U.S. patent application number 11/101878 was filed with the patent office on 2011-08-11 for method and apparatus for maintaining and monitoring sleep quality during therapeutic treatments.
Invention is credited to David Burton, Eugene Zilberg.
Application Number | 20110192400 11/101878 |
Document ID | / |
Family ID | 32094335 |
Filed Date | 2011-08-11 |
United States Patent
Application |
20110192400 |
Kind Code |
A9 |
Burton; David ; et
al. |
August 11, 2011 |
METHOD AND APPARATUS FOR MAINTAINING AND MONITORING SLEEP QUALITY
DURING THERAPEUTIC TREATMENTS
Abstract
The present invention monitors and interprets physiological
signals and spontaneous breathing events to detect the onset of
arousal. Once the onset of arousal is determined, the present
invention determines adjustments that are needed in the operation
of a therapeutic device to avoid or minimize arousals. In one
embodiment, the present invention includes one or more sensors
which detect a patient's physiological parameters, a controller
which monitors and determines the onset of arousal based on the
physiological variables received from the sensor, and a therapeutic
treatment device which is controlled by the controller. The sensor
can be a combination of one or more devices which are able to
monitor a physiological parameter that is used by the present
invention to determine the onset of arousal or the onset of a sleep
disorder. The sensors can be integrated into one unit or may
operate independent of the others.
Inventors: |
Burton; David; (Camberwell,
AU) ; Zilberg; Eugene; (Sandringham, AU) |
Prior
Publication: |
|
Document Identifier |
Publication Date |
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US 20050217674 A1 |
October 6, 2005 |
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Family ID: |
32094335 |
Appl. No.: |
11/101878 |
Filed: |
April 8, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US2003/032170 |
Oct 9, 2003 |
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11101878 |
Apr 8, 2005 |
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60417445 |
Oct 9, 2002 |
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Current U.S.
Class: |
128/204.23 ;
128/204.18 |
Current CPC
Class: |
A61M 2230/04 20130101;
A61M 16/101 20140204; A61M 2230/62 20130101; A61B 5/08 20130101;
A61B 5/4818 20130101; A61M 16/0069 20140204; A61B 5/082 20130101;
A61B 5/24 20210101; A61M 2230/10 20130101; A61M 16/0006 20140204;
A61M 2230/205 20130101; A61M 2016/0036 20130101; A61M 16/10
20130101; A61M 2230/60 20130101; A61B 5/145 20130101 |
Class at
Publication: |
128/204.23 ;
128/204.18 |
International
Class: |
A62B 7/00 20060101
A62B007/00; A61M 16/00 20060101 A61M016/00 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 10, 2002 |
AU |
AU 2002951984 |
Claims
1. A method of providing a therapeutic treatment to a patient, the
method comprising: setting a treatment level; monitoring a
physiological parameter that is an indicator of arousal;
determining if the monitored physiological parameter indicates
onset of arousal; and adjusting the treatment level to avoid
arousal.
2. The method of claim 1, and further comprising the step of
determining if an arousal is caused by upper airway resistance
(UAR) or by the treatment level.
3. The method of claim 1, wherein the step of adjusting includes
varying treatment levels until a treatment level is determined
which does not cause arousal.
4. The method of claim 3, and further comprising the step of
storing the treatment level which does not cause arousal.
5. The method of claim 1, wherein the step of monitoring includes
monitoring of cortical or sub-cortical activity.
6. The method of claim 5, wherein the step of determining includes
detecting a shift to alpha or theta EEG activity from a slower
background frequency.
7. The method of claim 1, wherein the step of determining includes
detecting a drop in the pulse transit time (PTT).
8. The method of claim 1, and further comprising computing an index
value from a plurality of monitored parameters and utilizing the
index value to predict onset of arousal.
9. The method of claim 1, wherein the step of determining includes
comparing a patient airflow waveform with a plurality of templates
which depict onset of arousal.
10. The method of claim 1, and further comprising the additional
step of providing a table correlating a combination of
physiological events to an optimum adjustment of the treatment
level which minimizes arousal.
11. The method of claim 10, wherein the step of determining
includes detecting whether an event is associated with central
apnea or obstructive apnea.
12. The method of claim 1, wherein the step of determining includes
utilizing patient specific data to determine thresholds for
arousal.
13. The method of claim 1, wherein the step of determining includes
comparing cortical and subcortical activity to determine periodic
leg movement-related arousals.
