forehead EEG OR the Chin EMG electrodes. For the purpose of
this description of preferred embodiment we will assume: [0402]
MODE 4--Integrated AEPi and Bi with SSA as Bi-AEP arbitrator.
[0403] Option 1--Body Movement Multi-zone movement Biomat sensor.
[0404] Option 3--Electrophysiological Arousal Detection (derived
from chin EMG electrodes of forehead EEG electrodes).
[0405] Automatic Electrode and Sensor Pass-Fail detect and Mode
select.
[0406] Fail condition for AEPi, Bi or SSAi is signaled when any of
the respective electrodes or signals is poor quality. Poor quality
electrodes (for example) would be signaled if the impedance of the
said electrodes were above the acceptable electrode impedance
thresholds. Typical impedance threshold would be 10 thousand ohms
impedance, for example. Above this threshold (10K) value ADMS would
signal the user exactly which electrode is not performing
appropriately, what steps can be taken to alleviate the problem.
Alternatively the user can be prompted to request the ADMS system
to reconfigure the system MODE in order to ignore the poor
electrode connection. The 10 K threshold can be changed to the
user's selection.
[0407] Fail condition for AEPi, Bi or SSAi would signal that the
respective index should be weighted to zero. Therefore zero
weighting of analysis in response to signal failure automatically
changes mode in accordance to above STEP 4 and the following
table.
[0408] Mode 1 impedance weighting effects are shown in Table 1
below: TABLE-US-00003 TABLE 1 AEPi Bi SSAi Pass Pass Fail MODE 1 -
Integrated AEPi and Bi without SSA arbitration. Pass Pass Pass MODE
1 - Integrated AEPi and Bi with SSA arbitration. Fail Pass Fail
MODE 2 - Bi only. Fail Pass Pass MODE 2 - Bi only. Pass Fail Fail
MODE 3 - AEPi only. Pass Fail Pass MODE 3 - AEPi only. BM-SZ BM-MZ
AR Pass Fail Fail Option 1 - Body Movement (BM) movement Biomat
Single-Zone (SZ) sensor. Fail Pass Fail Option 2 - Body Movement
(BM) movement Biomat Multi-Zone (MZ) sensor. Fail Fail Pass Option
3 - Electrophysiological Arousal Detection. Pass Fail Pass Option 3
and 1- Fail Pass Pass Option 3 and 2- NOTE 1: BM-SS = Body Movement
Single Sensor; BM-MS = Body Movement Multi-Sensor NOTE 2: Subject
to connected electrodes and sensors (per STEP 2 above) and the
status of each of the said sensors and electrodes the ADMS system
determines the system configuration.
Step 5--Patient Specific Calibration Transition Values-Calpat
[0409] The Transition thresholds of Consciousness to
Unconsciousness (TCU or change of zone A to B), Transition from the
deepest stage of Unconsciousness to a lesser degree of
Unconsciousness (change of Zone B to C) and Transition from
Unconsciousness to Consciousness (TUC or Zone C to D) in the ADMS
system is determined from either default values as derived from
empirical clinical data (see below) or from values as derived by
way of thresholds determined with Calibration of Patient (CALPAT)
function.
[0410] Graphic reference of AEP and Bi showing phases of a typical
anesthesia monitoring session are shown in FIG. 2. Tables 2 to 4
below describe the associated ADMS transition zones. TABLE-US-00004
TABLE 2 1. Start of monitoring 2. Zone A . . . C 3. Transition from
Zone A to B . . . TCU 4. Zone B . . . U 5. Transition from Zone B
to C . . . TSW 6. Zone C . . . . . . U 7. Transition from Zone C to
D . . . TCU 8. Zone D . . . C
[0411] FIG. 2 presents a typical AEPi and Bi versus time functions
for a patient undergoing general anesthesia. The Horizontal axis
represents time progressing left to right from the earliest to
latest time. Note the gradual Bi curve ascension in zone C versus
the steeper ascension of AEPi in Zone C for the Transition of
patient from Unconsciousness to Consciousness. The new ADMS system
produces an ideal depth of anesthesia monitoring by incorporating a
method to deploy the advantages of both AEPi and Bi, while
presenting a simple single anesthesia Depth of Anesthesia
monitoring index. TABLE-US-00005 TABLE 3 ADMS ZONE -> ZONE A
ZONE B ZONE C ZONE D Transition Zone-> C TCU U TSW U TUC C
Default or Empirical Data -> IDDZA IDTCU IDDZB IDTSW IDDZC IDTUC
IDDZD Calibrated Patient Data -> CPDZA CPTCU CPDZB CPTSW CPDZC
CPTUC CPDZD NOTE: Values exist for Bi and AEPi for each of the
transition points and zones A, B, C and D.
[0412] TABLE-US-00006 TABLE 4 Definition of Zones A, B, C, D Zone
Ranges CODE and Events. Key Description Zone A C Patient in
Consciousness State. TCU Transition from Consciousness to
Unconsciousness Zone B U Patient in unconscious state. Zone C U
Patient in unconscious state. TUC Patient Transition from
Unconsciousness to Consciousness Zone D C Patient in Consciousness
State. BM BMe Presence of Body Movement events (ref 34). Ae Ae
Presence of Arousal events (ref 35)
[0413] Patient Calibrated values refers to modifying or adjusting
these said transition threshold values on an individual
patient-by-patient basis.
[0414] The means of this Automatic Calibration method are based
around measuring the patient's Bi during the patient's first
occurrence of transitioning from Consciousness to Unconsciousness
(in accordance with AEPi TCU empirical data transition threshold
level (refer step 7).
[0415] After Calibration of Patient, transition threshold values
(TCU, TUC) and display zones (C, U) can be allocated specific to
the individual patient's sensitivity to AEPi and Bi.
[0416] Default empirical data values of AEPi (ref 3), Bi (ref 3)
and SSA are compared to data of the patient's first Transition from
Consciousness to Unconsciousness (TCU) is detected by observing
AEPi, Bi and SSA transitioning through the respective TCU threshold
values. This initial or first transition of consciousness state
serves as a calibration point for the ADMS system to optimize to
each individual patient's depth of anesthesia AEPi, Bi and SSAi
monitoring sensitivity.
[0417] Once the first consciousness to unconsciousness transition
has been tracked and analyzed using the ADMS system's CALPAT
function, all other transitions (A to B, B to C, C to D) and
monitoring Display Zones (A, B, C, D) can be optimized or
fine-tuned to the individual patient's sensitivity. The
relationship between AEPi and Bi at TCU is unique to each patient
and can be used to extrapolate each individual patient's TUC
threshold values.
[0418] The ADMS uses the basic principal that detection of the TCU
for a specific patient allows all subsequent transitions and zones
to be estimated with greater sensitivity and accuracy than using
empirical data solely (as with prior art DOE systems).
[0419] General overview for CALPAT operation: [0420] a) After
ADMSSTART is selected the ADMS monitors patients AEPi, Bi and SSA.
[0421] b) Empirical data values derived for typical (ref 1)
conditions of TCU are compared to actual and real-time patient's
data for AEPi, Bi and SSA. [0422] c) The weighting factor applied
to each of AEP, Bi and SSA is dependent on the following factors.
[0423] d) When the TCU (Transition A to B) has been identified for
a specific patient is derived. [0424] e) The TCU transition is
noted in terms of the AEPi, Bi and SSA value. The noting of these
corresponding TUC values, allows the accurate switching and
monitoring of AEPi and Bi subsequent to changing transitions (A to
B, B to C and C to D) and Display Zones (A, B, C, D). [0425] f)
When the TCU (Transition A to B) has been identified for a specific
patient other transitions (B to C-TSW, and C to D-TCU) can then be
derived from this calibration data.
[0426] Deriving the subsequent transition states from the
CALPAT.
[0427] TCU state is more sensitive and accurate for a given patient
than reliance on empirical data values for these subsequent
transition states (B to CTSW, C to DTUC).
[0428] Sample pseudo code sample for CALPAT function (Psuedo code
may be expanded upon or deleted dependent upon whether the
preferred embodiment demands such detail). [0429] a) Select
STARTADMS=1% Wait till ADMSSTART button is selected
[0430] b) Assign defaults TCU, TSW and TUC values from empirical
data transition thresholds (3). TABLE-US-00007 TABLE 5 Empirical
Empirical Bi AEPi SSA Conditions Zone Transition- Transition- (Ref
34, 45, 46, 113) Transition ref1 ref1 (ref STEP 16) Zone A to B
(TCU) 76 65 CAW > S OR CA2W > S Zone B to C (TSW) 40 36 CA3W
> S Zone C to D (TUC) 74 50 CAW > S OR CA2W > S AEPi Bi
Assign TCU 65 76 Assign TUC 50 74
[0431] d) Start CAP PAT procedure and determine patient specific
values for TCU and TUC. [0432] e) Read Current Patient Data value
for AEPi, Bi and SSAi % read the real-time patient data and Compare
this real patient TCU data values to TCU Empirical Data values for
AEPi, Bi and SSAi.
[0433] If Current Patient Data AEPi Value (CDAEPi) for Transition
from Consciousness to Unconsciousness (TCU) is less than or equal
to (<=) Empirical Data AEPi Values (IDAEPi) for Transition from
Consciousness to Unconsciousness (TCU) note the Current Data values
for Bi and SSAi.
[0434] The said CD values for Bi and SSA are assigned respectively
to variables for Calibrated Patient data for Transition from
Consciousness to Unconsciousness for Bi (CDTCUBi) and Calibrated
Patient data for Transition from Consciousness to Unconsciousness
for SSAi (CDTCUSSA). [0435] f) Assign; [0436] CPTCUBi [0437]
CPTCUSSA [0438] g) Transition states TSW and TUC are now derived
from TCU.
[0439] Calibrated Patient data for Transition from Unconsciousness
to Consciousness for AEPi (CPTCUAEPi) will be derived from
CPTCUAEPi transition state, ie. CPTUCAEPi is proportional or
related to CPTCUAEPi.
[0440] Note that in more complex embodiments of the ADMS more
complicated calibration of the patient's variables can be applied
to provide a greater degree of patient sensitive system
calibration.
[0441] Once the value for CPTCUBi is established per above, CPTUCBi
can be derived (in a simple embodiment as described herein) by
using the ratio derived from empirical data being IDTUCBi/IDTCUBi.
[0442] h) Assign value for CPTUCbi [0443] CPTUCBi =IDTUCBi/IDTCUBi
X CPTCUBi
[0444] Further embodiments can utilize a more sensitive formula
based on applying any combination of TCU, TSW and TUC derived from
using patient AEPi, Bi and SSAi data to derive TSW and TUC.
[0445] PATCAL and default threshold determination for TCU and TUC
The thresholds for TUC and TCU for BSAEPI and Bi can vary between
patients. The current ADMS sample embodiment assumes that the
relationship between these TCU and TUC values is able to be derived
(refer step 7, ADMS sample embodiment) from empirical data (Gajrag
et al 1999) and then modified for individual patient compliance
with PATCAL function (per step 5, ADMS sample embodiment). However,
as the ADMS system further evolves, the means of providing a more
accurate determination of the TCU and TUC thresholds may also
evolve, particularly with increased clinical data and experience
with this device. Combinations of the following variables can
assist the ADMS in predicting more accurate default and PATCAL TCU
and TUC values; BSAEPi, Bi, SSA, eyelid opening and movement
status, eye movement status, arousal and body movement status.
STEP 6. is Calpat Set?
[0446] If no go to Step 7 and if yes go to Step 9.
STEP 7. Set to ADMS Default (Imperecal Data) Display Zone Functions
(DDZF). (DDZA, DDZB, DDZC, DDZD)
[0447] Empirical data values are referenced as a means of
establishing the transition of zones A, B, C and D based on data
collected from normal patients.
[0448] The empirical data values used for the purpose of this
embodiment and simplicity of presentation is set out in Tables 6
and 7 below (3). TABLE-US-00008 TABLE 6 Empirical AEPi Empirical Bi
SSA Conditions Zone Transition Transition Transition (Ref 34, 45,
46, 113) Zone A to B(TCU) 65 76 (W OR STG1) to (STG2 or STG3) Zone
B to C(TSW) 36 40 STG2 or STG3 to 4 or REM Zone C to D(TUC) 50 74 W
or STG1 to STG2 or STG3
[0449] TABLE-US-00009 TABLE 7 AEPi Zone Ranges Bi Range Range SSA
Conditions ref Zone A 100-82 100-65 W OR STG1 Zone B 82-40 65-35
STG2 or STG3 or STG4 or REM Zone C 40-75 35-50 STG2 or STG3 or STG4
or REM Zone D 75-100 50-100 W OR STG1
[0450] Determination of switching or weighting of BSAEPi and
BI.
[0451] It has been reported (Gajrag et al 1999) that AEPi provides
improved detection of the transition from unconsciousness to
consciousness (TUC).
[0452] This may be due to BSAEP reflecting the neural response to
the auditory sensory nerve, as stimulated by the application of
ADMS earphones click stimulator, to a subject's ear and auditory
nerve. An increase in the AEP signal can provide a sensitive
measure of the audio sensory nerves response (or lack of in state
of unconsciousness) with communication paths to the brain (BSAEP),
and in particular the associated vulnerability to incidence of
audio recall.
[0453] The "switching on" or activation of the auditory BSAEP
communication paths provides a more rapid signal change and
subsequent measure of transition state than that of BIC. BIC
signal, in contrast, is a measure of overall brain activity and can
incorporate a mixture of control signals for the body. These
"mixture" of signals may not directly relate to the consciousness
factors or factors effecting depth of anesthesia status, such as
vulnerability or risk of post-operative memory recall.
[0454] In MODE 1 the ADMS is capable of referring to the SSA
analysis and in particular the patient's EEG spectral composition,
to assess the progression from one stage of unconsciousness (sleep)
to a lighter stage of consciousness (sleep). This "independent"
(from BSAEP and BIS) assessment of SSA, aids the arbitration
process. Improved determination of pending onset of the TUC
transition can therefore be achieved with the ability to apply
closer analysis and measurement focus on the rapid increase in the
BSAEP signal (as would be expected with TUC).
[0455] Other MODES of the ADMS are capable of applying any
combination of BSAEPi, Bi, SSA, eyelid opening and movement status,
eye movement status, arousal and body movement status, as a means
of determining switching or weighting between Bi and BSAEPI.
Step 8. Set to ADMS Default (Imperecal Data) Display Zone
Transition Functions (IDZTF). (IDZTCU, IDZTWS, IDZTUC)
Step 9. Set to ADMS Calpat Display Zone Functions (CPDZF). (CPDZA,
CPDZB, CPDZC, CPDZD)
Step 10. Set ADMS Display Zone Formulas A, B, C, D. to Calibrated
Patient Display Zone Transition Formulas (CPDZTF)
Step 11. Set ADMS Alarm Thresholds
[0456] Display Zone Critical Alarms Thresholds (DZCAT) are defined
These DZCAT consist of alarm warnings and display notification of
particular importance to ADMS user, including body arousal or
movement for example.
[0457] The DZCAT can be presented as markers on the CIAi display,
alarms of other forms of user notification to assist the ADMS
operation. BMi and Ai can be used to weight or bias the CIAi
towards patient consciousness state and/or represented as separate
display, alarms of other forms of user notification to assist the
ADMS operation.
STEP 12--AEPi Analysis (ref 3, 61)
STEP 13--AEPi Analysis DISPLAY OR PRINT
STEP 14--Bi Analysis (ref 3)
[0458] FIG. 3 shows a flow chart of one form of bicoherence, real
triple product and bispectral index analysis. Computation of
Bispectrum (B), Bicoherence and Real Triple Product B .function. (
f1f2 ) = I = 1 L .times. .times. Xi .function. ( f1 ) .times. Xi
.function. ( f2 ) .times. Xi .times. * .times. ( f1 + f2 ) ##EQU1##
[0459] Epoch length=30 seconds [0460] 75% overlap of epochs to
reduce variance of bi-spectral estimate [0461] L=epochs, i.e. 1
minute of data [0462] f1&f2 are frequency components in the FFT
such that f1+f2<fs/2 where fs is the sampling frequency Real
Triple Product (RTP) RTP .function. ( * .times. f1f2 ) = I = 1 L
.times. .times. Pi .function. ( f1 ) .times. Pi .function. ( f2 )
.times. Pi .function. ( f1 + f2 ) ##EQU2## Where Pi(f1) IS THE
POWER SPECTRUM P(F)=|X (F)|.sup.2 Bi-coherence (BIC) BIC .function.
( f1f2 ) = 100 .times. B .function. ( f1f2 ) RTP .function. ( f1f2
) ##EQU3## ranging from 0 to 100% Step 15--Bi Analysis Display or
Print Step 16--Sleep Staging Analysis (SSA) (34, 35, 45, 46)
[0463] FIG. 4 shows one form of sleep staging analysis. Referring
to FIG. 4, the Sleep Staging Analysis (SSA) provides two data
descriptions, being the context analysis (described below in the
form of S1W>S etc) and sleep stage estimation of a subjects
(derived) EEG, EOG and EMG data (in the form of sleep stage as
derived from spectral analysis of EEG and correlation of EMG and
EOG signals (34, 45, 46). "Derived" in FIG. 5 denotes that these
signals may be direct electrode connections to the scalp for
neurology, nears patient's eyes for EOG, near patients chin or
cheek for EMG signals, or alternatively may be derived from a
single forehead (or forehead to chin area) electrode
attachment.
[0464] If only forehead EEG electrodes are used, the EMG data will
be derived as muscle electrical amplitude from signal frequency
response range of (typically 70 Hz to 150 Hz bandwidth).
[0465] The SSA outputs are utilized to determine the weighting
analysis and time of switching weighting analysis (STEP 23).
TABLE-US-00010 Sleep State Context **KEY for SSA (where sleep
stages can be 1, 2, 3, 4, REM, WAKE) ANALYSIS STATE TYPE CA1W >
S Change from WAKE to (sleep-stage 1 OR 2 OR 3 OR 4 OR REM) ref 34,
35, 45, 46 CA2W > S Change from sleep-stage 1 to (2 OR 3 OR 4 OR
REM) ref 34, 35, 45, 46 CA3W > S Change from sleep-stage 2 to (3
OR 4 OR REM) ref 34, 35, 45, 46 CA4W > S Change from sleep-stage
3 to (4 OR REM) ref 34, 35, 45, 46 CA5W > S Change from
sleep-stage 4 to REM) ref 34, 35, 45, 46 CA6S > W Change from
sleep-stage REM to (WAKE OR 1 OR 2 OR 3 OR 4) ref 34, 35, 45, 46
CA7S > W Change from sleep-stage 4 to (WAKE or 1 OR 2 OR 3) ref
34, 35, 45, 46 CA8S > W Change from sleep-stage 3 to (WAKE OR 1
OR 2) ref 34, 35, 45, 46 CA9S > W Change from sleep-stage 2 to
(WAKE OR 1) ref 34, 35, 45, 46 CA10S > W Change from sleep-stage
1 to WAKE ref 34, 35, 45, 46
[0466] For simplification and minimal electrode attachments to
patient Ai can be derived from existent EEG forehead (B or AEP)
electrodes.
Step 17--Sleep Stage Analysis (SSA) Display or Print
Step 18--Body Movement Index (BMi) Analysis
[0467] BM detection may be by way of analysis from a mattress
movement sensor device or other pressure or movement sensitive
sensors/electrodes attached to the patient. Detection of Body
Movement (BM) relates to a physical movement of the body such as
detected by a pressure or vibration sensitive sensors.
Step19--Body Movement Index (BMi) Analysis Display or Print
Step 20--Arousal Index (Ai) Analysis (35)
Step 21--Arousal Index (Ai) Analysis Display or Print
Step 22--Display Zone Transition Formula (DZTF)
Step 23--Set Analysis Arbitration, Weighting and Timing
[0468] This step defines the weighting ratios together with timing
of changes of the weighting ratios of AEPi and Bi for each of the
zones A, B, C D. for the ADMS Comprehensive & Integrated depth
of Anesthesia index (CIAi).
[0469] FIG. 5 shows a block diagram of an overview of analysis
associated with weighting index. The abbreviations TF and OS in
FIG. 5 are defined as follows. [0470] TF=Transfer Formula. The
transfer formula is designed to provide an adjustment or
normalization of index values in order to allow all analysis input
data to be comparable and allow cross-selection within the
Weighting Analysis Block without mismatching or obvious level
jumps, when switching between AEP, Bi or SSA analysis. [0471]
OS=Offset. The Offset is designed to provide an offset adjustment
between AEPi, Bi and SSAi in order to avoid level jumps when
switching between AEPI, Bi and SSA.
[0472] FIG. 6 shows a flow diagram associated with AEP:BIC analysis
weighting arbitration (Mode 1). The abbreviation S in FIG. 6
denotes a step.
[0473] The system of the present invention may allow the user to
readily upgrade the system's logic and accuracy with the course of
time and more advanced ADMS clinical data. The ADMS system may
include a self-learning capability to evaluate any selected group
of studies and via analysis of these studies allow ADMS system
weighting and analysis priorities to change in accordance with more
developed clinical data studies.
[0474] As detailed in the steps of FIG. 6 the ADMS system
highlights to the system user 4 main zones of interest, while
monitoring a patient under general anesthesia, as detailed in Table
9 below. The following codes are used in Table 9. TABLE-US-00011
Definition of Zones A, B, C, D CODE Zone Ranges Key Description
Zone A CU Patient emerging from Consciousness to Unconsciousness.
Zone B U Patient in unconscious state. Zone C U Patient in
unconscious state. Zone D UC Patient Transition from
Unconsciousness to Consciousness.
[0475] Table 9 presents examples of ADMS modes of operation. The
ADMS may provide a capability for weighting ratios to be changed or
programmed by ADMS system researchers or for a range of
pre-configured weighting ratios (MODES) to be selected.
TABLE-US-00012 TABLE 9 MODE 1 MODE 2 AEP Bi SSAi AEP Bi SSAi Zone
RATIO RATIO RATIO RATIO RATIO RATIO A 100 0 0 80 20 0 B 0 100 0 20
100 0 C 0 100 0 20 80 D 100 0 0 80 20 MODE 3 MODE 1 + N AEP Bi SSAi
AEP Bi SSAi Zone RATIO RATIO RATIO RATIO RATIO RATIO 100 0 0 B 0 50
50 C 0 50 50 D 100 0 0 NOTE: The range of MODES may be selected in
accordance with patient or medical procedure related factors. For
simplicity a simple MODE 1 configuration is presented as an example
of an ADMS embodiment. N + 1 Mode represents a large library of
Modes which may be selected or programmed into the ADMS system.
Step 24--Scaling and Transition Function
[0476] The scaling/range and transition functions are designed to
provide a method of scaling inputs to the CIAi to minimise
confusion or error associated with ADMS operation. In particular
this confusion or error can occur if the two scales and ranges of
BICi and AEPi (for example) are not compatible, or in a data format
suitable to be combined and displayed as a single CIAi.
[0477] Scaling and range of AEPi and BICi refers to a change or
adjustment of calculated values of AEPi and BICi (as detailed in
steps 14 and 12) respectively, to "match" the 2 separate indices so
that when weighting or switch changes occur, the CIAi does not have
a sudden jump or confusing change in value or scale
representation.
[0478] The switch transition function may adjust the time duration
over which any switch or weighting change occurs between (for
example) BICi and AEPi. Furthermore the transfer function applied
to each of the respective data inputs (BICi and AEPi, for example)
during this switch over duration or period may be selected from a
range or transfer functions. However, as with the scaling factor
the default transfer function will be X1 (linear).
[0479] The diagram shown in FIG. 7 presents a simplified overview
of scaling factor and transition curve functions associated with
the ADMS.
Step 25--Display Comprehensive Integrated ADMS Index (CIAi) MODE 1
CIAi basic Assumptions
[0480] 1. MODE 1 presents one of the simplest embodiments of the
ADMS. [0481] 2. Table 10 below summarizes the weighting factors for
zones A, B, C and D.
[0482] 3. The column entitled Display transition includes a column
titled display offset. This value is designed to minimize level
changes during the switching of AEPi: Bi weighting from 100:0 to
0:100. TABLE-US-00013 TABLE 10 CIA Display Transfer CIAi Display
AEP:Bi Translation function Zone ratio formula IDO (offset) IDO A
100:0 X1 0 0 B 0:100 X1 -(76 - 65) -11 C 0:100 X! -(76 - 65) -11 D
100:0 X! (74 - 50) - 11 13 NOTE: 1 Offset code IDOA AEPi IDOB
Empirical Data Offset applied for zone B = -(Bi-AEPi); for values
end of first consciousness period (ref 1, FIG. 5). IDOC Empirical
Data Offset applied for zone C = -(Bi-AEPi); for values at end of
first consciousness period (ref 1, FIG. 5). IDOD Empirical Data
Offset applied for zone D = (Bi-AEPi; for values at start of second
consciousness period (ref 3, FIG. 5))-IDOC NOTE 2: Values at end
and start of conscious periods (TCU and TUC respectively) and TSW
(ref3, FIG. 5) are set out below.
[0483] TABLE-US-00014 TABLE 11 Transition AEPi Value Bi Value TCU
65 76 TSW 36 40 TUC 50 74
[0484] TABLE-US-00015 TABLE 12 Time DZ AEPi Bi Ai BMi DZTF AEPi:Bi
CIAi t0 = start Display ** ** * * assume= Ratio *** t10 = end Zone
X 1 ref: 1 See Ref. 1 Ref. 1 ref: Step 22 See See Step 8 Step 23
Step 24 COL 1 COL 2 COL 3 COL 4 COL 5 COL 6 COL 7 COL 8 COL 9 t1 A
77 85 81 79 (X1) 100:0 77 t2 A 76 90 73 75 (X1) 100:0 76 IDC(mean)
75 90 70 65 (X1) 100:0 75 IDTCU A 65 76 80 64 (X1) 100:0 65
IDU(mean) 37 49 43 45 (X 1) - 11 0:100 38 t3 B 35 42 35 38 (X 1) -
11 0:100 31 t4 B 34 41 36 36 (X 1) - 11 0:100 30 t5 C 35 38 36 37
(X 1) - 11 0:100 27 IDTSW 36 40 38 37 (X 1) - 11 0:100 29 t6 C 40
52 41 38 (X 1) - 11 0:100 41 t7 C 40 62 42 39 (X 1) - 11 0:100 51
t8 C 39 71 43 40 (X 1) - 11 0:100 60 IDTUC 50 74 40 38 (X1) + 13
0:100 87 t9 D 60 75 50 60 (X1) + 13 100:0 88 t10 D 77 80 77 75 (X1)
+ 13 100:0 93 * Data presented for sample only ** The ID values
present some ambiguity, particularly in relation to IDTCU, IDC
(mean), IDU (mean), IDTSW and IDTUC values. However the selected ID
values are designed for update with clinical data studies,
currently in progress (Reference 3). *** CIAi formula; (AEPi
(column 3) .times. AEPi ratio (column 8 AEPi ratio value)) +
(cont'd) (Bi (column 4) .times. Bi ratio (column 8 Bi ratio value))
+ DZTF (Column 7) = CIAi Note: AEPI/Bi or Bi/AEPi numerator and
denominator are taken from respective AEPi and Bi ratio values per
column 8 in Table 12.
[0485] FIG. 8 shows a graphical representation of CIAi,TCU &
TUC values; [0486] FIG. 9 shows a graphical representation of
absolute value of Ai+Bi, TCU & TUC; [0487] FIG. 10 shows a
graphical representation of AEPi; [0488] FIG. 11 shows a graphical
representation of Bi; [0489] FIG. 12 shows a graphical
representation of Ai; [0490] FIG. 13 shows a graphical
representation of BMi; [0491] FIG. 14 shows a graphical
representation of Bi with the colour of the background changing to
indicate transition of consciousness state.
[0492] NOTE 1. Sleep Staging Analysis Step 16 and Analysis
Weighting Step 23 are simplest embodiments as operated in MODE 1.
However, the current state of clinical data (ref 11) provides only
a slight correlation between bispectral values of EEG and
conventional sleep staging. More advanced embodiments of the ADMS
will provide greater definition and specifications in relation to
spectral based sleep analysis (ref 3, 8, 9, 11). These further
MODES will, in particular, deploy modified frequency distribution
as opposed to the conventional frequency and amplitude analysis for
sleep stage definition.
[0493] NOTE 2: Values at end and start of conscious periods (TCU
and TUC respectively) and TSW (ref3, FIG. 5) are set out below.
TABLE-US-00016 TABLE 13 Transition AEPi Value Bi Value TCU 65 76
TSW 36 40 TUC 50 74
[0494] TABLE-US-00017 TABLE 14 Time DZ AEPi Bi Ai BMi DZTF AEPi:Bi
t0 = start Display * * assume= Ratio t10 = end Zone X 1 ref: 1 ref:
Step ref: Step ref: Step 14 ref: Step 22 ref: Step 23 t1 A 76 85 81
79 1 100:0 IDTCU-60 A 75 82 80 79 1 100:0 t2 A 75 90 73 75 1 100:0
IDC(mean) 75 90 60 65 1 100:0 IDU(mean) 40 49 43 45 1 0:100 t3 B 35
42 35 38 1 0:100 t4 B 31 41 36 36 1 0:100 t5 C 35 38 36 37 1 0:100
IDTSW 35 38 38 37 0:100 IDTUC 40 44 40 38 0:100 t6 C 40 52 41 38 1
0:100
[0495] FIG. 15 shows a flow chart of an improved system for
monitoring indices associated with human consciousness and
incorporating artifact rejection.
[0496] FIG. 16 shows a simplified functional system overview (FSO)
of a preferred embodiment of apparatus according to the present
invention. The apparatus of FIG. 16 is a Monitoring and Diagnostic
System incorporating a reduced risk Depth of Anaesthesia Analysis
and Monitoring System, including Minimal Sensor-Electrode
attachments for Consciousness, Audio Sensory,
Movement/Arousal/Muscle Activity, Eye Movement/Opening,
Stress/Anxiety/Vital Signs Parameters, and Audio-Visual Recall.
[0497] FIG. 17 shows a more detailed functional system overview
(MDFSO) of a preferred embodiment of apparatus according to the
present invention. The apparatus of FIG. 17 is a Depth of
Anaesthesia Analysis and Monitoring System, incorporating an
extended range of Sensor-Electrode attachments for Consciousness,
Audio Sensory, Movement/Arousal/Muscle Activity, Eye
Movement/Opening, Stress/Anxiety/Vital Signs Parameters, and
Audio-Visual Recall, audio, video, PTT, activity sensor, blood
pressure, oximeter, body and head wireless electrode modules.
[0498] Referring to FIG. 16, the apparatus of the HCM system
includes a electrode-sensor system (Block 1) connected to a signal
conditioning and data acquisition system (Block 4), an analysis and
monitoring system (Block 3) and a user display and optional touch
screen operator interface system (Block 2). Block 5 provides means
for time stamped video and audio to be recorded.
[0499] General Overview of Human Consciousness Monitoring System
Incorporates drawing FIGS. 16, 17, 35, 34, 43.
[0500] Block 1 in FIG. 16 presents that sensors and electrodes are
connected to the patient body by means of a unique integrated
electrode system (refer FIG. 35). The latter provides use of
wireless electrode systems and special self-adhesive electrode
attachment systems to achieve a minimally invasive and simple
tangle-free patient connection system, desirable for anaesthesia
application.
[0501] EEG electrodes are for simultaneously monitoring EEG
physiological data for optimised bi-spectral and optimised
Sleep/Wake analysis, EOG electrodes are for Sleep/Wake analysis,
Audio Sensory Electrodes are for monitoring auditory evoked
potential from a patient's auditory sensory nerve (refer Block 11
in FIG. 18), Reference Electrodes are for reference of
electro-physiological signals, Chin EMG electrodes are for arousal
and sleep/wake analysis and redundant or backup electrodes can be
applied with various embodiments (refer FIGS. 35, 37, 34, 17).
Audio stimulation can optionally be applied by means of a wireless
linked patient earpiece, to minimise wiring.
[0502] The apparatus may be configured by the user for different
modes of operation and furthermore is designed in a modular fashion
to allow varying degrees of complexity and versatility. The most
complex version of the system is configured to accommodate
monitoring and analysis of a broad range of physiological
parameters, as detailed above, while more basic versions can be
configured to accommodate critical parameters such as "sleep-wake"
analysis (34,45,46), bi-coherence analysis, audio evoked potential
and arousal analysis. "Sleep-wake" analysis, for example, may be
applied to optimise appropriate weighting between audio evoked
potential and bi-coherence analysis in determining a subject's
consciousness.
[0503] Electrode high-impedance amplifiers, signal conditioning and
audiovisual monitoring and recording functions (refer Blocks 2, 3
and 4 in FIG. 16) are provided by devices such as Compumedics
Siesta, E-Series and Profusion software (71,72,73). The
aforementioned devices are supplemented with specialized sensors
(refer Block 1 in FIG. 16) such as addition of minimally invasive
wireless and integrated function electrode and sensor systems
(refer FIGS. 35, 34). Time synchronized audio-visual capability of
the apparatus (refer Block 5 in FIG. 16) is further detailed in
FIG. 43.
[0504] Basic electrode amplification requires medical grade
isolation, with special additional input circuitry for
electrosurgery protection and RF input filtering for protection
against extreme conditions of voltage as may occur with
defibrillation procedures which are possible in a critical
monitoring environment of an operating theatre, being the likely
application environment for the apparatus.
[0505] Overview of Types of Physiological Sensory Monitoring
parameters and analysis for depth of anaesthesia application and
usefulness. The apparatus provides electrode-sensor attachment
capability to a patient and includes a capability, with use of
integrated and wireless electrodes (refer FIGS. 33, 34, 35, 37) to
provide a comprehensive assessment of a patient's physiological
states via monitoring and analysis of the patient's critical
sensory systems (critical includes avoiding incidence of recall or
premature anaesthesia awakening), while the patient is undergoing
anaesthesia drug delivery. Comprehensive assessment of human
sensory systems includes consciousness (bi-coherence &
Sleep/wake. Audio sensory (AEP analysis), arousal sensory (arousal,
micro-arousal and movement states), eye opening (special EOS),
anxiety & stress state and vital signs (Blood pressure,
temperature, GSR, HR and oxygen saturation). Furthermore the
apparatus provides a means of recording patient and operating
environment audio and video with time synchronisation link to
patient physiological parameters, thus providing evidence for legal
implications such as claims made relating to premature depth of
anaesthesia wakening or for physiological recall purposes.
[0506] Block 5 in FIG. 16 presents that audio and video can be
recorded in time synchronisation with the depth of anaesthesia
monitoring procedure, providing an important evidence record. This
may be particularly important for verifying audio recall or other
type of claims by subjects undergoing depth of anaesthesia
monitoring.
[0507] FIG. 18 shows a main flow diagram (MFD) of the HCM system
according to a preferred embodiment of the present invention.
Patient physiological parameters are signal conditioned and
digitised in Block 4 of FIG. 16. The digitised signals are read or
buffered in Block 3 of FIG. 18. Data is stored in Block 3 of FIG.
18 in buffer sizes based on filter and analysis requirements. Data
from Block 3 is applied to Digital Filtering in Block 40. Block 40
provides filtering for various physiological data channels. Block
40 is also linked to signal validation Block 7 to provide a means
of compensating for poor signal conditions, such as excessive mains
interference noise, which may require notch filtering at 50 or 60
Hz depending on the mains frequency in the country of operation of
the apparatus.
[0508] Data from Block 40 is also linked to Analysis Format Block 8
to provide specialised filtering where signals required for
analysis may need to be substituted by selected alternative
signals. This could occur, for example, where Sleep/Wake analysis
is required and no C3 electrode scalp signal is available but outer
malbar forehead signals may instead need to be optimised with
digital filtering to provide the closest possible emulation of C3
EEG signal format.
[0509] Filtered signals from Block 40 are validated in Block 7,
where each signal is characterised and checked for a range of
potential errors, artefact and corruption. The validation of each
signal allows the HCM system to present a signal validation score
for each signal, so that the user can be prompted when erroneous or
unreliable signals could adversely affect the systems output state
determination.
[0510] This type of method provides an early warning and error
reduction for critical monitoring and analysis in a depth of
anaesthesia system, which otherwise could be more vulnerable to
ambiguous outcomes of patient state determination.
[0511] The following provides a more detailed overview of types of
physiological parameters, weighting and data translation and
combining of analysed parameters for presentation of Integrated or
Combined Index to provide desired functional output and achieve
useful application of the HCM system and useful apparatus for depth
of anaesthesia monitoring.
[0512] The following section details how the apparatus has a
capability to take sensory physiological parameters including
consciousness, audio, arousal-movement, eye movement-opening,
stress-anxiety and vital signs parameters, and apply weighting and
combining techniques to these parameters to provide a user friendly
and risk minimised depth of consciousness monitoring and analysis
device. For ease of presentation this overview will proceed in the
order of physiological parameters set out above.
[0513] The apparatus is capable of monitoring
electroencephalographic physiological parameters to provide
neurological based analysis for optimised bi-spectral analysis
patient state and optimised R&K sleep-wake patient states. The
physiological parameters for bi-spectrum values are the outer
malbar EEG electrode connections to the patient's forehead together
with A1 or A2 EEG mastoid reference connections.
[0514] EEG signals are analysed in Block 10 of FIG. 18 whereupon
the bi-spectrum, bi-coherence and real triple products are
derived.
[0515] In accordance with empirical clinical data results
(initially set with factory default values) the weighting for
column 1 of table DCTT presents bands of bi-spectrum values between
value 0 and 100, where the bi-spectrum values refer to between
above mentioned bi-coherence and triple product and bi-spectral
index together with empirical clinical data results calculate and
determine these 0 to 100 values.
[0516] Column 2 of table DCTT presents Consciousness To
Unconsciousness Transition Thresholds (CTUT) Negative Slope, for
the BIC or bi-spectrum values critical threshold values of the
bi-spectrum value normalised between values of 1 to 100
(bi-spectrum value is determined from bi-coherence, triple product
and optimisation of these parameters with empirical data
results).
[0517] Column 3 of table DCTT presents Unconsciousness To
Consciousness Transition Thresholds (UCTCT) Negative Slope, for the
BIC or bi-spectrum values critical threshold values of the
bi-spectrum value normalised between values of 1 to 100 (where the
bi-spectrum value is determined from bi-coherence, triple product
and optimisation of these parameters with empirical data results).
An object of the HCM system is to distinguish between the
transition of consciousness to unconsciousness and visa versa and
to apply critical threshold detection and weighting values to the
analysis data in accordance with the transition. In this way the
apparatus optimises visual display tracking of a subject's depth of
anaesthesia to reduce risk of interpretation of state determination
of a monitored patient. "Positive" and "negative" as used in this
context has similar meaning throughout this document.
[0518] Column 4 of table DCTT presents the weighting values which
are applied to optimised bi-spectral analysis (0-100 normalised
values) to amplify the critical area of the display graph for
bi-spectrum display and also to achieve a visual affect so that all
sensory displays (consciousness, audio, arousal-movement, eye
movement-opening, stress-anxiety and vital signs) appear to be
visually aligned so that when all sensory and combined sensory
index's are operating with optimal zone system the user has a
simple visual alignment of various graph displays. These weighting
factors are indicated as sample factory default values, but this is
only indicative as the means of system to weight these parameters
is achieved by allowing the apparatus to be modified and upgraded
by various techniques including any form of network access, smart
card or other removable storage device or specially authorised user
system access and configuration.
[0519] Alignment of critical thresholds and optimal working area is
an object of the HCM system, as the user has a uncomplicated method
of ensuring that concentration and delivery of anaesthetic agent
does not cause the display metering of the depth of anaesthesia
monitor to move outside the optimal area of operation. Furthermore
the display graphs associated with each sensory parameter and the
combined index change colour to say, green when operating within
the optimal area and orange when operating outside the optimal
area, for example.
[0520] In a busy and stressful operating theatre these operational
and user aspects may make a substantial difference to the
useability of the apparatus. The apparatus may improve accurate
assessment of rate of and concentration of, anaesthetic drug
administration during depth of anaesthesia monitoring.
[0521] Column 5 of table DCTT presents Unconsciousness To
Consciousness Transition Thresholds (UCTCT) Positive Slope, for the
BIC or bi-spectrum values critical threshold values of the
bi-spectrum value normalised between values of 1 to 100 (where
bi-spectrum value is determined from bi-coherence, triple product
and optimisation of these parameters with empirical data
results).