14. The method of claim 1, and further comprising the additional
step of determining a patient's sleep state and regulating
treatment based on a patient's sleep state.
15. The method of claim 1, and wherein the step of determining
includes detecting a change in measure of residual carbon dioxide
levels, residual nitric oxide levels, or residual oxygen
levels.
16. The method of claim 1, and further comprising the additional
step of transferring data to a treatment device to enable the
treatment device to determine a treatment parameter which minimizes
arousals or sleep breathing disorders.
17. A method of controlling delivery of a gas to a patient
comprising: monitoring a physiological parameter; delivering gas at
a set level; determining onset of arousal from the physiological
parameter; determining if onset of arousal is caused by upper
airway resistance or gas delivery; and adjusting the level of
delivered gas based on whether the arousal is attributed to upper
airway resistance or gas delivery.
18. The method of claim 17, and further comprising using a forced
oscillation treatment to determine threshold levels for
arousal.
19. The method of claim 17, and further comprising the additional
step of determining the onset of Respiratory Effort Related
arousal.
20. The method of claim 17, and further comprising the step of
determining the onset of obstructive sleep apnea-hypopnea.
21. The method of claim 17, and further comprising providing a
table having a plurality of entries which define an onset of an
event based on a set of values for a plurality of different
monitored physiological parameters.
22. The method of claim 17, and further comprising the additional
step of adjusting the pressure level of the delivered gas based on
an index value derived from at least one of the plurality of
monitored physiological parameters.
23. The method of claim 17, and further comprising the step of
determining sleep quality.
24. The method of claim 17, and further comprising the step of
determining cheynes-stokes respiration.
25. The method of claim 17, and further comprising providing a
table linking a plurality of values from the monitored parameters
to an event, and updating the table to accommodate a patient's
sensitivity.
26. The method of claim 17, wherein the step of determining
includes high bandwidth monitoring of airflow waveforms or pressure
waveforms to determine arousal or sleep breathing disorders.
27. The method of claim 17, wherein the step of determining
includes analyzing pulse transit time, blood pressure, and ECG
signals to determine the onset of arousal.
28. The method of claim 17, wherein the step of determining
includes analyzing a patient's breath to determine a periodic
breathing event.
29. The method of claim 17, and further comprising the step of
determining the existence of oral breathing and wherein the step of
adjusting includes compensating for oral breathing.
30. An apparatus comprising: a sensor for detecting a physiological
signal; a therapeutic device having an adjustable treatment level;
and a controller in communication with the sensor and the
therapeutic device, the controller adapted to detect onset of
arousal from the detected physiological signal and to adjust
treatment level to avoid arousal.
31. The apparatus of claim 30, wherein the therapeutic device is a
gas delivery device.
32. The apparatus of claim 30, wherein the therapeutic device is an
infusion device.
33. The apparatus of claim 30, wherein the therapeutic device is a
cardiac pacemaker system.
34. The apparatus of claim 30, and further comprising a memory
device containing a table correlating the values of a plurality of
physiological parameters to onset of arousal.
35. The apparatus of claim 30, and further comprising a memory
device containing a table correlating onset of arousal with an
appropriate treatment level.
36. The apparatus of claim 30, wherein the controller is also
adapted to predict the onset of obstruction sleep
apnea-hypopnea.
37. The apparatus of claim 30, wherein the controller is also
adapted to perform a test varying treatment level to determine
sensitivity of patient to treatment related arousal.
38. The apparatus of claim 30, wherein the controller is also
adapted to monitor sleep quality.
39. The apparatus of claim 30, wherein the controller is also
adapted to create an index value from a plurality of different
physiological signals and to adjust the treatment level based on
the index value.
40. An apparatus comprising: a plurality of sensors for monitoring
a plurality of physiological signals; a processor adapted to detect
an onset of arousal from the plurality of physiological
signals.
41. The apparatus of claim 40, wherein the sensors include EEG
sensors.
42. The apparatus of claim 40, wherein the sensor includes EMG
sensors.
43. The apparatus of claim 40, wherein the sensors include an
airflow sensor.
44. The apparatus of claim 40, wherein the sensors include an ECG
sensor and an SpO.sub.2 sensor and wherein the processor is adapted
to calculate PTT.
45. The apparatus of claim 40, wherein the sensors include a body
position sensor.