[0522] An example of bi-spectrum values and weighting in accordance
with the above detailed formats and processing are presented in
table DCTT column 6 (sample bi-spectrum data), column 7 (weighting
or translation values applied to the bi-spectrum values), column 8
(un-normalised bi-spectrum values) and column 9 (bi-spectrum values
normalised between 1 and 100).
[0523] In the system's minimum and preferred configuration (for
reasons of simplicity) a single pair of EEG electrodes attached to
a subject's forehead (A1, A2 outer malbar bone skin surface
positions) is monitored and analysed to produce a bi-spectral index
(derivation of bi-coherence analysis) and also subjected to
spectral analysis with artefact rejection techniques to produce an
estimation of sleep state based on R&K rules but with
compromised signal locations. Compromised electrode locations refer
to applying forehead A1 and A2 outer malbar electrode positions as
opposed to the clinical standard. (refer Principles and Practice of
Sleep Medicine--Kryger Roth and Dement Roth) instead of the typical
A3 (requires specialised scalp electrode application).
[0524] The apparatus has a capability to present reports and
analysis display and reports in a simple condensed tabular or
graphic form, or more detailed reports and displays detailing raw
or basic physiological data. In this way expedient and effective
validation of condensed raw data results is accessible to the user.
Furthermore graphic and condensed display graphs provide a means of
combining or integrating various combinations of consciousness
input monitoring variables (including one or more of the various
sensory monitored inputs). In this way the user has a capability of
combining sets of consciousness index including for example
bi-spectral analysis combined with audio-evoked potential analysis,
arousal analysis combined with bi-spectral and audio evoked
potential analysis, amongst other combinations of analysis and
subsequent index measures.
[0525] Note 1: Any combination of 1, 2, 3, 4 and 5 can be utilized
for display purposes.
[0526] Note 2: 1, 2, 3 4, 5 and 6 represent analysis outputs for
BIC, AEP, Arousal, Eye opening and movement, anxiety and
sleep-unconsciousness/wake-consciousness respectively.
[0527] Note 3: A, B, C, D, E represents analysis data after
critical threshold detection, Display data translation and display
normalization. TABLE-US-00018 BLOCK 7 - FIG. 18 SIGNAL VALIDATION
Raw Data Frequency Actual Actual set Value Pass band Signal Range
High Pass Impedance Impedance a) Impedance Distortion Signal Group
Electrode (MilliVolts) Low Pass Measure Weight Normalised Measure
Channel Type Type Placement or per unit (Hz) Value Factor 1-10
Value Value SIGNAL CONFIGURATION AND TABLE REFERENCES 1 EEG R&K
C3 0-.300 0.3-30 1 Imped-1 2 EOG R&K Left eye 0-.300 0.3-30 1
Imped-1 3 EOG R&K Right eye 0-.300 0.3-30 1 Imped-1 4 EMG
R&K subment 0-.260 0.3-30 1 Imped-1 5 EMG R&K selectEMG
0-.260 0.3-30 1 Imped-1 6 EEG BIC Fp1 0-.300 0.3-30 1 Imped-1 7 EEG
BIC Fp2 0-.300 0.3-30 1 Imped-1 8 EEG BIC Fpz 0-.300 0.3-30 1
Imped-1 9 EEG AEP Mastoid+ 0-.300 70-260 1 Imped-1 10 EEG AEP
mld-foreh- 0-.300 70-260 1 Imped-1 11 EMG EP L-EP+ 0-.260 70-260 1
Imped-1 12 EMG EP L-EP- 0-.260 70-260 1 Imped-1 13 EYE TRK EYE-LID
+ 0-500 .01-15 1 Peizo-1 14 EYE TRK EYE-LID - 0-500 .01-15 1
Peizo-1 15 ECG Vital-Signs 0-5 .03-30 1 Imped-1 16 Sa02-HR
Vital-Signs BPM NA SAO2-1 17 Sa02 Vital-Signs 0-100% NA SAO2-1 18
SAO2-PTT Vital-Signs arous/min NA SAO2-1 19 BloodPres Vital-Signs
0-300 mmHg NA Distortion b) Distortion DC-Offset DC-Offset c)
DC-Offset Dc Stability Dc Stability d) Dc Stability Signal Weight
Normalised Measure Weight Normalised Measure Weight Normalised
Channel Type Factor 1-10 Value Value Factor 1-10 Value Value Factor
1-10 Value 1 EEG Distn-1 DC-Offset 1 1 0-10 DC-Stab1 2 EOG Distn-1
DC-Offset 1 0-10 DC-Stab1 3 EOG Distn-1 DC-Offset 1 0-10 DC-Stab1 4
EMG Distn-1 DC-Offset 1 0-10 DC-Stab1 5 EMG Distn-1 DC-Offset 1
0-10 DC-Stab1 6 EEG Distn-1 DC-Offset 1 0-10 DC-Stab1 7 EEG Distn-1
DC-Offset 1 0-10 DC-Stab1 8 EEG Distn-1 DC-Offset 1 0-10 DC-Stab1 9
EEG Distn-1 DC-Offset 1 0-10 DC-Stab1 10 EEG Distn-1 DC-Offset 1
0-10 DC-Stab1 11 EMG Distn-1 DC-Offset 1 0-10 DC-Stab1 12 EMG
Distn-1 DC-Offset 1 0-10 DC-Stab1 13 EYE TRK NA NA 0-10 NA 14 EYE
TRK NA NA 0-10 NA 15 ECG NA NA 0-10 NA 16 Sa02-HR 17 Sa02 18
SAO2-PTT 19 BloodPres Amp-headr Amp-headr e)Amp-headr Mains int.
Mains int. f)Mains int. Sig/Noise Sig/Noise Signal Measure Weight
Normalised Measure Weight Normalised Measure Weight Channel Type
Value Factor 1-10 Value Value Factor 1-10 Value Value Factor 1 EEG
Amp-Head1 Mains-Int1 S/N-1 2 EOG Amp-Head1 Mains-Int1 S/N-1 3 EOG
Amp-Head1 Mains-Int1 S/N-1 4 EMG Amp-Head1 Mains-Int1 S/N-1 5 EMG
Amp-Head1 Mains-Int1 S/N-1 6 EEG Amp-Head1 Mains-Int1 S/N-1 7 EEG
Amp-Head1 Mains-Int1 S/N-1 8 EEG Amp-Head1 Mains-Int1 S/N-1 9 EEG
Amp-Head1 Mains-Int1 S/N-1 10 EEG Amp-Head1 Mains-Int1 S/N-1 11 EMG
Amp-Head1 Mains-Int1 S/N-1 12 EMG Amp-Head1 Mains-Int1 S/N-1 13 EYE
TRK NA NA NA 14 EYE TRK NA NA NA 15 ECG NA NA NA 16 Sa02-HR 17 Sa02
18 SAO2-PTT 19 BloodPres Signal Validity NB 30 Sig/Noise Normalised
sample Channel Signal Type g) 1-10 Value Filters Actual Settings
Filters Recom. h) Filters Alarm formula 1 EEG Filt-1 2 EOG Filt-1 3
EOG Filt-1 4 EMG Filt-1 5 EMG Filt-1 6 EEG Filt-1 7 EEG Filt-1 8
EEG Filt-1 9 EEG Filt-1 10 EEG Filt-1 11 EMG Filt-1 12 EMG Filt-1
13 EYE TRK NA 14 EYE TRK NA 15 ECG NA 16 Sa02-HR 17 Sa02 18
Sa02-PTT 19 BloodPres NB 1 - Valid Impedance Table NB 2 - NA = Not
Applicable NB 3 - KEY a) Impedance normalised 1-10 value b)
Distortion normalised 1-10 value c) DC Off-set normalised 1-10
value d) DC stability normalised 1-10 value e) Amp-Headroom
normalised 1-10 value f) Mains Interference normalised 1-10 value
g) Signal to Noise normalised 1-10 value NB 4 For current channel
mark as Valid if (a > A)&(b > B)&(c > C)&(d
> D)&(e > E)&(f > F)&(g > G) - see NB3
NB 4
[0528] For current channel mark as Valid if
(a>A)&(b>B)&(c>C)&(d>D)&(e>E)&(f>F)&(g>G)--see
NB3
[0529] The following tale is set-up in system configuration options
TABLE-US-00019 Valid Table Number Imped-1 Imped-1 Valid Table
Number Peizo-1 Valid Table Name Electro-Impedance Valid Table Name
Eye Track-Validate Signal EEG, EOG, EMG, ECG Signal Eye Track
Sensor Groups Electro Groups Eye Piezo Impedance Weighted Impedance
Weighted Value (K) Value Value (K) Value 1 to 10 4 100K-200K 3 10
to 15 3 201K-300K 2 15 to 25 2 >300K 1 >25 1 Valid Table
Number SAO2-1 Valid Table Number Distn-1 Valid Table Name SAO2
Valid Table Name Electro-Impedance Signal SaO2 Signal EEG, EOG,
EMG, ECG Groups SaO2 Groups Electro DC Weighted Distortion Weighted
Value (V) Value Value (%) Value 0-1 3 1 <1 or >1 1 2 3 >3
Valid Table Number DC-Offset 1 Valid Table Number DC-Stab1 Valid
Table Name DC-Offset Valid Table Name DC-stability Signal
Electrophysiological Signal Electrophysiological Groups Electro
Groups Electro DC Weighted DC Weighted Value (mV) Value Value (mV)
Value 1-100 mV 4 1-100 mV 1-200 mV 3 1-200 mV 200-300 mV 2 200-300
mV >300 mV 1 >300 mV Valid Table Number Amp-Head1 Valid Table
Number Mains-Int1 Valid Table Name Amp-Head1 Valid Table Name
Mains-Interference Signal Electrophysiological Signal
Electrophysiological Groups Electro Groups Electro DC Weighted DC
Weighted Value (mV) Value Value (dB) Value No-Clip 4 Bn <20
Clip+ 3 20-30 Clip- 2 30-40 Clip+&- 1 >40 1 Valid Table
Number S/N-1 Valid Table Number Filt-1 Valid Table Name Signal to
Noise Valid Table Name Filters Signal Electrophysiological Signal
Electrophysiological Groups Electro Groups Electro DC Weighted
Deviation from rec. Weighted Value (mV) Value Value (% Hz) Value
>40 4 HP >20 30-40 3 EEG amp HP 0-20 20-30 2 LP >20 <20
1 LP 0-20 Ref: 3.2 Analysis Weighting Table See Table 2
Output Complexity Levels 1,2, 3 and 4 Signal Validation
[0530] Provides a means for Automatic signal validation of a
subject's monitored variables by way of automatic impedance
measurement, frequency response, mains interference, signal to
noise and signal distortion characteristics as part of the analysis
algorithm for monitoring, detection or prediction of a subject's
state of consciousness, sedation or vigilance.
Patient Calibration
[0531] Provides a means for a patient's calibration data to be
utilised in analysis algorithm for monitoring, detection or
prediction of a subject's state of consciousness, sedation or
vigilance.
Analysis Validation
[0532] Provides a means for Automatic Analysis Adaptation linked to
signal validation. Where the analysis types are determined in
accordance to status and quality of patient signals being
monitored.
[0533] Automatic determination of available analysis processes by
way of validating input signal quality and activating analysis only
in accordance to validated signal sets associated with the
analysis.
[0534] Once analysis types have been activated, weighting
techniques are applied to apply optimal emphasis for each analysis
type. Furthermore various analysis types are combined to simplify
the display method of tracking, prediction or detection of
consciousness, sedation level or a subject's vigilance.
Analysis Format
[0535] Provides a means for Automatic Analysis format linked to
signals connected, such as in the case of sleep and wake analysis
where the analysis parameters applied will depend on the validated
signals. If, for example, only EEG outer malbar electrodes are
validated, then frequency optimised EEG outer malbar signals can be
utilised for analysis, as opposed to more complex analysis signal
combinations including EMG and EOG signals.
[0536] Furthermore, weighting associated with each analysis type
will depend on the complexity and signal types available for each
analysis type.
Analysis
[0537] Incorporates an integrated BIC and AEP algorithm, predicting
EEG amplitude, integration of frequency (95% spectral edge, FFT)
and, 1/2 period amplitude analysis.
[0538] By utilising sleep and wake state determination as a means
of context analysis to assist in determining which analysis method,
from 5 or more methods (auditory evoked potential (AEP) index (a
numerical index derived from the AEP), 95% spectral edge frequency
(SEF), median frequency (MF) and the coherence (CHI) and R&K
sleep staging) is most suitable for optimal accuracy off tracking
each phase of the human vigilance stages.
[0539] Provides a means for Localised Evoked potential analysis to
detect muscle or nerve response to incisions during localised or
gas delivered anaesthetic drug administration.
[0540] Provides a means for Eyelid tracking for vigilance
monitoring and detection with wireless electrode option. A further
option exists using self-applied electrodes where the electrodes
consist of a low cost disposable component and a more expensive
reusable component.
Patient Information
[0541] Provides a means for a patient's body Mass Index, age,
medical history and other relevant information to be utilised in an
analysis algorithm for monitoring, detection or prediction of a
subject's state of consciousness, sedation or vigilance.
BIC Vigilance Application
[0542] Provides a means for BIC analysis for vehicle and machine
operator vigilance detection with wireless electrode option. A
further option exists using self-applied electrodes where the said
electrodes consist of a low cost disposable component and a more
expensive reusable component. The said EEG monitoring can be by way
of self-applied wireless or headrest attached electrodes.
TABLE-US-00020 BLOCK 7, EXAMPLE EXAMPLE OF SIGNAL VALIDATION
PRESENTING LOGIC EXAMPLE, BEHIND DETERMINATION OF VALIDATION OR
RELIABILITY LEVEL OF VARIOUS SETS OF PHYSIOLOGICAL DATA STATES, FOR
PURPOSE OF R&K SLEEP-WAKE STATE DETERMINATION. THIS VALIDATION
LEVELK CAN BE DISLAYED FOR PURPOSE OF PROVIDING THE SYSTEM USER A
CONFIDENCE LEVEL OF ANALYSIS MONITORING AND DISPLAY. Example of
Sleep Staging Signals Validity and Weighting R&K Signals
H-High, L-Low, Weighting factor M-Medium L L L M L M L H L M L H M
M M H EEG-C3 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 EMG 0 0 1 1 0 0 1 1 0
0 1 1 0 0 1 1 EOG 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 BIC 0 0 0 0 0 0 0
0 1 1 1 1 1 1 1 1 Note: 1 = Signal Valid 0 = Signal not valid X =
do not care
[0543] TABLE-US-00021 BLOCK 8 - FIG. 18 ANALYSIS FORMAT EXAMPLE
PRESENTING EEG FORMAT ANALYSIS DETERMINATION IN PREPARATION FOR
SLEEP/WAKE ANALYSIS PER BLOCK 18. An Analysis format Validation
Flow Diagram is shown in FIG. 19 based on analysis confidence
level, signal combination and analysis crosscheck. Actual Actual
Actual Actual Actual Analysis Analysis Analysis Analysis Analysis
Options Options Options Options Options Level 1 Level 2 Level 1
Level 2 Level 3 Signal BIC BIC R&K R&K R&K Signal Group
Electrode Weight Weight Weight Weight Weight Channel Type Type
Placement Value = 10 Value = 5 Value = 10 Value = 5 Value = 3 1 EEG
R&K, NB1, 18 C3 YES YES YES YES 2 EOG R&K, NB18 Left eye
YES YES 3 EOG R&K, NB18 Right eye YES YES 4 EMG R&K, NB18
subment YES 5 EMG R&K, NB18 selectEMG YES 6 EEG BIC, NB18 Fp1
YES YES 7 EEG BIC, NB1, 18 Fp2 YES YES 8 EEG BIC, NB1, 18 Fpz YES
YES YES 9 EEG AEP Mastoid+ 10 EEG AEP mid-foreh- 11 EMG EP L-EP+ 13
EMG EP L-EP- 14 EYE TRK EYE-LID + 15 EYE TRK EYE-LID - 16 ECG
Vital-Signs 17 SAO2-HR Vital-Signs 18 SAO2 Vital-Signs 19 SAO2-PTT
Vital-Signs 20 BloodPres Vital-Signs Actual Actual Actual Actual
Actual Analysis Analysis Analysis Analysis Analysis Options Options
Options Options Options Level 1 Level 2 Level 3 Level 4 Level 5
Signal Arousal Arousal Arousal Arousal Arousal Signal Group
Electrode Weight Weight Weight Weight Weight Channel Type Type
Placement Value = 10 Value = 9 Value = 8 Value = 7 Value = 6 1 EEG
R&K, NB1, 18 C3 YES YES YES YES 2 EOG R&K, NB18 Left eye
YES 3 EOG R&K, NB18 Right eye YES 4 EMG R&K, NB18 subment
YES YES YES YES 5 EMG R&K, NB18 selectEMG YES YES YES 6 EEG
BIC, NB18 Fp1 YES YES 7 EEG BIC, NB1, 18 Fp2 YES YES 8 EEG BIC,
NB1, 18 Fpz YES 9 EEG AEP Mastoid+ YES 10 EEG AEP mid-foreh- YES 11
EMG EP L-EP+ YES 12 EMG EP L-EP- YES 13 EYE TRK EYE-LID + YES 14
EYE TRK EYE-LID - YES 15 ECG Vital-Signs 16 SAO2-HR Vital-Signs 17
SAO2 Vital-Signs 18 SAO2-PTT Vital-Signs 19 BloodPres
Vital-Signs
[0544] TABLE-US-00022 BLOCK 9 (FIG. 18) ANALYSIS SUMMARY DATA
INSERT INDEPTHANESTH, BLOCK 9 Analysis Signal Priority Analysis
Summary Data Validity (Reference: Signal User Input Valid or
Analysis Analysis Analysis Algorithm Signal Group Electrode Select
Invalid Format& Interface Algorithm Algorithm Period Channel
Type Type Placement On/Off See SigVal Priority Version Type &
Version Period Type NB1 NB 1 NB 3 NB 4 NB 5 NB 6 NB 7 NB 8 SIGNAL
CONFIGURATION AND TABLE REFERENCES 1 EEG R&K, NB1, 18 C3 2 EOG
R&K, NB18 Left eye 3 EOG R&K, NB18 Right eye 4 EMG R&K,
NB18 subment 5 EMG R&K, NB18 selectEMG 6 EEG BIC, NB18 Fp1 7
EEG BIC, NB1, 18 Fp2 8 EEG BIC, NB1, 18 Fpz 9 EEG AEP Mastoid+ 10
EEG AEP mid-foreh- 11 EMG EP L-EP+ 12 EMG EP L-EP- 13 EYE TRK
EYE-LID + NA 14 EYE TRK EYE-LID - NA 15 ECG Vital-Signs NA 16
SAO2-HR Vital-Signs 17 SAO2 Vital-Signs 18 SAO2-PTT Vital-Signs 19
BloodPres Vital-Signs NA Analysis Analysis Analysis Inputs,
Analysis Analysis Analysis Reference Validity Signal Outputs, Index
Index Calibrate Analysis Analysis Weight Weighted Analysis Channel
Type Conditions Unit Measure Reference Patient Data State table
Value Depth NB 9 NB 10 NB 11 NB 12 NB 13 NB 14 NB 15 NB 16 NB 17
SIGNAL CONFIGURATION AND TABLE REFERENCES 1 EEG 2 EOG 3 EOG 4 EMG 5
EMG 6 EEG 7 EEG 8 EEG 9 EEG 10 EEG 11 EMG 12 EMG 13 EYE TRK 14 EYE
TRK 15 ECG 16 SAO2-HR 17 SAO2 18 SAO2-PTT 19 BloodPres
NB 1
[0545] These channels can be referenced for 95% edge analysis
and/or 1/2 period amplitude analysis for purpose of validating
neurological hypnosis, wake or sleep state. The following table is
set-up in system configuration options.
NB 2
[0546] User select on/off-user can configure which input channels
are selected
NB 3
[0547] Signal validity (valid or invalid) Signal validity table
determines whether the signal status is valid or invalid. Analysis
format Validation Flow Diagram shows an example of how the selected
channels and processing format.
NB 4
[0548] Analysis priority is determined by combination of input
signals and validity of input signals. See diagram: Analysis format
Validation Flow Diagram, which details example low diagrams
detailing selection of appropriate analysis, subject to input
signal type and signal validation.
NB 5
[0549] Analysis Interface version is necessary to ensue that the
analysis type and version is compatible with analysis algorithm
interface.
NB 6
[0550] Analysis algorithm type and version. Each analysis algorithm
is interfaced to main program by way of a standard analysis
interface, which can be in the form of a DLL or other defined and
standard interface method. This function provides a means of
configuring, updating and convenient definition and display of a
system's analysis status and configuration.
NB 7
Algorithm Period
[0551] 1 sec [0552] 10 secs [0553] 30 secs [0554] 1 min [0555] 2
min [0556] 5 min [0557] 10 min [0558] 20 min [0559] 30 min [0560]
40 min [0561] 60 min NB 8 Algorithm Period Type Options Average
Over Past Period Running Average Over Period Running Average Since
Start NB 9
[0562] Analysis inputs, outputs and conditions describe standard
variables associated with interface between analysis algorithms and
main program analysis interface.
NB 10
[0563] Analysis index units refer to measure associated with Index,
such as respiratory events per hour for RDI.
NB 11
[0564] Analysis index measure refers to name of specific
index-example is RDI or Respiratory Disturbance Index.
NB 12
[0565] Analysis calibrate reference refers to calibration data
which was compiled from measurements associated with a specific
patient. This data could be, for example, normal wake and/or sleep
EEG bi-coherence reference data measured as part of a preparatory
study to assist more accurate depth of anaesthesia monitoring
during a patient's operation.
NB 13
[0566] Analysis patient data refers to special patient data such as
Body Mass Index (BMI), patient age and patient sex, which can
affect the amount of anaesthetic drug required for a particular
patient.
NB 14
[0567] Analysis state refers to the state of analysis such as wake,
sleep, conscious or unconscious.
NB 15
[0568] Analysis reference weight table refers to specific table
referenced for purpose of allocating correct analysis weighted
value.
NB 16
[0569] Analysis weighted value refers to value assigned for current
analysis output
NB 17
[0570] Analysis Depth refers to the degree or depth of the
analysis, where 1 represents conscious or wake state and 10
represent greatest depth of unconsciousness. In other words we
could have an analysis depth of say 8 (see NB 17) for BIC analysis
state and weighted value (see NB 16) of 7 (for example only). In
this example the weighted value is determined by the signal
validity associated with-- [0571] a) Signal quality associated with
BIC signals [0572] b) Analysis priority associated with BIC signals
[0573] c) Analysis probability and consolidation NB 18
[0574] Arousal detection can also be detected from this channel by
way of frequency shift detection.
[0575] FIG. 20A shows a flow diagram of computation of bicoherence,
real triple product and bispectral index in Block10 of FIG. 18.
Computation of Bispectrum (B), Bicoherence and Real Triple Product
B .function. ( f1f2 ) = I = 1 L .times. .times. Xi .function. ( f1
) .times. Xi .function. ( f2 ) .times. Xi .times. * .times. ( f1 +
f2 ) ##EQU4## [0576] Epoch length=30 seconds [0577] 75% overlap of
epochs to reduce variance of bi-spectral estimate [0578] L=epochs,
i.e. 1 minute of data [0579] f1&f2 are frequency components in
the FFT such that f1+f2<fs/2 where fs is the sampling frequency
Real Triple Product (RTP) RTP .function. ( * .times. f1f2 ) = I = 1
L .times. .times. Pi .function. ( f1 ) .times. Pi .function. ( f2 )
.times. Pi .function. ( f1 + f2 ) Where .times. .times. Pi
.function. ( f1 ) .times. .times. IS .times. .times. THE .times.
.times. POWER .times. .times. SPECTRUM .times. P .function. ( F ) =
X .function. ( F ) 2 ##EQU5## Bi-coherence (BIC) BIC .function. (
f1f2 ) = 100 .times. B .function. ( f1f2 ) RTP .function. ( f1f2 )
##EQU6## ranging from 0 to 100%
[0580] FIG. 20B shows a graphical representation of bispectrum,
bicoherence and real triple product in Block 10 of FIG. 18.
Block 11--FIG. 18
Audio Evoked Potential Depth of Hypnosis Frequency Sensitivity
Analysis
[0581] FIG. 21A shows waveform trace1 representing a sample of
frequency sweep signals which can are applied to one or both of a
patient's ears.
[0582] FIG. 21B shows waveform trace 2 representing the frequency
sweep signal at a sensitivity lower than trace 1. FIG. 21C shows
one form of hardware for generating the signals shown in FIGS. 21A
and 21B. FIG. 21D shows one form of hardware for collecting AEP
sensory data from a subject.
[0583] FIG. 21E shows Waveform Trace 3 representing a sample of the
signal resulting from monitoring the ear sensory nerve when the
patient's ear is receiving signals such as trace 1 or trace 2. The
system has the capability of applying a range of frequencies at
various sensitivity levels to provide a gauge of the patient's
response to frequency and sensitivity variations whilst undergoing
anaesthesia. In this manner relatively complex audio performance
evaluation of a patient is possible. Detailed and precise
performance evaluation assists in obtaining an accurate measure of
critical thresholds (as determined by empirical clinical data for
varying patient ages and types). Furthermore more accurate
determination is possible by calibrating the system detection
(consciousness and unconsciousness) thresholds for a specific
patient. This may be achieved by measuring normal consciousness
values and in some circumstances values as the subject transitions
into sleep.
[0584] FIGS. 21F and 21G show graph 1 and graph 2 respectively
representing examples of AEP output results from measuring a
sequence of input signal amplitudes at selected sensitivities for a
range of frequency sweeps. By outputting the same sequence of
frequency sweeps but with varying sensitivities (eg. trace 1 and
trace 2) it is possible to graph the effect of the subjects hearing
during anaesthesia and provide an accurate assessment based on
deterioration of frequency response and sensitivity of the Audio
Evoked Potential signal, the likely critical points in the process
of anaesthesia (ie. the points where the patient is at low risk of
audio-recall while undergoing operation procedure).
[0585] The above provides an extremely sensitive performance
evaluation system for ear-related operations where monitoring of a
patient's audio sensory nerve function can be critical. The same
system may also be applied to comprehensive measurement and
evaluation of audio performance.
[0586] FIG. 21H shows graph 3 demonstrating a sample of varying
response curves expected from the AEP electrode output when
outputting to a patient a series of frequencies at different
sensitivities.
[0587] The same type of graphical curves are stored as part of the
reference block to determine various stages of a subjects monitored
anaesthesia--ie. thresholds for a patient in consciousness and
unconsciousness with low risk of audio recall. TABLE-US-00023 BLOCK
15 - FIG. 18 system output alarms, indicators and displays COMBINED
CONSCIOUSNESS-TRANSITION NEW INDEX WEIGHTED ANALYSIS Display Level
1 ##STR1##
[0588] FIG. 22A shows a bar graph of Context Analysis Method and
FIG. 22a shows the corresponding display validation status.
Validation status is represented by a colour coded bar display
wherein green indicates that the parameter is operating in an
optimal area, orange indicates that it is operating in a marginal
area outside the optimal area and red indicates that the parameter
is operating in an invalid or unreliable area.
[0589] FIG. 22B shows a bar graph of Context Analysis Probability
and FIG. 22b shows the corresponding display validation status.
FIG. 22C shows a bar graph of Transition Analysis Method and FIG.
22c shows the corresponding display validation status. FIG. 22D
shows a bar graph of Transition Analysis Probability and FIG. 22d
shows the corresponding display validation status. FIG. 22E shows a
bar graph of Movement Analysis Method and FIG. 22e shows the
corresponding display validation status. FIG. 22F shows a bar graph
of Movement Analysis Probability and FIG. 22f shows the
corresponding display validation status.
Block 15--FIG. 18
System Output Alarms, Indicators and Displays
[0590] Consciousness Index (derivation of BIC).
[0591] Transition Index (derivation of AEP and Arousal Index), with
cross-linked verification and feedback (transition state holding
precedent and over-riding priority over BIC derived Index).
[0592] FIGS. 23A to 23C show graphical representations of system
output alarms, indicators and displays associated with Block 15 in
FIG. 18. FIG. 23A shows a typical AEP and BIC display and report
output together with an integrated and weighted example display of
auto track AEP-BIC index wherein the colour of the display
indicates its value as set forth in the figure. FIG. 23B shows a
bar graph display of discrete sensory index wherein the colour of
the display indicates its validation status as set forth in the
figure. FIG. 23C shows a sample display screen associated with a
hospital in depth anaesthesia meter/hospital ward rest meter with
depth anaesthesia analysis embodiment.
Block 16--FIG. 18
Arousal Detection
[0593] FIG. 24 shows a flow diagram of arousal detection in Block
16 of FIG. 18.
Block 17--FIG. 18
Determination of Eyeopen Index (EOI)
[0594] Eye Opening Sensor Device (EOSD) outputs a unique voltage
level in response to each eye opening status. The Actual Eye
Opening Value (AEOV) is determined by detecting periods from the
subject's consecutive blinks and detecting a maximum value of eye
opening during these periods. This procedure excludes blinks and
effects of blinks, but rather extracts a maximum eye opening during
the period.
[0595] The Reference Eye Opening Wake Value (REOWV) can be
determined by instigating the systems REOWV calibration procedure.
This procedure records the Actual Eye Opening Value (AEOV) during a
designated period, say for example 60 seconds, and then determines
the average AEOV during this 60-second period. REOWV = total
.times. .times. addition .times. .times. of .times. .times. AEOV
.times. .times. for .times. calibration .times. .times. time
.times. - .times. ( 60 .times. .times. seconds ) total .times.
.times. number .times. .times. of .times. .times. AEOV ' .times. s
.times. .times. for .times. .times. calibration .times. .times.
time .times. .times. period ##EQU7##
[0596] The Percentage Eye Opening (PEO) value can be calculated by
dividing the Actual Eye Opening Value (AEOV) by the Reference Eye
Opening Wake Value (REOWV) and multiplying this value by 100 in
order to determine the PEO value. PEO=(AEOV/REOWV) X 100
[0597] Eye Opening Index (PEOI) is calculated with the following
formula 100 1 .times. Total .times. .times. addition .times.
.times. of .times. .times. AEOV .times. .times. for .times. .times.
1 .times. - .times. minute .times. .times. period Total .times.
.times. number .times. .times. of .times. .times. AEOV .times.
.times. during .times. .times. said .times. .times. 1 .times. -
.times. minute .times. .times. period .times. ##EQU8## [0598]
PEOI=Percentage Eye Opening Index [0599] AEOV=The Actual Eye
Opening Value [0600] REOWV=Reference Eye Opening Wake Value [0601]
AEOV=Actual Eye Opening Value [0602] PEO=Percentage Eye Opening
Determination of Eye Movement Index
[0603] The Eye Movement Index (EMI) is determined by detecting each
Eye Movement and using a running average formula determining the
EMI for the past time period t; EMI=Total number of EM for period 1
minute (last minute for running average calculation) wherein:
[0604] t=time period under measurement--this is typically running
time window and for the EMI can be typically 1 minute (ie.
representing EM's over the past 1 minute for EMI running average)
[0605] EM=Eye Movements. The eye movements are detected by way of
output from EOSD sensor and detecting for minimum period and
threshold values [0606] EMI=Eye Movement Index Block 21--FIG. 18
Sleep--Wake Analysis
[0607] Block 21 performs automatic recognition of sleep and wake
states.
[0608] FIG. 25 shows a flow diagram of the process of detecting
zero derivative time instants and elementary maximum segments -1 in
Block 21 of FIG. 18.
[0609] FIG. 26 shows a flow diagram of the process of detecting
zero derivative time instants and elementary minimum segments -1 in
Block 21 of FIG. 18.
Block 21--FIG. 18
Sleep-Wake Analysis and BIC EEG Artifact Removal
[0610] FIG. 27 shows a flow diagram of the process of sleep/wake
analysis and BIC EEG artefact removal in Block 21 of FIG. 18;
Blocks 23, 24, 25, 26, 27, 42, 29, 30, 31, 32, 33, 43 Display Range
Scaling and Samples of Display Output (Block 15) Display Range
Scaling
[0611] Display scaling is designed to provide the system end-user
with a simple and intuitive view of important analysis data whilst
monitoring a patient.
[0612] A Display Range and Display Translation table has been
designed from empirical data (derived from clinical studies) to
convert actual analysis data values to normalised or weighted
Display Unit Values (DUV). Accordingly, the Display Translation
table can distort or provide a non-linear translation across
various sections of display translation, to improve visual tracking
across critical regions of analysis data.
[0613] The Display Unit Values (DUV) are formulated to provide the
system user with a means to display the critical working range of
each measured variable in a convenient and user friendly
manner.
[0614] Furthermore, one or more Display Range Translation Tables
(DRTT) may be dynamically allocated to a single Display Unit. The
specific DRTT applied at any point in time to a DU can be
determined by the context of or change sequence associated with a
subject's hypnotic state. In this manner typically different slopes
or rates of change associated with a subjects measured variables
may be displayed to present a maximised and Auditory Evoked
Potential critical transition of a subject's state from
consciousness to unconsciousness and visa versa. Calculation of
Display Scale Range Calculation DVD = AV - MNS SR .times. 100
.times. .times. ( FULL .times. .times. SCALE .times. .times. RANGE
) ##EQU9## Variables: Display Range Transition Table (DRTT) [0615]
DSV=Display Screen View [0616] DU=Display Unit which is one meter
or trace that forms part of the Display Screen View [0617]
MXS=maximum Scale Value is the Actual input data minimum or
"cut-off" lower value displayed [0618] MNS=minimum Scale Value is
the Actual input data maximum or "cut-off" higher value displayed
[0619] DVD=Display Value Deflection [0620] SRAD=MXS-MNS [0621]
AV=Actual Value, or the value that is currently being displayed by
a DU. [0622] DR=Display Range. This can be, for example any value
between 1 to 100. [0623] DUV=Display Unit Values [0624] OWR=Optimal
Working Range BIC Function and AEP Typical Values
[0625] Example of Display Range Translation tables for BIC function
and AEP Indexes (note this is an example presenting 10 data points
translation but a complete table would present at least 100 data
points).
Display Transition
Step 1.
Define Critical Zones of Display
[0626] The critical zone of display represents the values, which
are desired to be displayed in such a manner that the user has an
expanded viewing range (on meter display, for example) compared to
less critical display zones. In the HCM system the ability exists
to define these "critical display zones" and in particular the
critical display zones can change subject to both the context of a
subjects current and past states of conscious/wake or
unconscious/sleep.
Step 2.
Define Critical Threshold Values.
[0627] These values are typically the following data points.
[0628] The following table defines the default critical values.
These default values can be changed or modified in accordance with
the user interface or different system configuration
requirements.
[0629] Display Critical Threshold Value and Display Transition for
BIC analysis (DCTT) TABLE-US-00024 TABLE DCTT Weighted BIC Display
1-100 BIC\CTUT BIC\UCTCT function normalised BIC critical BIC
critical Display Display Translation (weighted in values. CTUT
UCTCT transition transition value as accordance with (divide by
300/100 Thresholds Thresholds Factor- Factor- derived from CTUT and
UCTCT and rounded to BIC DATA Negative Positive Negative Positive
columns 4 translation values. nearest whole RANGE Slope Slope slope
slope and 5 above. (per column 4 & 5) unit) COLUMN 1 COLUMN 2
COLUMN 3 COLUMN 4 COLUMN 5 COLUMN 6 COLUMN 7 COLUMN 8 COLUMN 9 0-10
.5 1 T1-85-(pos slope) 3 255 75 11-20 .5 1 T2-90-(pos slope) 3 270
90 21-30 .5 1 T3-42-(pos slope) 1 42 14 31-40 DCTTW DCTTW 2 1
T4-41-(neg slope) 2 82 27 Threshold-35 Threshold-35 41-50 2 1
T5-38-(neg slope) 2 76 25 51-60 2 1 T6-52-(pos slope) 1 52 17 61-70
CTUT 2 3 T7-62-(pos slope) 3 186 62 Threshold-80 71-80 UTCT 2 3
T8-71-(pos slope) 3 213 71 Threshold-75 81-90 2 3 T9-75-(pos slope)
3 225 75 91-100 2 3 T10-80 (pos slope) 3 240 80
[0630] Display Critical Threshold Value and Display Transition for
AEP Analysis TABLE-US-00025 AEP critical AEP critical Display 1-100
CTUT UCTCT normalised Thresholds Thresholds AEP\CTUT Weighted AEP
values values. Negative Positive Display AEP\UCTUT Translation
(weighted in (divide by Slope Slope transition Display Typical AEP
value as accordance with 160/100 and +/-10%- +/-10%- Factor-
transition values for time derived from CTUT and UCTCT rounded to
AEP DATA see block see block Negative Factor-Positive sequence T1
to columns translation values. nearest whole RANGE 39.sup.1
39.sup.1 slope slope T10 4 and 5 above. (per column 4 & 5)
unit) COLUMN 1 COLUMN 2 COLUMN 3 COLUMN 4 COLUMN 5 COLUMN 6 COLUMN
7 COLUMN 8 COLUMN 9 0-10 1 1 T1-77-(neg slope) 2 154 96 11-20 1 1
T2-76-(neg slope) 2 152 95 21-30 DCTTW DCTTW 1 1 T3-37-(neg slope)
1 37 23 Threshold-25 Threshold-25 31-40 1 1 T4-35-(neg slope) 1 35
22 41-50 UTCT 2 2 T5-36-(pos slope) 1 36 23 Threshold-50 51-60 2 2
T6-40-(pos slope) 1 40 25 61-70 CTUT 2 2 T7-39-(neg slope) 1 39 24
Threshold-65 71-80 2 1 T8-38-(neg slope) 1 38 24 81-90 1 1
T9-60-(pos slope) 2 120 75 91-100 1 1 T10-75(pos slope) 1 75 47
Note 1 refer block 39 for details on selector logic for BIC and AEP
combined output.
[0631] FIG. 28 shows weighted and display normalized (1-00) BIC and
AEP data.
[0632] Above Example with combined BIC and AEP Display (refer
blocks 12, 14 and 34) [0633] Note that switching between BIC and
AEP is in accordance with Block 12 logic or; [0634] 1.
Consciousness (wake) to unconsciousness (sleep) state
transition--switch to BIC function [0635] 2. Unconsciousness
(sleep) to consciousness (wake) state transition--switch to AEP
value [0636] 3. During consciousness (wake) state--switch to AEP
value
[0637] 4. During unconsciousness (sleep) state--switch to BIC
function TABLE-US-00026 State Conscious Unconscious Weighted, Wake*
Normalised (1-100) sleep* and (example combined BIC indicates only
function and AEP consciousness Value and Weighted and Weighted and
Greater of values unconsciousness Normalised (1-100) Normalised
(1-100) from columns 3 Time period state). BIC function AEP Value
and 4. COLUMN 1 COLUMN 2 COLUMN 3 COLUMN 4 COLUMN 5 T1
consciousness 75 96 96 T2 consciousness 90 95 95 T3 unconsciousness
14 23 23 T4 unconsciousness 27 22 27 T5 unconsciousness 25 23 25 T6
unconsciousness 17 25 25 T7 unconsciousness 62 24 62 T8
unconsciousness 71 24 71 T9 consciousness 75 75 75 T10
consciousness 80 47 80
[0638] Sleep and wake states can include stage 1 sleep, stage 2
sleep, stage 3 sleep, stage 4 sleep, REM sleep, movement sleep,
arousal sleep and micro-arousal sleep subject to the HCM system's
application configuration and user's required sensitivity (ie.
system may be configured and selected for application as a sedation
or activity monitor for the aged or subjects undergoing drug
administration, in which cases the HCM system may be configured and
selected for full-sleep state sensitivity. Alternatively the HCM
system may be selected for vigilance monitoring with a jet pilot or
other transport driver or pilot steering a ship or other sea
vehicle and in this case electrode attachments to the subject may
be as minimal as a disposable wireless linked electrode for BIC
parameters. Accordingly only level and state of conscious or
unconsciousness may be required.
Sample of Combined AEP and BIC with Critical Threshold and Patient
State Display
[0639] FIG. 29 is a sample of combined and weighted BIC and AEP
data with critical threshold and patient state display.
Status and Critical Threshold Display--LAST 10, 20, 30, 40, 50, 60,
70, 80, 90, or 100 Epochs of 30 Seconds (Subject to User
Requirements and Application)
[0640] sample for above t1 to t10 period with basic main
states.