46. The apparatus of claim 40, wherein the processor is adapted to
test a patient's sensitivity to arousal.
47. The apparatus of claim 40, and further comprising a memory
device adapted to store values for physiological parameters that
indicate arousal for a particular patient.
48. The apparatus of claim 40, and further comprising a memory
device, and wherein the processor is adapted to store the
physiological signals in the memory device.
49. The apparatus of claim 40, wherein the processor is adapted to
display raw data and derived indexes from the physiological
parameters according to user selected formats.
50. A gas delivery device comprising: a plurality of sensors for
monitoring a plurality of physiological parameters; a gas delivery
device having an adjustable pressure level; and a controller in
communication with the sensor and the gas delivery device, the
controller adapted to determine onset of arousal resulting from
RERA or from TERA, and to adjust the pressure level of the gas
delivery device accordingly.
51. The gas delivery device of claim 50, wherein the gas delivery
device is a CPAP machine.
52. The gas delivery device of claim 50, wherein the gas delivery
device is a ventilator.
53. The gas delivery device of claim 50, wherein the gas delivery
device is an oxygen concentrator.
54. The gas delivery device of claim 50, wherein the controller is
also adapted to obtain and store patient-specific physiological
parameters which indicate the onset of arousal.
55. The gas delivery device of claim 50, wherein the plurality of
sensors communicate with the controller utilizing a wireless
technology.
56. A drug delivery apparatus comprising: plurality of sensors; a
drug delivery device having an adjustable drug delivery level; and
a controller in communication with the sensors and the drug
delivery device, the controller adapted to adjust the drug delivery
level based on a patient's sleep state.
57. A cardiac pacing system comprising: a plurality of sensors; a
pacing device having an adjustable output control; and a controller
in communication with the sensors and the pacing device, the
controller adapted to adjust the output of the pacing device to
minimize arousal.
Description
FIELD OF THE INVENTION
[0001] Generally, the invention relates to the field of therapeutic
treatments. More specifically, the invention relates to a method
and apparatus for delivering therapeutic treatments to patients
without adversely affecting their sleep.
BACKGROUND OF THE INVENTION
[0002] Many therapeutic treatments are administered to a patient
while they are sleeping or are attempting to fall asleep. While
these treatments may achieve their intended result, they also often
severely affect the quality of sleep that the patient gets while
undergoing these treatments. These treatments often interrupt the
patient's normal progression of sleep, causing transient arousals.
While these arousals do not result in the awakening of the patient,
they often pull patients from deeper stages or higher quality
states of sleep. Patients often do not reenter these deeper stages
of sleep for a relatively long period of time.
[0003] In some instances, a therapeutic treatment may cause
numerous arousals. This fragments the patient's sleep and prevents
the patient from reaching the deeper stages of sleep. Studies have
shown that fragmented sleep results in excessive daytime
sleepiness. This, in turn, is a direct contributor to many
accidents, to a general feeling of lethargy, deterioration of
cognitive performance, and/or daytime sleepiness, in the
patient.
[0004] One example of therapeutic treatments causing sleep
fragmentation is in the treatment of sleep disorders. Continuous
Positive Air Pressure (CPAP) treatments are a primary remedy for a
number of sleep disorders such as sleep apnea, hypopnea, and
snoring. CPAP treatments consist of delivering a constant positive
airway stream of air pressure into a patient's airway during sleep
in order to keep the patient's airway from collapsing upon itself.
State-of-the-art CPAP machines, often called auto-titration PAP
(APAP) machines, automatically adjust the pressure of the delivered
air in order to accommodate a patient's respiratory pattern. to the
rapid changes of pressure in the patient's airway caused by the
APAP machines. Another drawback of current state-of-the-art APAP
machines is that they are subject to either false positives (such
as when UAR and/or natural irregular breathing events are not
pre-empted or do not occur, despite false detection of such and
associated treatment control change) or false negatives (such as
when genuine upper airway resistance (UAR) and/or related events
are pre-empted or do occur but are not detected or responded to
with treatment control change). This is due in part to the reliance
of these machines on the correct interpretation of an inspiratory
waveform and the inaccuracies related to the interpretation of the
underlying waveform by the APAP machine. This can also be due to
current state of the art gas delivery (or other treatment control
such as pacemaker devices) devices inability to enable suitable
algorithms to detect and adapt their computation detection
sufficiently to pre-empt or predict the probability or onset
likelihood of shallow breathing, UAR, arousals, and or associated
sleep fragmentation or sleep quality deterioration.