[0641] The data appearing to roll down in the format provides users
a clear graphic means of detecting the monitored subject's
progression of consciousness states, consciousness transitions and
critical thresholds. TABLE-US-00027 TIME EPOCH # CONSCIOUS CTUT
UNCONSCIOUS UTCT DCTTW 10:44:16 300 10:44:00 299 10:43:30 298
10:43:00 297 10:42:30 296 10:42:00 295 10:31:30 294 10:31:00 293
10:30:30 292 10:30:00 291
[0642] Sample for above t1 to t10 period can include basic main
states and sleep states (would be the same as above table with the
inclusion of states wake, stage 1 sleep, stage 2 sleep, stage 3
sleep, stage 4 sleep, REM sleep, movement sleep, arousal sleep and
micro-arousal sleep). Key; [0643] CTUT--Consciousness To
Unconsciousness Transition [0644] UTCT--Unconsciousness To
Consciousness Transition [0645] DCTTW--Deep Consciousness
Transition Threshold Warning [0646] CS--Conscious state [0647]
US--Unconscious State [0648] *Slope indicates that value is
measured in conjunction with increasing (positive slope) or
decreasing (negative slope). Step 3
[0649] Define the transition formula associated with each segment
or section of the display. Transition formula refers to a single
co-efficient (such as 0.5 or 2, for example) for the formula such
as log of input value. This transition formula defines the method
whereby different display sections are amplified, divided,
distorted, stretched. For a viewing perspective the display may be
contracted or expanded. The display transition may be important to
simplify verification of the subjects status, i.e. Index of BIC or
Index of AEP or Arousal Index. Using the application of display
transition method, the HCM system presents to the user a clear and
concise operation method whereupon each compliance or optimal
status of each parameter can be quickly and easily verified by
ensuring that the metered level falls within the optimal display
range. Furthermore, each critical parameter being measured (such as
Hypnosis Sensory-BIC Index, Auditory Sensory-AEP Index, Muscle
Sensory-Arousal Index, Visual Sensory-Eye Opening Index, Eye
Movement Sensory-Eye Movement Index-EOI) may be viewed across a
common optimal working and the display graphs can be colour coded
so that the user is given colour and positional information which
instantly verifies whether or not the subject's physiological
parameters are measured in the optimal zone or display area at any
point in time. With dangerous and critical drug administration the
ability to monitor a number of critical variables with simple and
accurate verification can avert an otherwise fatal or critical
situation for the subject under monitoring. For example, the system
user may be instructed to administer the anaesthesia drug while
ensuring that each sensory graph such as Hypnosis Sensory-BIC
Index, Auditory Sensory-AEP Index, Muscle Sensory-Arousal Index,
Visual Sensory-Eye Opening Index, Eye Movement Sensory-Eye Movement
Index-EOI or an Integrated Sensory Index (combined discrete sensory
Indexes) are within the optimal range (colour and position) during
drug administration.
[0650] In particular the current method may provide users a simple
and precise method of metering critical variables being analysed
for a subject undergoing administration of potentially dangerous
drugs such as drugs promoting anaesthesia.
BIC and AEP Index Typical Unweighted Data
[0651] FIG. 23A shows typical AEP and BIC index display together
with an integrated and weighted example display of auto track
AEP-BIC index.
BIC and AEP Index Typical Weighted Data With Expansion of Critical
Display Regions
[0652] FIG. 23B shows a discrete sensory index display example
including: [0653] HYPNOSIS (45) [0654] AUDITORY (78) [0655] MUSCLE
(44) [0656] EYE MOVE (76) [0657] EYE OPEN (50) [0658] INTEGRATED
AND WEIGHTED SENSORY EXAMPLE Step 4
[0659] Verify or modify Display Translation coefficients or
critical thresholds using empirical data derived from clinical
studies.
Block 29 FIG. 18
CSCA Data Translation Table (DTT) & Alarm Thresholds (AT) &
Level Normalisation (LN)
[0660] The translation tables provide a means to translate raw
analysis output data into a non-linear or linear manner. The
translated data is output in a form suitable for user display
viewing. The working or optimal value range for various analysis
functions can be transposed in order to fit the screen display and
resolution for ease of user system operation.
[0661] Important or critical threshold values, associated with
analysis data output provide a means for the system to
automatically generate alarm indicators or displays. For example,
transition from conscious to unconscious and transition from
unconscious are critical thresholds, which would be displayed as
critical status displays.
Block 35--FIG. 18
Weighting for Combined (1,2,3,4,5) Index
[0662] The analysis index from 1) CORTICAL SENSORY (EEG)
CONSCIOUSNESS ANALYSIS, 2) AUDITORY SENSORY TRANSITION ANALYSIS
(ASTA) AEP, 3) MUSCLE SENSORY AROUSAL ANALYSIS, 4) VISUAL SENSORY
ANALYSIS, 5) SLEEP/WAKE SENSORY ANALYSIS input and combined with a
formula to provide a single index designed to register the maximal
value of 1, 2, 3, 4 and 5 at any point in time;
[0663] Select output value to maximal value from 1, 2, 3, 4 and 5
inputs.
[0664] FIGS. 30A and 30B show tables of examples of weighting for
combined (1,2,3,4,5) analysis index in Block 35 of FIG. 18.
Block 37--FIG. 18
Transition State Analysis
[0665] BODY MOVEMENT, (34), AROUSAL (35), AEP (30) ANALYSIS
ALGORITHMS.
Block 37
Contest & Transition Weighting Analysis
[0666] FIG. 31 shows an example format for transition weighting
based upon context analysis in Block 37 of FIG. 18.
[0667] Weighting based upon context analysis, BIC co-efficient
table (range of BIC function versus critical thresholds, and
weighting value versus BIC function).
Consciousness Probability
[0668] Compute Bi-spectrum [0669] Real-Triple Product [0670]
Bi-coherence Transition State [0671] AEP [0672] Arousal [0673] Eye
movement Analysis [0674] EOG analysis [0675] EMG analysis (Chin)
Combined AEP and BIC Index for Consciousness & Unconsciousness
Determination Using BIC and R&K in Unique Decision Context
[0676] FIG. 32 shows a flow diagram for determining
consciousness/unconsciousness using combined AEP and BIC index and
R & K in decision context in Block 37 of FIG. 18.
Block 44
GSR (Galvanic Skin Response) or EDA (Electrodermal Activity) or SCR
(Skin Conductivity Response)
[0677] GSR (galvanic skin response) or EDA (electro dermal
activity) or SCR (skin conductivity response) as it is now called,
is a measure of the conductivity of the skin from the fingers
and/or palms. In practice the measurement is made by passing a
constant current through the electrodes to determine the skin
resistance.
[0678] Physiologically the EDA is a measure of sweat gland
activity. Increased sympathetic nervous activity will cause sweat
to be released onto the palms, thus increasing the conductance.
Many emotions such as fear, anger and being startled will elicit
increased sympathetic activity--hence its use in lie-detectors and
biofeedback relaxation training.
Block 44-51--FIG. 18
Stress and Anxiety Analysis
[0679] The HCM system proposes to apply periodic cuff attached
(arm, wrist or other patient attachment location) blood-pressure
measurement system, in conjunction with an oximeter pulse waveform
and ECG waveform (for PTT calculation). The method of utilising the
PTT (by way of oximeter pulse wave and ECG waveform) together with
periodic cuff based blood-pressure measurement provides a means to
derive the quantitative blood-pressure measurement from the cuff
value, and the qualitative blood-pressure measurement from the PTT
calculated signal. In other words the baseline quantitative
blood-pressure value is derived from the cuff blood-pressure value,
while continuous and qualitative blood pressure value is derived
from the PTT value. Furthermore the application of PAT (104-108)
measurement as a means of sensitive EEG arousal detection
potentially provides a new method for minimally invasive and
maximally sensitive arousals detection. In the context of
monitoring a subject in a minimally invasive fashion, and with the
intent of reducing the risk associated with premature awakening
during an anaesthesia related procedure this new method provides
promising scope and application. The benefit of this type of system
is its accuracy and continuous blood pressure monitoring
capability, while maintaining patient comfort by only implementing
the cuff inflation and deflation at periodic time intervals.
[0680] Furthermore the system has a capability to simplify user
operation with application of wireless interconnection of the pulse
oximeter, ECG electrode and blood pressure cuff. This wireless
interconnection may allow calculation of continuous blood pressure
at a remote wireless or wire-linked site (such as a patient
monitoring device), at the EFCG electrode attachment site, at the
oximeter finer probe site or the blood pressure cuff site (refer
FIG. 33).
Respiration and In-Depth Anaesthesia Monitoring
[0681] Effects of paced respiration and expectations on
physiological and physiological responses to threat, anxiety or
stress conditions can be detected by monitoring a subject's
respiration rate.
[0682] These states of threat, anxiety, or stress may be expected
in a case where a patient partially or fully awakens during a
medical procedure. In many cases muscles are paralyzed through
special muscle relaxants, and the ability to alert surrounding
people may be disabled.
Measurement of Respiration Rate and Respiration Rate
Variability
[0683] Step 1. Determine the respiration rate for the past 60
second period. This is repeated after every second for the past 30
seconds of respiratory data to produce a running average
respiratory rate variability.
[0684] Step 2. A similar method as described in block 21 is applied
to provide a syntactic or breath-by-breath detection of the
respiratory waveform. The respiratory waveform data can be derived
(subject to system configuration) from Respiratory Inductive
Plethysmography or other type of respiratory bands or patient
airflow sensors. Alternatively the respiratory waveform can be
derived indirectly from channels such as PTT, ECG, ECG, amongst
others.
[0685] Step 3. An average baseline (AB) for the past 5 minutes
(period is nominal but adjusted with reference to empirical
clinical data) is calculated as a mean average. The change of
respiration (CR) for the past 1 minute (period is nominal but
adjusted with reference to empirical clinical data) is measured
against the stated AB value, to produce the current Respiration
Variability Rate value (RVRV). RVRV=CR/AB RVRV is compared to
threshold values (TV) alarm or notification indication for user or
user display. This notification can be in the form of color changes
of screen display, meter threshold or the like. TV's are determined
from empirical clinical data for the range of normal respiration,
anxious or high level respiration and below normal respiration.
[0686] Step 4. The RVRV, AB, CR are available for display against
the various threshold guide values (ie. TV's) (53).
Heart Rate and In-Depth Anaesthesia Monitoring (see refs. 54, 55,
56, 57, 60)
Galvanic Skin Response
[0687] Galvanic Skin Response is one physiological parameter, which
has been found to be associated with threatening or stressful
conditions and may be correlated with patients under stress.
Galvanic Skin Response may be evident during premature waking
associated with an anesthetic procedure. ps Blood Pressure and
In-Depth Anaesthesia Monitoring
[0688] FIG. 33 shows one form of apparatus for wireless linked
continuous blood pressure measurement (see ref.58).
Improved Biological Sensor for Sensing and Measuring Eye
Opening
[0689] FIG. 34A shows one form of biological sensor device for
sensing and measuring eye opening. The biological sensor includes a
pair of scissor arms 34, 35 connected for pivot able movement at
hinge 36. Arm 34 is adapted to move substantially with an eyelid.
In one form the free end of arm 34 may be fixed to a movable part
of the eyelid by means of an adhesive such as double-sided tape.
The free end of arm 35 may be fixed to part near the eye that
substantially does not move with the eyelid. Each arm 35, 36
includes conductive carbon tracks 37. Tracks 37 may form an
inductor on each arm. Alternatively tracks 37 may form a plate of a
capacitor on each arm. It may be seen that as arms 34, 35 move or
pivot relative to each other the degree of over lap between carbon
tracks 37 on the respective arms changes with the movement. Tracks
37 are connected to an Electronics Interface for converting the
position of arms 35,36 to an electrical signal.
[0690] FIG. 34B shows one form of Electronics Interface wherein the
eye track sensor is represented by a variable inductor 37A for
tracking eyelid position. Variable inductor 37A is formed with
carbon tracks on respective arms 34, 35. Variable inductor 37A
includes a coil on each arm 34, 35 arranged such that movement of
the arms changes the amount of coupling between the coils and
therefore the inductance value of each coil.
[0691] The inductance value may be measured in any suitable manner
and by any suitable means such as a wien bridge. In one form the
inductance value may be measured by a circuit including oscillator
38, resistor 39 and low pass filter 40. The output of low pass
filter 40 provides a signal that is indicative of the relative
position of arms 34, 35 and hence provides a measure of eye
opening. An additional measure of eyelid activity is provided via
EOG electrodes 41, 42 at the free ends arms 34,35. Electrodes 41,
42 are connected to suitable monitoring apparatus via respective
wires 43, 44.
[0692] FIG. 34C shows one form of Electronics Interface wherein the
eye track sensor is represented by a variable capacitor 37B for
tracking eyelid position. The embodiment shown in FIG. 34C is
similar to the embodiment of FIG. 34B except that variable
capacitor 37B is formed with carbon tracks on the respective arms
34, 35. Variable capacitor 37B includes a capacitor plate on each
arm separated by an insulator (dielectric) and is arranged such
that movement of the arms changes the amount of coupling between
the plates and therefore the capacitance value of the variable
capacitor. The capacitance value is measured by the circuit shown
in FIG. 37C which is similar to the circuit in FIG. 34B.
Integrated Anaesthesia Monitoring Electrode System (IAMES) Block
Diagram--Wireless or Wired Version--refer FIG. 35
[0693] FIG. 35 shows one form of electrode system for integrated
anaesthesia monitoring. The IAMES system may be applied for each
wireless electrodes set. 2 unique components may be utilised,
including the Electrode Attachment System (EAS) and the Wireless
Electronic System (WES).
[0694] FIG. 36 shows a sample embodiment of a wire connected sensor
device including bi-coherence, EOG, chin EMG and Eye Opening.
Integrated Sleep Electrode System (ISES)--refer FIG. 37
[0695] Sample of embodiment including bi-coherence, EOG, chin EMG
and Eye Opening Wireless Sensor Device.
[0696] FIG. 37 shows a sample embodiment of a wireless integrated
electrode system including bi-coherence, EOG chin EMG and Eye
Opening.
[0697] The ISES system may be applied for each wireless electrodes
set. 2 unique components may be utilised, including the Electrode
Attachment System (EAS) and the Wireless Electronic System
(WES).
[0698] Note--all above electrode positions may include an optional
redundant electrode system to allow automatic electrode switching
or exchange where a poor quality or excessively high impedance
electrode is detected.
Wireless Electrode Preferred Embodiment (WEPE)--refer FIG. 38
[0699] FIG. 38 shows a preferred embodiment of a wireless
electrode.
[0700] A radio transmitter sends data to a PC within the same room
(operating theatre) which analyses EEG and determines depth of
anaesthesia.
Transmitter Unit
[0701] Battery powered--Maxell rechargeable lithium cell. 3V 65mAh
3 mm.times.20 mm diameter ML2033. [0702] Should provide at least 12
hours operation from a single charge, ideally 24 hours--so that it
may be used for other applications. Radio Transmitter [0703] Prefer
use 915 MHz ISM band or 2.4 GHz ISM band. [0704] Prefer spread
spectrum so that signal is less prone to interference than a single
carrier frequency. [0705] Lower power average <65 mA/12. [0706]
Transmission range 10 m. [0707] Data rate average 256.times.12=3000
bps min. ie. 256 samples per second, 12 bits/sample prefer 16
bits/sample. [0708] Would expect to have much higher Tx data rate
but only use low duty cycle to save power. [0709] Prefer operate at
3V or less. [0710] Blue tooth has too much protocol overhead to get
really low power consumption Data Acquisition
[0711] Done by micro-controller, which also controls radio
transmitter. Use 16 bit or 12 bit with differential end--(INA122)
or discrete op. amps.
[0712] Spread spectrum transmitters normally have receivers to
convey hopping sequence and/or that data has been correctly
received.
[0713] Texas Instruments TRF6900 3V single chip radio
transceiver.
[0714] Tx 21 mA @ 20 dB attenuation, 37 mA @ 0 dB attenuation.
[0715] Rx 24 mA [0716] Power down 2 mA
[0717] Using MSP430 micro-controller to perform base band
operations and data acquisition.
[0718] A system with one master unit may collect acquisition data
from up to 12 slaves. Each slave may collect 512 bytes of data per
second and transmit this to the master. The whole system may
operate at the LIPD (Low Interference Potential Device) ISM band at
915-928 MHz. This is an "unlicensed band" and is subject to the "no
interference, no protection" policy. No protection implies that
several methods have to be devised to make the whole system as
interference-immune as possible.
[0719] The main design criteria are listed below in order of
importance. [0720] Minimal current consumption in slave (ideally
<2 mA). [0721] Maximum immunity to interference. [0722] Small
physical size. [0723] Component lead-time <8-12 weeks. [0724]
System manufacturing cost. Channel Assignment
[0725] The ISM band is located between the GSM mobile and GSM base
station band at 915-928 MHz. Channel spacing is decided to be 500
kHz, giving 24 usable channels for the frequency-hopping
scheme.
Master Unit--refer FIG. 39
[0726] Current consumption is not an adverse factor on the master
unit, so the master will have to control all RF traffic. In each
1-second time slice up to 12 slave transactions of 512 bytes may be
made.
[0727] Referring to FIG. 39, the following scheme is proposed:
[0728] At a data rate of approximately 110 kBps a 512 byte NRZ
packet will take 46.5 mS. Timeslots of approx. 70 mS are allocated
for each slave, totalling 840 mS. The remaining 160 mS are
arbitrary timeslots reserved for retries on unsuccessful slave
transfers (up to 2 for each second).
[0729] On power-up, the master starts "calling" for slaves using
short format packets. During this acquisition process all channels
are sequentially scanned to find free channels for each timeslot
subsequently assigned to a slave. Each time a slave is found a
"time marker" is set in the master indicating which slave needs to
be acquired on which channel in the next timeslot (1000 mS later).
When a transfer from slave to master is due, the master first sends
a synchronisation packet and waits for the relevant slave's
acknowledge. If the slave does not reply, the master starts sending
sync packets while hopping the channels based on a PN sequence
seeded by the current targeted slave's ID. The slave itself also
follows the same PN sequence. About 20 retries are allowed for so a
new channel can be found for the slave to transfer its 512 byte
acquisition packet in case of "jamming".
[0730] Once data transfer with one or more slaves starts, a host PC
will collect the data via an RS232 interface, possibly
incorporating RTS/CTS lines for hardware handshake. Since the
MSP430F149 has 2 KB of RAM, it is expected that 1.5 KB will be
reserved for the acquisition data, so a 3 level deep "FIFO" can be
implemented on the master. This may be useful in case the host PC
has say Windows calling it to perform other functions. This implies
that the PC host software can have a maximum latency of 2 seconds
to collect the data, otherwise an overrun will occur.
Slave Unit--refer FIG. 40
[0731] On power-up, the slave goes into receive mode waiting for a
master sync packet (Master Acquisition Mode--MAM).
[0732] A sync packet will approximately take 1.4 mS, including
preamble, frame header, descriptor and CRC (approx. 150 bits)
[0733] An arbitrary amount of time is designated for the slave to
spend in MAM, say 10 seconds. If a master sync is not acquired, the
slave waits for 20 seconds and enters MAM again.
[0734] This is to avoid excessive current consumption in case the
master is not present or fails during operation of the network.
Once sync is achieved with the master (Master Sync Mode--MSM), the
slave starts taking 256 A/D samples with a resolution of 12-16 bits
every second. These are stored in a RAM buffer and will be
transferred to the master at the end of each 1-second time
slice.
[0735] The PCB for the slave is intended to be identical to the
master's HAN.
[0736] The RS232 pads will be used to assign an ID to the slave and
store it in Flash.
Slave Current Consumption
[0737] The Slave's current consumption is made up of 3 components,
namely the continuous current, peak transceiver current component,
and peak A/D conversion component and works out to be approx. 1.82
mA. Each retry for a slave in a 1 second time slice incurs an extra
1.74 mA. This is anticipated to be unlikely since 24 channels are
available at +5 dBm output.
Continuous Slave Current
[0738] The MSP430F149's LFXTAL is running with a 32.768 kHz crystal
and clocks the internal Timer A. This Timer has a three-channel
Capture/Compare Unit and will be used to interrupt the core at a
256 Hz rate for A/ D conversion. This is the continuous
component.
Transceiver Peak Current
[0739] In each 1-second time slice the TRF6900 will be active for
about 50 mS total. The sequence is as follows: [0740] On wake-up,
The XT2 oscillator is started and is allowed start-up (Crystal
oscillators typically will start from 5-10 mS), together with the
DDS reference. [0741] The CPU is now turned on and provides ample
processor throughput to handle the 110 kBps link and SPI
communication with the TRF6900 transceiver block. About 1 mS is
needed to set-up the TRF6900 into receive and lock. [0742] The
slave has CPU+TRF6900 activated for approx. 2 mS, assuming good BER
and clear channel. [0743] The TRF6900 is put in TX mode. It is
decided to initially output the full output power on the TRF6900.
This results in a higher peak current but will ensure minimal BER
and therefore retries thereby minimising current. A/D Conversion
Peak Current
[0744] The A/D converter has its own RC internal clock and does a
conversion in max 4 uS. (12 bit resolution).
Software
[0745] The firmware will be written in "C" to allow for clarity and
easy expansion. It is worth noting that after production the design
can be ported to a MSP430F147 to reduce cost. Further expansion and
addition should be made easier by a considerable amount of spare
program memory (the F149 has 60 KB Flash memory). The presence of a
1 cycle signed/unsigned 16.times.16 into 32 bit H/W MAC will be
useful for possible future DSP additions like wave filtering.
Hardware
[0746] The Slave and Master PCB should be identical and will be
implemented on a 4 layer PCB.
Analysis Overview--A Breakdown of Primary, Secondary and Tertiary
Analysis--Refer FIG. 41
Vehicle Bicoherence Wireless System (VBWS)--Refer FIG. 42--Car
Vigilance System
System Hardware Block Diagram
[0747] The block diagram in FIG. 42 shows a system consisting of
wireless attached electrodes to patient's forehead and wireless
interface for electrode signal pick-up and EEG processing, within a
driving environment. EEG processing can include coherence spectral
analysis and/or Audio Evoked Response.
[0748] The VBWS system can be applied for each wireless electrodes
set. 2 unique components can be utilised, including the Electrode
Attachment System (EAS) and the Wireless Electronic System
(WES).
Audio Visual Flow Diagram (AVF)--Refer FIG. 43
[0749] FIG. 43 shows a sample embodiment using synchronized audio
and video as a means for in-depth anesthesia system validation and
recall apparatus.
[0750] The embodiment includes use of bi-phasic and AEP in-depth
anaesthesia monitoring system with synchronised video detection and
recording capability.
Pain Level or Consciousness Level Remote Indicator (PLCLRI)--Refer
FIG. 44
[0751] Pain Level or Consciousness Level Remote Indicator
Spread Spectrum Based Wireless, Active Electrode System
(SSBWAES)--Refer FIGS. 45 and 46
[0752] Spread Spectrum Based Wireless, Active Electrode System,
with redundant electrode substitution, dynamic signal quality
verification, impedance verification and calibration.
[0753] FIG. 45 shows a direct connected wireless module.
[0754] FIG. 46 shows an indirect connected wireless module.
[0755] The SSBWAES system may be applied for each wireless
electrodes set. 2 unique components may be utilised, including the
Electrode Attachment System (EAS) and the Wireless Electronic
System (WES).
[0756] Example of embodiment of wireless based active electrode
system.
[0757] FIG. 47 shows one embodiment of a wireless based active
electrode system.
Biofeedback Controlled Drug Delivery System Linded to Consciousness
Monitoring Investigational Device (BCDDSLCIG)--Refer FIG. 48
[0758] FIG. 48 shows a drug delivery system linked to a
consciousness-monitoring device.
[0759] Finally, it is to be understood that various alterations,
modifications and/or additions may be introduced into the
constructions and arrangements of parts previously described
without departing from the spirit or ambit of the invention.
APPENDIX I
References Adjoining Patent Application
[0760] The HCM system utilises a range of different parameters,
which allow the user to establish a library or range of patient
input variables, a range of different secondary analysis and a
range of different weighting and summary tertiary analysis as the
means to determine the depth of anaesthesia for a particular
subject. The following studies demonstrate that the use of a simple
or single dimension or measure for depth of anaesthesia, while
desirable, is not practical with such a complex physiological state
and change of state:
1.
[0761] Barr G, Anderson R E, Samuelsson S, Owall A, Jakobsson J G,
described in British Journal of Anaesthesia June 2000, PMID:
10895750, UI: 20354305 "Fentanyl and midazolam anaesthesia for
coronary bypass surgery: a clinical study of bi-spectral
electroencephalogram analysis, drug concentrations and recall." In
this study, Barr and colleagues describe: "Bi-spectral index (BIS)
was assessed as a monitor of depth of anaesthesia during fentanyl
and midazolam anaesthesia for coronary bypass surgery." "BIS
decreased during anaesthesia, but varied considerably during
surgery (range 36-91) with eight patients having values >60.
Midazolam and fentanyl drug concentrations did not correlate with
BIS. No patient reported explicit or implicit recall. During
clinically adequate anaesthesia with midazolam and fentanyl BIS
varies considerably. The most likely reason is that BIS is not an
accurate measure of the depth of anaesthesia when using this
combination of agents."
2.
[0762] Schraag S, Bothner U, Gajraj R, Kenny G N, Georgieff M,
described in Anesth Analg Apr 2000, PMID: 10553858, UI: 20019286
"The performance of electroencephalogram bi-spectral index and
auditory evoked potential index to predict loss of consciousness
during propofol infusion." In this study, Schraag and colleagues
describe: "The bi-spectral index (BIS) of the electroencephalogram
and middle latency auditory evoked potentials are likely candidates
to measure the level of unconsciousness and, thus, may improve the
early recovery profile." "The electroencephalogram BIS and the
auditory evoked potential index (AEPi), a mathematical derivative
of the morphology of the auditory evoked potential waveform, were
recorded simultaneously in all patients during repeated transitions
from consciousness to unconsciousness.""We conclude that both the
BIS and AEP are reliable means for monitoring the level of
unconsciousness during propofol infusion. However, AEPi proved to
offer more discriminatory power in the individual patient.
IMPLICATIONS: Both the bi-spectral index of the
electroencephalogram and the auditory evoked potentials index are
good predictors of the level of sedation and unconsciousness during
propofol infusion. However, the auditory evoked potentials index
offers better discriminatory power in describing the transition
from the conscious to the unconscious state in the individual
patient."
3.
[0763] Gajraj R J, Doi M, Mantzaridis H, Kenny G N, described in
British Journal of Anaesthesia May 1999, PMID: 10536541, UI:
20006623 "Comparison of bi-spectral EEG analysis and auditory
evoked potentials for monitoring depth of anaesthesia during
propofol anaesthesia." In this study, Gajraj & colleagues
describe: "We have compared the auditory evoked potential index
(AEP Index) and bi-spectral index (BIS) for monitoring depth of
anaesthesia in spontaneously breathing surgical patients." "The
average awake values of AEP Index were significantly higher than
all average values during unconsciousness but this was not the case
for BIS. BIS increased gradually during emergence from anaesthesia
and may therefore be able to predict recovery of consciousness at
the end of anaesthesia. AEPIndex was more able to detect the
transition from unconsciousness to consciousness."
4.
[0764] Gajraj R J, Doi M, Mantzaridis H, Kenny G N, described in Br
J Anaesth Jan 1998, PMID: 9505777, UI: 98166676 "Analysis of the
EEG bispectrum, auditory evoked potentials and the EEG power
spectrum during repeated transitions from consciousness to
unconsciousness." In this study, Gajraj & colleagues describe:
"We have compared the auditory evoked potential (AEP) index (a
numerical index derived from the AEP), 95% spectral edge frequency
(SEF), median frequency (MF) and the bi-spectral index (BIS) during
alternating periods of consciousness and unconsciousness produced
by target-controlled infusions of propofol." "Our findings suggest
that of the four electrophysiological variables, AEP index was best
at distinguishing the transition from unconsciousness to
consciousness."
5.
[0765] Witte H, Putsche P, Elselt M, Hoffmann K, Schack B, Arnold
M, Jager H, described in: Neurosci Lett Nov 1997, PMID: 9406765,
UI: 98068600 "Analysis of the interrelations between a
low-frequency and a high-frequency signal component in human
neonatal EEG during quiet sleep." In this study, Witte and
colleagues describe: "It can be shown that dominant rhythmic signal
components of neonatal EEG burst patterns (discontinuous EEG in
quiet sleep) are characterised by a quadratic phase coupling
(bi-spectral analysis). A so-called `initial wave` (narrow band
rhythm within a frequency range of 3-12 Hz) can be demonstrated
within the first part of the burst pattern. The detection of this
signal component and of the phase coupling is more successful in
the frontal region. By means of amplitude demodulation of the
`initial wave` and a subsequent coherence analysis the phase
coupling can be attributed to an amplitude modulation, i.e. the
envelope curve of the `initial wave` shows for a distinct period of
time the same qualitative course as the signal trace of a `lower`
frequency component (0.75-3 Hz)."
6.
[0766] Schneider G, Sebel P S, described in Eur J Anaesthesiol
Suppl May 1997, PMID: 9202934, UI: 97346517 "Monitoring depth of
anaesthesia". In this study, Schneider & Sebel describe: "In
clinical practice, indirect and non-specific signs are used for
monitoring anaesthetic adequacy. These include haemodynamic,
respiratory, muscular and autonomic signs. These measures do not
indicate adequacy of anaesthesia in a reliable manner." "EEG
information can be reduced, condensed and simplified, leading to
single numbers (spectral edge frequency and median frequency).
These methods appear insufficient for assessing anaesthetic
adequacy. The bi-spectral index, derived from bi-spectral analysis
of the EEG, is a very promising tool for measuring adequacy of
anaesthesia. An alternative approach is to monitor evoked
potentials. Middle latency auditory evoked potentials may be
helpful in assessing anaesthetic adequacy. Both techniques need
further validation."
[0767] The following studies indicate the use of BIS as a strong
indicator of depth of anaesthesia and accordingly the HCM System
utilises BIS as one of the indices for in-depth anaesthesia but
provides multiple concurrent indices to ensure that the user is
able to ultimately provide an informed decision on the depth of a
patient's anaesthesia as opposed only the reliance of one
indicator:
7.
[0768] Sandler N A, Sparks B S, described in J Oral Maxillofac Surg
April 2000, PMID: 10759114, UI: 20220864 "The use of bi-spectral
analysis in patients undergoing intravenous sedation for third
molar extractions." In this study, Sandler describes: "The
Observer's Assessment of Alertness and Sedation (OAA/S) scale was
used to subjectively assess the level of sedation observed by the
anaesthetist before initiating the sedation procedure and then at
5-minute intervals until the end of the procedure. The BIS level
was simultaneously recorded." "The time and dose of the drug given
were recorded. The level of sedation based on a single
anaesthetist's interpretation (OAA/S) and the BIS readings were
then compared. RESULTS: A strong positive relationship between the
BIS index and OAA/S readings was found (P <0.0001).""CONCLUSION:
BIS technology offers an objective, ordinal means of assessing the
depth of sedation. There was a strong relationship between the
objective BIS values and subjective assessment (OAA/S scale) of the
depth of anaesthesia. This can be invaluable in providing an
objective assessment of sedation in oral and maxillofacial surgery
where it may be difficult to determine the level of sedation
clinically."
8.
[0769] Denman W T, Swanson E L, Rosow D, Ezbicki K, Connors P D,
Rosow C E, described in Anesth Analg Apr 2000, PMID: 10735791, UI:
20200014 "Pediatric evaluation of the bi-spectral index (BIS)
monitor and correlation of BIS with end-tidal sevoflurane
concentration in infants and children." In this study, Denman &
colleagues describe: "The bispectral index (BIS) has been developed
in adults and correlates well with clinical hypnotic effects of
anesthetics. We investigated whether BIS reflects clinical markers
of hypnosis and demonstrates agent dose-responsiveness in infants
and children." "BIS correlated with clinical indicators of
anesthesia in children as it did in adults: as depth of anesthesia
increased, BIS diminished. BIS correlated with sevoflurane
concentration in infants and children." "The use of bispectral
index (BIS) during general anesthesia improves the titration of
anesthetics in adults."
9.
[0770] Hirota K, Matsunami K, Kudo T, Ishihara H, Matsuki A,
described in Eur J Anaesthesiol Aug 1999, PMID: 10500939, UI:
99430726 "Relation between bispectral index and plasma
catecholamines after oral diazepam premedication." In this study,
Hirota and colleagues describe: "Venous blood samples (6 mL) were
collected in the case of patients in group D(+) for the measurement
of plasma catecholamines levels using high-performance liquid
chromatography. The bispectral index level (mean +/- SD) in group
D(+): 93.5+/-773.5 was significantly lower than that in group D(-):
96.1+/-1.8 (P <0.05). There was a significant correlation
between bispectral index and plasma norepinephrine levels (r
=0.567, P <0.05). study suggests that the bispectral index
monitor may detect the effect of oral diazepam premedication."
10.
[0771] Muthuswamy J, Roy R J, described in IEEE Trans Biomed Eng
Mar 1999, PMID: 10097464, UI: 99197537 "The use of fuzzy integrals
and bispectral analysis of the electroencephalogram to predict
movement under anesthesia."In this study, Muthuswamy and Roy
describe: "The objective of this study was to design and evaluate a
methodology for estimating the depth of anesthesia in a canine
model that integrates electroencephalogram (EEG)-derived
autoregressive (AR) parameters, hemodynamic parameters, and the
alveolar anesthetic concentration." "Since the anesthetic dose
versus depth of anesthesia curve is highly nonlinear, a neural
network (NN) was chosen as the basic estimator and a multiple NN
approach was conceived which took hemodynamic parameters, EEG
derived parameters, and anesthetic concentration as input feature
vectors. Since the estimation of the depth of anesthesia involves
cognitive as well as statistical uncertainties, a fuzzy integral
was used to integrate the individual estimates of the various
networks and to arrive at the final estimate of the depth of
anesthesia." "The fuzzy integral of the individual NN estimates
(when tested on 43 feature vectors from seven of the nine test
experiments) classified 40 (93%) of them correctly, offering a
substantial improvement over the individual NN estimates."
11.
[0772] Muthuswamy J, Sherman D L, Thakor N V, described in IEEE
Trans Biomed Eng Jan 1999, PMID: 9919830, UI: 99118483
"Higher-order spectral analysis of burst patterns in EEG." In this
study, Muthuswamy & Colleagues describe: "We study power
spectral parameters and bispectral parameters of the EEG at
baseline, during early recovery from an asphyxic arrest (EEG burst
patterns) and during late recovery after EEG evolves into a more
continuous activity. The bicoherence indexes, which indicate the
degree of phase coupling between two frequency components of a
signal, are significantly higher within the delta-theta band of the
EEG bursts than in the baseline or late recovery waveforms. The
bispectral parameters show a more detectable trend than the power
spectral parameters." "The bicoherence indexes and the diagonal
elements of the polyspectrum strongly indicate the presence of
nonlinearities of order two and in many cases higher, in the EEG
generator during episodes of bursting. This indication of
nonlinearity in EEG signals provides a novel quantitative measure
of brain's response to injury."
12.
[0773] Lipton J M, Dabke K P, Alison J F, Cheng H, Yates L, Brown T
I, described in: Australas Phys Eng Sci Med Mar 1998, PMID:
9633147, UI: 98296803 "Use of the bispectrum to analyse properties
of the human electrocardiograph." In this study, Lipton &
colleagues describe: "The bispectrum and bicoherence spectrum have
been shown to be powerful techniques for identifying different
types of nonlinear system responses. This paper presents an
introduction to bispectral techniques applied to biomedical signals
and examines the bispectral properties of the human
electrocardiograph (ECG). The bispectrum proves to be an effective
tool for representing and distinguishing different ECG response
types. Bispectral ECG analysis is non-invasive and may prove to be
a useful discriminant diagnostic."
13.
[0774] Hall J D, Lockwood G G, described in Br J Anaesth Mar 1998,
PMID: 9623435, UI: 98286638 "Bispectral index: comparison of two
montages." In this study, Hall & Lockwood describe: "We have
compared fronto-central and bifrontal montages using a new EEG
monitor, the Aspect A-1000. The monitor uses bispectral analysis to
derive an index of anaesthetic depth, the bispectral index (BIS)."
"ECG electrodes placed in a bifrontal montage were more reliable
than silver dome electrodes in a fronto-central montage and both
types of electrodes had impedances in the clinically useful range.
However, BIS values derived from each montage were found to differ
in an unpredictable manner." "We conclude that the BIS may be
useful for following trends in anaesthetic depth in individual
cases but it is less helpful when making comparison between
patients or as a single value."
14.
[0775] Struys M, Versichelen L, Byttebier G, Mortier E, Moerman A,
Rolly G, described in Anaesthesia Jan 1998, PMID: 9505735, UI:
98166634 "Clinical usefulness of the bispectral index for titrating
propofol target effect-site concentration." In this study, Struys
and colleagues describe: "A greater percentage of bispectral index
readings lying outside the target range (i.e. <40 or >60) and
more movement at incision and during maintenance were found in
Group 1. There was a trend towards more implicit awareness in
patients in Group 1." "Propofol dosage could not be decreased but a
more consistent level of sedation could be maintained due to a more
satisfactory titration of target effect-site concentration."
15.
[0776] Kearse L A Jr, Rosow C, Zaslavsky A, Connors P, Dershwitz M,
Denman W, described in Anaesthesia Jan 1998, PMID: 9447852, UI:
98107541 "Bispectral analysis of the electroencephalogram predicts
conscious processing of information during propofol sedation and
hypnosis." In this study, Kearse & colleagues describe:
"BACKGROUND: The bispectral index (BIS) measures changes in the
interfrequency coupling of the electroencephalogram (EEG). The
purposes of this study were (1) to determine whether BIS correlates
with responses to command during sedation and hypnosis induced by
propofol or propofol and nitrous oxide, and (2) to compare BIS to
targeted and measured concentrations of propofol in predicting
participants' responses to commands.""CONCLUSIONS: Bispectral index
accurately predicts response to verbal commands during sedation and
hypnosis with propofol or propofol plus nitrous oxide. Accuracy is
maintained in situations likely to be encountered during clinical
use: when propofol concentrations are increasing or decreasing and
when repeated measurements are made over time".
16.
[0777] Glass P S, Bloom M, Kearse L, Rosow C, Sebel P, Manberg P,
described in Anesthesiology April 1997, PMID: 9105228, UI: 97259091
"Bispectral analysis measures sedation and memory effects of
propofol, midazolam, isoflurane, and alfentanil in healthy
volunteers." In this study, Glass & colleagues describe: "At
each pseudo-steady-state drug concentration, a BIS score was
recorded, the participant was shown either a picture or given a
word to recall, an arterial blood sample was obtained for
subsequent analysis of drug concentration, and the participant was
evaluated for level of sedation as determined by the responsiveness
portion of the observers assessment of the alertness/sedation scale
(OAAS). An OMS score of 2 or less was considered unconscious. The
BIS (version 2.5) score was recorded in real-time and the BIS
(version 3.0) was subsequently derived off-line from the recorded
raw EEG data.""CONCLUSIONS: The BIS both correlated well with the
level of responsiveness and provided an excellent prediction of the
loss of consciousness. These results imply that BIS may be a
valuable monitor of the level of sedation and loss of consciousness
for propofol, midazolam, and isoflurane."
17.
[0778] Sebel P S, Lang E, Rampil I J, White P F, Cork R, Jopling M,
Smith N T, Glass P S, Manberg P, described in Anesth Analg April
1997, PMID: 9085977, UI: 97240517 "A multicenter study of
bispectral electroencephalogram analysis for monitoring anesthetic
effect." In this study, Sebel & colleagues describe:
"Bispectral analysis (BIS) of the electroencephalogram (EEG) has
been shown in retrospective studies to predict whether patients
will move in response to skin incision." "EEG was continuously
recorded via an Aspect B-500 monitor and BIS was calculated in real
time from bilateral frontocentral channels displayed on the
monitor." "Therefore, the adjunctive use of opioid analgesics
confounds the use of BIS as a measure of anesthetic adequacy when
movement response to skin incision is used as the primary end
point."
18.