[0005] The inspiratory waveform varies periodically for reasons not
always associated with upper airway resistance. The use of
inspiratory waveform as the primary or only means of detection of
UAR-related events can cause remedial auto titration measures to be
taken when none should be. This is particularly evident where the
inspiratory waveform analysis technique does not employ an
underlying time-course computational method. The time-course
computational method refers to comparing a previous sequence of
breaths (prestored from previous treatment session or stored from
current session breathing data) or the current breath and comparing
the variations or changes as an inferred measure of arousal or
sleep fragmentation onset. Excessively rapid or excessively
insensitive pressure changes often occur when an auto-CPAP machine
tries to correct a normal non-UAR related event, or misses
detecting the presence of subtle shallow breathing, hypopnea or
UAR, respectively. It is believed that the primary cause of sleep
fragmentation is the rapid pressure changes in the patient airway
produced by the current APAP machines.
[0006] In addition to the above, studies have also suggested that
some APAP machines are limited in their ability to accurately
detect the onset or incidence of shallow breathing, mild hypopnea,
or UAR events. This limitation is also possibly attributed to
limitations of the machines in interpreting the wave form.
Misdiagnosis of such mild hypopnea events results in increased UAR
which in turn results in arousal and subsequent sleep
fragmentation.
[0007] Current state-of-the-art therapeutic devices do not
optimally adapt to minimize arousals during therapy. Each patient's
arousal threshold is affected by varying parameters, yet current
state of the art devices do not have adaptive control algorithms
that can adapt their treatment levels to accommodate a number of
these varying parameters. These varying parameters include (but are
not limited to) sleep history such as sleep deprivation or sleep
propensity, physiological factors, psychological factors including
(but not limited to) stress or anxiety, environmental factors
including temperature; noise; lighting; vibration, factors such as
varying threshold to arousals with changing age, drugs and alcohol
effects to arousal thresholds and others.
[0008] Consequently, in light of the inherent drawbacks in current
therapeutic methods for administering treatments to patients who
are sleeping or are attempting to sleep, there exists a need for an
apparatus and method of monitoring for patient arousal and for
adapting a therapeutic treatment to minimize arousal.
SUMMARY OF THE INVENTION
[0009] For the purposes of explanation only, the present invention
is described primarily in the context of controlling delivery of
gas to a patient. One skilled in the art can readily appreciate
that the present invention is readily adaptable for use with other
therapeutic treatments. The said therapeutic treatments can include
ventilatory support or assist devices, oxygen therapy devices or
pacemaker devices. As such, it is not intended that this invention
be limited to the control of gas delivery.
[0010] The present invention is capable of maintaining the sleep
quality of a patient undergoing a therapeutic treatment by
sensitizing the therapeutic device to various physiological
indicators which predict the onset of arousal and using an adaptive
algorithm to modify a patient's therapeutic treatment. The
therapeutic control algorithm of the present invention has the
capability to be adapted during real-time operation based on any
combination of a) empirical clinical data, b) individual patient
collected or alternative (to laboratory) collected data (from
diagnostic study within sleep laboratory or other alternative site)
or c) real-time monitored and analyzed data.
[0011] In one embodiment, the present invention has a capability to
apply empirical clinical data to establish standard threshold
configurations, which in turn determine a therapeutic device's
response and performance given the current condition of the
patient. In the case of a gas delivery device, parameters such as
the rate of pressure change, the absolute amount of pressure
change, the minimum delivered pressure values and the maximum
delivered pressure values can be used. In order to minimize
arousals while maintaining the integrity of the treatment, these
rates and absolute pressure changes are adjusted in accordance to
various patient states including (for example only) the patient's
current sleep state or the patient's relative blood pressure or
arrhythmia detection. The present invention can be configured to
rely on a fixed set of reference data designed to predict the onset
or detect the occurrence of arousal.