[0779] Muthuswamy J, Sharma A, described in J Clin Monit Sept 1996,
PMID: 8934342, UI: 97088404 "A study of electroencephalographic
descriptors and end-tidal concentration in estimating depth of
anesthesia." In this study, Muthuswamy and Sharma describe:
"OBJECTIVE: To study the usefulness of three
electro-encephalographic descriptors, the average median frequency,
the average 90% spectral edge frequency, and a bispectral variable
were used with the anesthetic concentrations in estimating the
depth of anesthesia. METHODS: Four channels of raw EEG data were
collected from seven mongrel dogs in nine separate experiments
under different levels of halothane anesthesia and nitrous oxide in
oxygen." "CONCLUSIONS: The bispectral variable seems to reduce the
non-linearity in the boundary separating the class of
non-responders from the class of responders. Consequently, the
neural network based on the bispectral variable is less complex
than the neural network that uses a power spectral variable as one
of its inputs."
19.
[0780] Shils J L, Litt M, Skolnick B E, Stecker M M, described in
Electroencephalogram Clin Neurophysiol Feb 1996, PMID: 8598171, UI:
96173435 "Bispectral analysis of visual interactions in humans." In
this study, Shils & colleagues describe: "We used non-linear
spectral analysis, in particular the bispectrum, to study
interactions between the electrocerebral activity resulting from
stimulation of the left and right visual fields. The stimulus
consisted of two squares, one in each visual field, flickering at
different frequencies. Bispectra, bichoherence and biphase were
calculated for 8 subjects monocularly observing a visual stimulus."
"These results illustrate how bispectral analysis can be a powerful
tool in analyzing the connectivity of neural networks in complex
systems. It allows different neuronal systems to be labeled with
stimuli at specific frequencies, whose connections can be traced
using frequency analysis of the scalp EEG."
20.
[0781] Leslie K, Sessler D I, Schroeder M, Walters K, described in
Anesth Analg Dec 1995, PMID: 7486115, UI: 96079788 "Propofol blood
concentration and the Bispectral Index predict suppression of
learning during propofol/epidural anesthesia in volunteers." In
this study, Leslie & colleagues describe: "Propofol is often
used for sedation during regional anesthesia. We tested the
hypothesis that propofol blood concentration, the Bispectral Index
and the 95% spectral edge frequency predict suppression of learning
during propofol/epidural anesthesia in volunteers. In addition, we
tested the hypothesis that the Bispectral Index is linearly related
to propofol blood concentration." "The Bispectral Index value when
learning was suppressed by 50% was 91+/-1. In contrast, the 95%
spectral edge frequency did not correlate well with learning. The
Bispectral Index decreased linearly as propofol blood concentration
increased (Bispectral Index=-7.4.[propofol] +90; r2=0.47, n=278).
There was no significant correlation between the 95% spectral edge
frequency and propofol concentration. In order to suppress
learning, propofol blood concentrations reported to produce amnesia
may be targeted. Alternatively, the Bispectral Index may be used to
predict anesthetic effect during propofol sedation."
21.
[0782] Sebel P S, Bowles S M, Saini V, Chamoun N, described in J
Clin Monit Mar 1995, PMID: 7760092, UI: 95280046 "EEG bispectrum
predicts movement during thiopental/isoflurane anesthesia." In this
study, Sebel & colleagues describe: "OBJECTIVE. The objective
of our study was to test the efficacy of the bispectral index (BIS)
compared with spectral edge frequency (SEF), relative delta power,
median frequency, and a combined univariate power spectral
derivative in predicting movement to incision during
isoflurane/oxygen anesthesia." "CONCLUSIONS. When bispectral
analysis of the EEG was used to develop a retrospectively
determined index, there was an association of the index with
movement. Thus, it may be a useful predictor of whether patients
will move in response to skin incision during anesthesia with
isoflurane/oxygen."
22.
[0783] Kearse L A Jr, Manberg P, Chamoun N, deBros F, Zaslavsky A,
described in Anesthesiology Dec 1994, PMID: 7992904, UI: 95085072
"Bispectral analysis of the electroencephalogram correlates with
patient movement to skin incision during propofol/nitrous oxide
anesthesia." In this study, Kearse & colleagues describe:
"BACKGROUND: Bispectral analysis is a signal-processing technique
that determines the harmonic and phase relations among the various
frequencies in the electroencephalogram. Our purpose was to compare
the accuracy of a bispectral descriptor, the bispectral index, with
that of three power spectral variables (95% spectral edge, median
frequency, and relative delta power) in predicting patient movement
in response to skin incision during propofol-nitrous oxide
anesthesia." "CONCLUSIONS: The bispectral index of the
electroencephalogram is a more accurate predictor of patient
movement in response to skin incision during propofol-nitrous oxide
anesthesia than are standard power spectrum parameters or plasma
propofol concentrations."
23.
[0784] Sigl J C, Chamoun N G, described in J Clin Monit Nov 1994,
PMID: 7836975, UI: 95138762 "An introduction to bispectral analysis
for the electroencephalogram." In this study, Sigl and Chamoun
describe: "The goal of much effort in recent years has been to
provide a simplified interpretation of the electroencephalogram
(EEG) for a variety of applications, including the diagnosis of
neurological disorders and the intraoperative monitoring of
anesthetic efficacy and cerebral ischemia. Although processed EEG
variables have enjoyed limited success for specific applications,
few acceptable standards have emerged. In part, this may be
attributed to the fact that commonly used signal processing tools
do not quantify all of the information available in the EEG. Power
spectral analysis, for example, quantifies only power distribution
as a function of frequency, ignoring phase information. It also
makes the assumption that the signal arises from a linear process,
thereby ignoring potential interaction between components of the
signal that are manifested as phase coupling, a common phenomenon
in signals generated from nonlinear sources such as the central
nervous system (CNS)."
24.
[0785] Kearse L A Jr, Manberg P, DeBros F, Chamoun N, Sinai V,
described in Electroencephalogr Clin Neurophysiol Mar 1994, PMID:
7511501, UI: 94192475 "Bispectral analysis of the
electroencephalogram during induction of anesthesia may predict
hemodynamic responses to laryngoscopy and intubation." In this
study, Kearse and colleagues describe: "The use of
electroencephalography as a measure of adequacy of anesthesia has
achieved limited success. Our purpose was to determine whether the
non-linear properties of the electroencephalogram (EEG) as defined
by the bispectral index was a better predictor of autonomic
responses to endotracheal intubation during opioid-based anesthesia
than the linear statistical properties of the EEG formulated by
power spectral analysis." "There was a significant difference
between response groups as measured by the bispectral index which
distinguished responders from non-responders independently of the
amount of drug given. None of the variables of power spectral
analysis accurately distinguished responder from
non-responder."
[0786] The HCM System is designed to use conventional low cost
electrodes in conjunction with wireless interface device to reduce
the hazards and difficulties associated with wiring patients during
operational procedures. Furthermore the HCM System utilises a
unique method of displaying the charge status of the wireless
electrode module by way of simple led display representing the
available charge time, where each hour (or 2 hours) of charged
usage time available is represented by a LED display. The HCM
System wireless device also provides a simple fool-proof means of
recharging the wireless module by utilising a unique proximity RF
charging technique.
[0787] The following papers present some of the difficulties of
state of the art which are overcome by the HCM System:
25.
[0788] Yli-Hankala A, Vakkuri A, Annila P, Korttila K, described in
Acta Anaesthesiol Scand May 1999, PMID: 10342003, UI: 99273549 "EEG
bispectral index monitoring in sevoflurane or propofol anaesthesia:
analysis of direct costs and immediate recovery." In this study,
Yli-Hankala and colleagues describe: "BIS monitoring decreased the
consumption of both propofol and sevoflurane and hastened the
immediate recovery after propofol anaesthesia. Detailed cost
analysis showed that the monitoring increased direct costs of
anaesthesia treatment in these patients, mainly due to the price of
special EEG electrodes used for relatively short anaesthesias."
26
[0789] EEG power spectrum during repeated transitions from
consciousness to unconsciousness. R. J Gajrai, M. Doi, H.
Mantzzaridis and G. N. C. Kenny. British Journal of Anaesthesia
1998.
29
[0790] Moira L. Steyne-Ross and D. A. Steyne-Ross, of Department of
Anaesthetics, Waikato Hospital, Hamilton, New Zealand describe
"Theoretical electroencephalogram stationary spectrum for
white-noise-driven cortex:Evidence for a general
anaesthetic-induced phase transition" This paper describes an
increase in EEG spectral power in the vicinity of the critical
point of transition into comatose-unconsciousness.
[0791] The HCM System applies the capability to predict the
amplitude of an EEG signal during administration of an anaesthetic
drug as one o the weighted inputs for an improved in depth
anaesthesia monitoring system.
30.
[0792] Analysis of the EEG Bispectrum, auditory evoked potentials
and the EEG power spectrum during repeated transitions from
consciousness to unconsciousness. R. J. Gajraj, M. Doi, H.
Mantzaridis and G. N. C. Kenny. British Journal of Anaesthesia
1998.
31.
[0793] Differentiating Obstructive and Central Sleep Apnea
Respiratory Events through Pulse Transit Time. Jerome Argod,
Jean-Louis Pepin, and Patrick Levy. Resp Crit Care Med 1998 Vol 158
pp 1778-1783.
32.
[0794] Pulse Transit Time: an appraisal of potential clinical
applications. Robin P Smithj, Jerome Argod, Jean-Louis Pepin,
Patrick A Levy. Thorax 1999;54:452-458.
33.
[0795] An Introduction to Bispectral Analysis for the
electroencephelogram. Jeffrey C. Sigl. PhD, and Nassib G. Chamoun,
M S. 1994 Little, Brown and Company.
34.
[0796] Allan Rechtschaffen and Anthony Kales, Editors of A Manual
of Standardized Terminology, Techniques and Scoring System for
Sleep Stages of Human Subjects, Brain Information Service/Brain
Research Institute, University of California, Los Angeles, Calif.
90024.
35.
[0797] EEG Arousals: Scoring Rules and Examples. A Preliminary
Report from the Sleep Disorders Atlas Task Force of the American
Sleep Disorders Association. Sleep, Vol. 15 No. 2,1992.
36 .
[0798] 95% spectral edge analysis is the point on the spectral
power curve of a sample of data, which is measured at the 95% point
on the frequency axis, where the Y axis represents the frequency
band power.
[0799] For example refer to FIG. 49 which shows a power spectral
curve of sample data.
37.
[0800] The Biomedical Engineering Handbook. Joseph D. Bronzino.
1995 pages 840 to 852. Signal averaging.
[0801] 38. The Fourier Transform in Biomedical Engineering.
Introduction to the Fourier Transform. T. M. Peters. 1998. Chapter
1.
39.
[0802] Biomedical Instruments Theory and Design. Second
Edition.Walter Welkowitz1992. The frequency spectrum. Pages 10 to
19.
40.
[0803] The Biomedical Engineering Handbook. Joseph D. Bronzino.
1995 Bioloectric Phenomena. Craig S. Henriquez. Chapter 11.
41.
[0804] The Biomedical Engineering Handbook. Joseph D. Bronzino.
1995 Biomedical Signals: Origin and Dynamic Characteristics;
Frequency-Domain Analysis. Chapter 54.
42.
[0805] The Biomedical Engineering Handbook. Joseph D. Bronzino.
1995 Anaesthesia Delivery Systems. Chapter 86.
43.
[0806] The Biomedical Engineering Handbook. Joseph D. Bronzino.
1995 Measurement of Sensory-Motor Control Performance Capacities.
Chapter 145.
44.
[0807] Principles and Practice of Sleep Medicine, Second edition
1994, Kryger Roth Dement. Chapter 89 Monitoring and Staging Human
Sleep by Mary Carskadon and Allan Rechtschaffen.
45.
[0808] Patent Reference: AU 632432 Analysis System for
Physiological Variables. Burton and Johns, 1989.
46.
[0809] An improved method for EEG analysis and computer aided sleep
scoring. (1/2 period amplitude abstract--Johns and Burton 1989.
Abstracts of Conference Manual.
47.
[0810] Atlas of Adult Electroencephalography. Warren T Blume,
Masaka Kaibara, Raven Press, 1995. Artifacts, Chapter 2.
48.
[0811] The Biomedical Engineering Handbook. Joseph D. Bronzino.
1995 Higher-Order Spectra in Biomedical Signal Processing. Pages
915-916.
49.
[0812] Barrett-Dean Michelle, Preliminary Investigation of the
Compumedics Mattress sensor in a clinical setting. St Frances
Xavier Cabrini Hospital, Malvem, Victoria. ASTA, 1999.
50.
[0813] Modified R&K utilising frequency compensation techniques
to best approximate non-conventional R&K EEG electrode position
recommendations (34).
51.
[0814] Bispectral Analysis of the Rat EEG During Various Vigilance
States. Taiking Ning and Joseph D. Bronzino. IEEE transactions on
biomedical engineering. April 1989.
52.
[0815] Trademarks associated with Aspect monitoring; [0816]
BIS.RTM. [0817] Bispectral Index.RTM. [0818] A-2000.TM. 53.
[0819] Effects of paced respiration and expectations on
physiological and physiological responses to threat.
[0820] McCaul K D, Solomon S, Holmes D S, Journal of Personality
and Social Psychology 1979,Vol 37, No 4,564-571
54.
[0821] Casino gambling increases heart rate and salivary cortisol
in regular gamblers.
[0822] Meyer G, Hauqffa B P, Schedlowski M, Pawlak C, Stadler M A,
Exton M S Biol Psychiartry 2000 Nov 1;48(9):948-53
ABSTRACT
55.
[0823] Heart rate variability, trait anxiety, and perceived stress
among physically fit men and women.
[0824] Dishman R K, Nakamura Y, Garcia M E, Thompson R W, Dunn A L,
Blair S N Int J Psychophysiol 2000 Aug;37(2):121-33
56.
[0825] Effects of short-term psychological stress on the time and
frequency domains of heart variability
[0826] Delaney J P, Brodie D A Percept Mot Skills 2000
Oct;9(2):515-24
ABSTRACT
57.
[0827] Heart rate variability in depressive and anxiety disorders
Gorman J M, Sloan R P Am Heart J 2000 Oct;140(4 Suppl):77-83
ABSTRACT
58.
[0828] Chronic stress effects blood pressure and speed of
short-term memory.
[0829] Brand N, Hanson E, Godaert G Percept Mot Skills 2000
Aug;91(1):291-8
ABSTRACT
59.
[0830] Effects of Paced Respiration and Expectations on
Physiological and Psychological Responses to Threat.
[0831] Kevin D. McCaul, Sheldon Solomon, and David S. Holmes
University of Kansas, Journal of Personality and Social Psychology
1979, Vol. 37, No 4, 564-571
PAPER
60.
[0832] Heart. Rate Variability, trait anxiety, and perceived stress
among physically fit men and women
[0833] Rod K. Dishman, Yoshia Nakamura, Melissa E. Garcia, Ray W.
Thompson, Andrea L. Dunn, Steven N. Blair 16th Nov. 1999,
International Journal of Psychophysiology 37 (2000) 121-133
61
[0834] Auditory evoked potential index: a quantitative measure of
changes in auditory evoked potentials during general anaesthesia H.
Mantzaridis and G. N. C. Kenny Anaesthesia, 1997, 52 pages
1030-1036
62
[0835] Concept for an intelligent anaesthesia EEG monitor W. NAHM,
G. STOCKMANNS, J. PETERSEN, H. GEHRING, E. KONECNY, H. D. KOCHS and
E. KOCHS
[0836] Med. Inform. (1999), vol. 24. NO. 1-9
63
[0837] Monitoring depth of anaesthesia G. Schneider and P. S. Sebel
European Journal of Anaesthesiology 1997, 14 (Suppl. 15), 21-28
64
[0838] Clinical usefulness of the bispectral index for titrating
propofol target effect-site concentration
[0839] M. Struys, L. Versichelen, G. Byttebier, E. Mortier, A.
Moerman and G. Rolly Anaesthesia, 1998, 53, pages 4-12
65
[0840] Genetic dependence of the electroencephalogram bisprectrum
JOEL. WHITTON, SUSAN M. ELGIE, HERB KUGEL, AND HARVY MOLDOFSKY
[0841] Electroencephalography and clinical Neurophysiology, 1985,
60;293-298
66
[0842] Assessment of power spectral edge for monitoring depth of
anaesthesia using low methohexitone infusion
[0843] Peter S. Withington, John Morton, Richard Arnold, Peter. S.
Sebel and Richard Moberg
[0844] International Journal of Clinical Monitoring and Computing
3: 117-122,1986
67
[0845] Analysis of the interrelations between frequency band of the
EEG by means of the bispectrum.
[0846] Electroencephalography and clinical neurophysiology.
International Federation-7th Congress. Free communications in
EEG.
[0847] Kleiner B; Huber PJ, Dumermuth G;
68
[0848] GSR or Skin Response Biomedical instruments, Inc.
WWW.bio-medical.com/Gsr.html 26/0201
69
[0849] Awareness during general anaesthesia: is it worth worrying
about ? MJA Vol 174 5 Mar. 2001
70
[0850] Women take longer to recover from operations and are more
likely to suffer side-effects than men during surgery.
[0851] British Medical Journal, Mar. 23 2001.
[0852] (reference--Age Mar. 24, 2001)
71
[0853] Compumedics Siesta patient monitoring system.
72
[0854] Compumedics E-Series patent monitoring system.
73
[0855] Compumedics Profusion Software patient monitoring
system.
74
[0856] Recall of intraoperative events after general anaesthesia
and cardiopulmonary bypass; Phillips A A, McLean R F, Devitt J H,
Harrington E M; Canadian Journal of Anaesthesia; 1993 Oct.
75
[0857] Patient satisfaction after anaesthesia and surgery: results
of a prospective survey of 10,811 patients; Myles P S, Williams D
L, Hendrata M, Anderson H, Weeks A M; British Journal of
Anaesthesia; 2000 Jan.
76
[0858] EEGs, EEG processing, and the bispectral index; Todd M M;
Anesthesiology; 1998 Oct.
77
[0859] Detecting awareness during general anaesthetic caesarean
section. An evaluation of two methods; Bogod D G, Orton J K, Yau H
M, Oh T E; Anaesthesia; 1990 Apr.
78
[0860] Oesophageal contractility during total i.v. anaesthesia with
and without glycopyrronium; Raftery S, Enever G, Prys-Roberts C;
British Journal of Anaesthesia; 1991 May.
79
[0861] Effect of surgical stimulation on the auditory evoked
response; Thornton C, Konieczko K, Jones J G, Jordan C, Dore C J,
Heneghan C P H; British Journal of Anaesthesia; 1988 Mar.
80
[0862] Comparison of bispectral index, 95% spectral edge frequency
and approximate entropy of the EEG, with changes in heart rate
variability during induction of general anaesthesia; Sleigh J W,
Donovan J; British Journal of Anaesthesia; 1999 May.
81
[0863] Bispectral index monitoring allows faster emergence and
improved recovery from propofol, alfentanil, and nitrous oxide
anesthesia. BIS Utility Study Group; Gan T J, Glas P S, Windsor A,
Payne F, Rosow C, Sebel P, Manberg P; Anesthesiology; 1997 Oct.
82
[0864] Why we need large randomized studies in anaesthesia; Myles P
S; British Journal of Anaesthesia; 1999 Dec.
83
[0865] U.S. Pat. No. 5,381,804, Aspect Medical Systems, Inc Jan. 17
1995 describes a monitor for receiving electrical signals from a
living body.
84
[0866] U.S. Pat. No. 5,458,117, Aspect Medical Systems, Inc Oct. 17
1995 describes a cerebral biopotential analysis system and
method.
85
[0867] U.S. Pat. No. 5,320,109 Aspect Medical Systems, Inc Jun. 14
1994 describes a cerebral biopotential analysis system and
method.
86
[0868] It has been reported that Wrist actigraphic recordings may
differentiate sleep and wakefulness with a 94.5% agreement with
standard polysomnography (Mullaney et al. 1980).
[0869] Other Wrist actigraphic studies studies have reported a
91.8% agreement in healthy subjects, 85.7% in patients with
obstructive sleep apnea syndrome, 78.2% in patients with insomnia,
and 89.9% in children Sadeh et al. (1989) (21).
87.
[0870] Medical Dictionary. 1997 Merriam-Webster, Incorporated.
[0871] http://www.intelihealth.com/IH/
88.
[0872] The American Heritage.RTM. Dictionary of the English
Language
[0873] http://www.bartleby.com/61/
89.
[0874] William Thomas Gordon Morton first demonstrated what is
today referred to as surgical anaesthesia (89).
[0875] Pubmed search
90
[0876] In Australia about 1 million people a year undergo general
anaesthesia. Of these 1 million people about 5 people die each
year, as a direct result of the anaesthesia, while about 3000 more
will be inadequately anaesthetised. These people inadequately
anaesthetised will experience a range of symptoms from hearing
recall while undergoing a medical procedure, sight recall from
premature recovery and the early opening of eyes, stress and
anxiety from experiencing paralysis while some degree of mental
awareness to the medical procedure being instigated, memory recall
from having some degree of consciousness, operation mishaps can
occur in cases where the subject's state of paralysis is not
adequate and leads to movement of the subject's body during
incision.
91
[0877] In fact, even hospitals such as Melbourne's Alfred Hospital,
which demonstrated one of the world's lowest reported incidences of
consciousness under general anaesthesia, still have an incidence
rate of 1 in 1000 patients (for consciousness under anaesthesia)
(91).
[0878] Pubmed search
92
[0879] Up-to-date there has been no way to determine whether a
patient is asleep during a medical procedure (according to
University of Sydney-Australia's Web site, introductory paper on
anaesthesia).
[0880] Pubmed search
93
[0881] Furthermore, the discovery in 1942 Canadian anaesthetists
determined (Sir Walter Raleigh had known in 1596 that the
indigenous people of Bolivia had been using an American plant
derivative called curare to cause paralysis) that neuromuscular
blocking drugs could be developed (93). Since 1942 these drugs have
revolutionised surgery, particularly abdominal and chest operations
where muscle contraction had made cutting and stitching almost
impossible.
[0882] Pubmed search
94
[0883] Anaesthetists tend to overestimate the amount of anaesthetic
drug usage by up to 30%. This overestimation has consequences in
relation to a patient's health, recovery time and financial costs
to health services.
[0884] (Age article--eyes wide shut)
[0885] Pubmed search
95
[0886] The challenge to monitor for appropriate or optimum
anaesthesia is even further demonstrated with classic experiments
such as that of psychiatrist Bernard Levin in 1965, when 10
patient's who were read statements during anaesthesia, later had no
recall of the statements when questioned after surgery. However, of
the same patient's under hypnosis four could quote the words
verbatim and another four could remember segments, but became
agitated and upset during questioning.
[0887] Pubmed search
96
[0888] An adequately anaesthetised patient should not "feel",
"smell", "see" or "taste" anything until they regain
consciousness.
[0889] (Age article--eyes wide shut)
[0890] Pubmed search
97
[0891] In 1998 Dr David Adams of New York's Mount Sinai Medical
Centre replayed audiotapes of paired words (boy/girl, bitter/sweet,
ocean/water . . . ) to 25 unconscious heart surgery patients.
Approximately four days after the operation, the patients listened
to a list of single words. Some of these words had been played
while they were unconscious during their former operation. The
patients were asked to respond to each word with the first word
that came into their minds. The patient was found to be
significantly better at free-associating the word pairs they had
already encountered than those they had not. It was apparent that
the patients had heard the information and remembered it.
[0892] (Age article--eyes wide shut)
[0893] Pubmed search
98
[0894] It appears that while a smaller number of patient's have
conscious memories of their experiences on the operating table, a
larger number have unconscious recollections. While positive
messages during surgery may have desired consequences others can
have undesirable results.
[0895] Pubmed search
99
[0896] The PERCLOS Monitor
[0897] Reference: http://www.cmu.edu.cmri/drc/drcperclosfr.html,
12/10/2000
100.
Drowsy Driver Detection System
[0898]
http://www.jhuapl.edu/ott/newtech/soft/DDDSystem/benefits.htm
101.
[0899] Driver Drowsiness Literature Review & Perspective;
Cause, effects, detection, PSG methods, Bio-behavioural,
Physiological, safety air-bags business case, practicality,
ease-of-use, ethical implications & alarms.
[0900] Burton, June 2001.
102.
[0901] Proprietary, Zilberg Eugene, Ming Xu May 2001. Compumedics
preliminary Vigilance Project Report on Drowsiness and movement
sensor (seat & steering wheel)--correlation analysis (May,
2001).
103.
[0902] Burton David, Methods and Apparatus for Monitoring Human
Consciousness, U.S. Provisional Patent Application 60/298,011 filed
13 Jun. 2001.
104.
[0903] Iani C, Gopher D, Lavie P Effects of Task Difficulty and
Invested Mental Effort of Peripheral Vascoconstriction. SLEEP,
Vol-24, Abstract Supplement 2001.
105
[0904] Autonomic Activation Index (MI)--A New Marker of Sleep
Disruption. Pillar G, Shlitner A, Lavie P. SLEEP, Vol-24, Abstract
Supplement 2001.
106.
[0905] Lac, Leon. PAT perspective- how well is pulse wave amplitude
related top PAT?. Correspondence with DB. Jun. 27 2001.
107.
[0906] Peter G. Catcheside, R. Stan Orr, Siau Chien Chiong, Jeremy
Mercer, Nicholas A, Saunders. Peripheral cardiovascular responses
provide sensitive markers of acoustically induced arousals from
NREM sleep.
108.
[0907] Michael H. Pollok and Paul A. Obrist. Aortic-Radial Pulse
Transit Time and ECG Q-Wave to Radial Pulse Wave Interval as
Indices of Beat-By-Beat Blood Pressure Change. Psychophysiology.
Vol. 20 No. 11983.
109.
[0908] PAT signal Provides New Marker of Sleep Quality, Respiratory
and Cardiovascular Disorders.
[0909] http://www.talkaboutsleep.com/news/PAT signal.htm. Chicagao,
Ill., Jun. 7, 2001
110.
[0910] Todd, Michael M. MD. EEGs, EEG Processing, and Bispectral
Index. Anaesthesiology. Vol. 89(4), pp 815-817. Oct 1998
111.
[0911] A Primer for EEG Signal Processing in Anaesthesia.
[0912] Anaesthesiology. Vol. 89(4), pp 980-1002. Oct 1998
[0913] 112. Lippincott-Raven, 1997. Evoked Potentials in Clinical
Medicine. Third Edition. a) Click Intensity. CH8, 179. b) Click
Polarity. CH8, PP183. c) Stimulus Delivery Apparatus. CH8,
PP188.
[0914] 113. Nieuwenhuijs, D.; Coleman, E. L.; Douglas, N. J.;
Drummond, G. B.; Dohan, A. Bispectral index values and spectral
edge frequency of different stages of physiologic sleep.
Anesth.Analg.
[0915] 114. Kryger, Roth, Dement. Principles and Practice of Sleep
Medicine. Second edition, 2000.
APPENDIX II
Glossary:
Amplitude
[0916] One half the peak-to-peak height of a sinusoid, usually
measured in volts or microvolts (.mu.V). (33)
Anesthesia or Anaesthesia
[0917] Noun:
[0918] 1. Total or partial loss of sensation, especially tactile
sensibility, induced by disease, injury, acupuncture, or an
anesthetic, such as chloroform or nitrous oxide. 2. Local or
general insensibility to pain with or without the loss of
consciousness, induced by an anesthetic. 3. A drug, administered
for medical or surgical purposes, that induces partial or total
loss of sensation and may be topical, local, regional, or general,
depending on the method of administration and area of the body
affected.
[0919] Word History:
[0920] The following passage, written on Nov. 21, 1846, by Oliver
Wendell Holmes, a physician-poet and the father of the Supreme
Court justice of the same name, allows us to pinpoint the entry of
anesthesia and anesthetic into English: "Every body wants to have a
hand in a great discovery. All I will do is to give you a hint or
two as to names--or the name--to be applied to the state produced
and the agent. The state should, I think, be called `Anaesthesia`
[from the Greek word anaisthesia, "lack of sensation"]. This
signifies insensibility . . . . The adjective will be
`Anaesthetic.` Thus we might say the state of Anaesthesia, or the
anaesthetic state." This citation is taken from a letter to William
Thomas Green Morton, who in October of that year had successfully
demonstrated the use of ether at Massachusetts General Hospital in
Boston. Although anaesthesia is recorded in Nathan Bailey's
Universal Etymological English Dictionary in 1721, it is clear that
Holmes really was responsible for its entry into the language. The
Oxford English Dictionary has several citations for anesthesia and
anesthetic in 1847 and 1848, indicating that the words gained rapid
acceptance
Bicoherence
[0921] A normalised measure of phase coupling in a signal, ranging
from 0% to 100%. (33)
Bispectral Index
[0922] A mutlivariate measure incorporating bispectral and
time-domain parameters derived from the EEG. (33)
Bispectrum
[0923] A measure of the level of phase coupling in a signal, as
well as the power in the signal. The bispectrum can be described as
a measure of the actual level of phase coupling that exists in the
EEG signal, with the phase angles of the components at their actual
values.(33)
Component
[0924] One of the sinusoids summed together in a Fourier series to
represent a signal. (33)
Consciousness
[0925] Function: noun
[0926] 1: the totality in psychology of sensations, perceptions,
ideas, attitudes, and feelings of which an individual or a group is
aware at any given time or within a given time span <altered
states of con-scious-ness, such as sleep, dreaming and
hypnosis--Bob Gaines>
[0927] 2: waking life (as that to which one returns after sleep,
trance, or fever) in which one's normal mental powers are present
<the ether wore off and the patient regained
con-scious-ness>
[0928] 3: the upper part of mental life of which the person is
aware as contrasted with unconscious processes (87)
[0929] 1. The state or condition of being conscious. 2. A sense of
one's personal or collective identity, including the attitudes,
beliefs, and sensitivities held by or considered characteristic of
an individual or group: Love of freedom runs deep in the national
consciousness. 3a. Special awareness or sensitivity: class
consciousness; race consciousness. b. Alertness to or concern for a
particular issue or situation: a movement aimed at raising the
general public's consciousness of social injustice. 4. In
psychoanalysis, the conscious. (88)
Epoch
[0930] A series of successive, equal time segments (overlapping or
contiguous) into which the data series x(k) is divided. (33)
Features
[0931] Descriptive parameters extracted from a signal and
correlated with some information of interest, such as a particular
cerebral state. (33)
Fourier Series
[0932] A representation of a signal as a sum of sinusoid components
of different frequencies and amplitudes. (33)
Fourier Transform
[0933] A mathematical process that converts a time signal to its
representation in terms of the amplitudes and frequencies of its
sinusoid components. (33)
Frequency
[0934] The rate at which a signal or sinusoid oscillates, usually
measured in cycles per second (Hz). (33)
Frequency Domain
[0935] A representation of a signal in which amplitude or power is
a function of frequency. (33)
Frequency
[0936] The spacing in hertz between successive values of the
Fourier
Resolution
[0937] Transform. (33)
Fundamental
[0938] A component of an output signal that is not an IMP. (33)
Hertz (Hz)
[0939] A measure of frequency; equivalent to cycles per second.
(33)
Real Triple Product (RTP)
[0940] A measure of the maximum possible degree of phase coupling
that would result if the phase angle of each and every component of
a signal were exactly identical. It is also a function of signal
power. The ratio of the bispectrum to the square root of the real
triple product, which expresses the normalized degree of phase
coupling in the EEG range (ranging from 0 to 100%) is defined as
the bicoherence.
System
[0941] Refers to the device or apparatus forming the basis of
invention. This system typically contains physiologically recording
capabilities for 1 or more channels of physiological data, display
viewing capabilities for viewing or reviewing one or more channels
of physiological data, data analysis and reporting capabilities,
and data recording and archiving and retrieval capabilities, for
the purpose of providing a device for the investigation of a
patient's state of consciousness or vigilance.
HCM system
[0942] Denotes Human Consciousness Monitoring system including
methods and apparatus for monitoring, sensing, tracking, analysing,
storing, logging and/or displaying, in the context of the present
invention, data related to the state of mind or state of
consciousness of human and other sentient subjects.
System--Generated Audio
[0943] Refers to the audio click, which can be applied to the
patient's ear or ears during an operating procedure, for
example.
Unconscious
[0944] Adjective:
[0945] Lacking awareness and the capacity for sensory perception;
not conscious. 2. Temporarily lacking consciousness. 3. Occurring
in the absence of conscious awareness or thought: unconscious
resentment; unconscious fears. 4. Without conscious control;
involuntary or unintended: an unconscious mannerism.
[0946] Noun:
[0947] The division of the mind in psychoanalytic theory containing
elements of psychic makeup, such as memories or repressed desires,
that are not subject to conscious perception or control but that
often affect conscious thoughts and behavior.
[0948] Other forms:
[0949] un-con-scious-ly--ADVERB un-con-scious-ness--NOUN (88)
Unconsciousness
[0950] Function: adjective
[0951] 1: not marked by conscious thought, sensation, or feeling
<un-con-scious motivation>
[0952] 2: of or relating to the unconscious
[0953] 3: having lost consciousness <was un-con-scious for three
days> [0954] un-con-scious-ly adverb [0955] un-con-scious-ness
noun (87) [0956] Alert watchfulness (88) Vigilance
[0957] Function: noun
[0958] : the quality or state of being wakeful and alert: degree of
wakefulness or responsiveness to stimuli [0959]
vig-i-lant/-l&nt/adjective (87) Unconsciousness
[0960] Function: adjective
[0961] 1: not marked by conscious thought, sensation, or feeling
<un-con-scious motivation>
[0962] 2: of or relating to the unconscious
[0963] 3: having lost consciousness <was un-con-scious for three
days> [0964] un-con-scious-ly adverb [0965] un-con-scious-ness
noun (87) Subject
[0966] This word can be interchanged within context of this
document for "patient".
Patient
[0967] This word can be interchanged within context of this
document for "subject".
Vagal Modulation Definition;
[0968] The parasympathetics to the heart are contained in the vagus
nerves. The vagus nerves. Stimulation of these nerves causes
slowing of the heart while cutting the parasympathetics causes the
heart rate to increase. Vagal modulation relates to the modulation
of the vagus nerves (Stedmans, Medical Dictionary, 2000), which in
turn relates to the slowing of the heart (Vander etal, Human
Physiology, 1970 PP 241).
Relationship of Bispectrum, Real Triple Product and Bicoherence
[0969] The bispectrum can be described as a measure of the actual
level of phase coupling that exists in the EEG signal, with the
phase angles of the components at their actual values.
[0970] The real triple product is a measure of the maqximum
possible degree of phase coupling, which could result if the phase
angle of each and every component of the EEG were exactly
identical. The ratio of the bispectrum to the square root of the
real triple product, which expresses the normalized degree of phase
coupling in the EEG range (ranging from 0 to 100%) is defined as
the bicoherence.
Context Analysis
[0971] Refers to whether the patient's is entering a state of
consciousness or emerging from unconsciousness. 1/2 period
amplitude analysis (ref 3,4,8,9) is a method for determining the
stage od sleep a subject is in. Stages include WAKE, STAGE 1, STAGE
2, STAGE 3, STAGE 4 AND REM SLEEP. TABLE-US-00028 ABBREVIATIONS
ADMS Anaesthesia Depth of Monitoring System. Bi Bispectral index. B
Bicoherence derivattive of the EEG signal. SSA Sleep Staging
Analysis. AEPi Audio Evoked Potential index. TUC Transition From
Unconsciousness to Consciousness. TCU Transition From Consciousness
to Unconsciousness CIAi Comprehensive Integrated Anaesthesia index.
The main function and output of the ADMS. DOA Depth Of Anaesthesia.
CALPAT Calibrated Patient (values). CP Calibrated Patient. IDDZA
Impirical Data Display Zone A. IDDZB Impirical Data Display Zone B.
IDDZC Impirical Data Display Zone C. IDDZD Impirical Data Display
Zone D. CPDZA Calibrated Patient DisplayZone A. CPDZB Calibrated
Patient DisplayZone B. CPDZC Calibrated Patient DisplayZone C.
CPDZD Calibrated Patient DisplayZone D. CPTUCBi Calibrated Patient
data for Transition from Unconsciousness to Consciousness for Bi.
CPTUCAEPi Calibrated Patient data for C1260Transition from
Unconsciousness to Consciousness C1230 for AEPi. CPTUCSSA
Calibrated Patient Transition from Unconsciousness to Consciousness
for SSA. FE Forehead Electrodes EOG Electrooculogram The study of
electrophysiology eye movement surface electrode signals (which
show rapid activity with WAKE and REM sleep stages). EEG
Electroencephelogram The study of electrophysiology surface
electrode signals (electrical muscle energy, which decreases with
sleep state). EMG Electromyography The study of electrophysiology
eye movement surface electrode signals (which show rapid activity
with WAKE and REM sleep stages). SPL Sound Pressure Level C
Consciousness U Unconsciousness TSW Transition from Sleep to Wake
S1W > S SSA Stage 1 Wake to Sleep Bme Body Movement Event
Detection of Body Movement (BM) relates to a physical movement of
the body such as detected by a pressure or vibration sensitive
sensors. Bmi Body Movement index Ae Arousal event Arousal refers to
physiological events as can be cause by the Central Nervous System
(CNS), and may not always constitute a body movement detection. Ai
Arousal index DZ Display Zone Display Zones (DZ) of display
represents the zones of the ADMS display where defined phases or
states can be measured. DZCT The critical Display Zones Critical
Threshold (DZCT) of display represents the values which are desired
to be displayed in such a manner that the user has an expanded
viewing range (on meter display, for example) compared to less
critical display zones. In the present invention the ability exists
to define these said "critical display zones" and in particular the
critical display zones can change subject to both the context of a
subjects current and past states of conscious/wake or
unconscious/sleep. CD Current Data CDAEPi Current Patient Data AEPi
(Value) IDAEPi Impirical Data AEPi (Value) CDTCUAEPi Current Data
for TCU of AEPi. Current Data refers to latest analysed real time
data value. CDTCUBi Current Data for TCU of Bi. Current Data refers
to latest analysed real time data value. ID Impirical Data IDAEPi
Impirical Data value for AEPi IDBi Impirical Data value for Bi
CDTCUSSA Current Data for TCU of SSA. Current Data refers to latest
analysed real time data value. CPTUCSSA Calibrated Patient for
Transition from Unconsciousness to Consciousness for Sleep Staging
Analysis. CDTUCAEPi Current Data for TUC of AEPi. Current Data
refers to latest analysed real time data value. CPTSWAEPi
Calibrated Patient for Transition Context State from Sleep to Wake
for AEPi. DZTF Display Zone Transition Formula Zone A Patient
emerging from Consciousness to Unconsciousness. Zone B Patient in
unconscious state. Zone C Patient in unconscious state. Zone D
Patient Transition from Unconsciousness to Consciousness. CA1W >
S Context Analysis change from WAKE to (sleep-stage 1, 2, 3, 4 or
REM) ref 3, 4, 8, 9 CA2W > S context Analysis Change from
sleep-stage 1 to (2 OR 3 OR 4 OR REM) ref 3, 4, 8, 9 CA3W > S
Context Analysis Change from sleep-stage 2 to (3 OR 4 OR REM) ref
3, 4, 8, 9 CA4W > S Context Analysis Change from sleep-stage 3
to (4 OR REM) ref 3, 4, 8, 9 CA5W > S Context Analysis Change
from sleep-stage 4 to REM) ref 3, 4, 8, 9 CA6S > W Context
Analysis Change from sleep-stage REM to (WAKE OR 1, 2, 3 or 4) ref
3, 4, 8, 9 CA7S > W Context Analysis Change from sleep-stage 4
to (WAKE or 1 OR 2 OR 3) ref 3, 4, 8, 9 CA8S > W Context
Analysis Change from sleep-stage 3 to (WAKE OR 1 OR 2 ) ref 3, 4,
8, 9 CA9S > W Context Analysis Change from sleep-stage 2 to
(WAKE OR 1) ref 3, 4, 8, 9 CA10S > W Context Analysis Change
from sleep-stage 1 to WAKE ref 3, 4, 8, 9 W Wake State STG1 Stage 1
of Sleep STG2 Stage 2 of Sleep STG3 Stage 3 of Sleep STG4 Stage 4
of Sleep REM REM Stage of Sleep % Represents start of comments, as
applicable to psuedo coding or lines of program code. IDOA
Impirical Data Offset applied for zone A IDOB empirical Data Offset
applied for zone B IDOC Impirical Data Offset applied for zone C
IDOD Impirical Data Offset applied for zone D IDC Impirical Data
Consciousness. IDU Impirical Data Unconsciousness. BM-Mz Body
Movement Multi-zone sensor AEPiTF Audio Evoked Potential Transition
Formula BiTF Bicoherence index Transition Formula SSATF Sleep
Staging Analysis Transition Formula. EESM Electronics Electrode and
Sensor Module
* * * * *
References