[0012] In one embodiment, the present invention is capable of
operating with or without any previous patient data. In the case
where a subject has no previous data or threshold indications, the
present invention could commence operation with standardized
empirical data threshold settings. During device generated pressure
changes, or whenever there is a respiratory disturbance or
prediction of onset of a respiratory disturbance, the present
invention can adapt its control characteristics to minimize the
respiratory and arousal disturbance. Control characteristics refer
to the rate and absolute pressure changes delivered to a subject
together with the devices sensitivity to detect subtle hypopnea,
shallow breathing, or UAR. Respiratory disturbance, arousal or
upper airway resistance can be detected with an airflow shape
monitor, or more comprehensive combinations of physiological
monitored channels. In the simplest configuration the present
invention would record and note the likelihood of arousal or upper
airway flow limitation by way of the shape characteristics of the
airflow signal (as derived from a breathing mask circuit). This
detection of waveshape characteristics could be achieved by
detecting changes in the sequence (1 or more) breathing waveform
shapes and then associating these changes with the onset
probability or actual incidence of hypopnea, shallow breathing or
UAR.
[0013] In one embodiment, the present invention includes an
algorithm for detecting variation in airflow shape that could be
indicative of the incidence or probable onset of upper airway
resistance (UAR) or variations of UAR, respiratory event related
arousals (RERA) or treatment event related arousals (TERA). These
airflow shape variations (and others) can be detected in the
breathing mask of a patient undergoing CPAP, oxygen concentration,
ventilation or other gas delivery or ventilation support. The
detection capability of airflow shape variations enable the present
invention to adopt analysis techniques such as neural networks or
other methods that are capable of adopting self-learning and
algorithm adaptation techniques.
[0014] In one embodiment, self-learning and adaptation techniques
are specifically applicable to the detection of RERA and TERA. RERA
and TERA can be detected by monitoring cortical or subcortical
activity or by detecting airflow wave shapes associated with
generation of such RERA's. Alternatively, airflow and shape only
analysis methods can be adopted.
[0015] In one embodiment, the present invention is adapted to
detect UAR, RERA, and TERA in a patient using physiological
parameters such as pulse transit time (PTT) pulse arterial
tonometry (PAT), plethysmographic wave amplitude,
electroencephalogram (EEG), electro-myogram (EMG) and
electro-oculogram (EOG), to name a few.
[0016] Utilizing these techniques, a gas delivery pressure device
(oxygen concentrator, ventilator, VPAP, CPAP, APAP and others) can
predict the UAR, RERA and TERA events or the onset of such events
and adjust the treatment to avoid such events.
[0017] In one embodiment, the process of detecting and monitoring
for arousals could occur simultaneously or in virtual real-time
with automated gas delivery treatment algorithms which are able to
adapt to reduce or eliminate both sleep breathing disorders and
sleep fragmentation. The present invention is able to recognize
when the pressure adjustment of the gas delivery device is either
too severe and leading to the promotion of RERAs or TERAs or avoid
the failure to compensate for less obvious (without comprehensive
shape analysis and possibly patient specific calibration) or more
subtle SBD such as UARs, hypopnea events, and shallow
breathing.
BRIEF DESCRIPTION OF THE DRAWINGS AND FIGURES
[0018] For purposes of facilitating and understanding the subject
matter sought to be protected, there is illustrated in the
accompanying drawings an embodiment thereof. From an inspection of
the drawings, when considered in connection with the following
description, the subject matter sought to be protected, its
construction and operation, and many of its advantages should be
readily understood and appreciated.
[0019] FIG. 1 is a schematic diagram of one embodiment of the
present invention.
[0020] FIG. 2 is a schematic diagram of the arousal monitoring
functions of the present invention.
[0021] FIG. 3 is a flowchart of the airflow diagnostic process for
the present invention.
[0022] FIG. 4 is an example of a waveform for inspiration cycle
with snoring.
[0023] FIG. 5 is an example of a waveform for an inspiration with a
UAR.
[0024] FIG. 6 is a flow diagram of one embodiment of the present
invention.
[0025] FIG. 7 is a schematic diagram of one embodiment of the
present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
A. General Overview
[0026] The present invention is an apparatus and method for
maintaining the sleep quality of a patient undergoing a therapeutic
treatment. The present invention monitors and interprets
physiological signals and spontaneous breathing events to detect
the onset of arousal. Once the onset of arousal is determined, the
present invention determines adjustments that are needed in the
operation of a therapeutic device to avoid or minimize
arousals.
[0027] As shown in FIG. 1, in one embodiment, the present invention
includes one or more sensors 10 which detect a patient's
physiological parameters, a controller 12 which monitors and
determines arousal based on the physiological variables received
from the sensor, and a gas delivery apparatus 14 which is
controlled by the controller 12. The sensor 10 can be a combination
of one or more devices which are able to monitor a physiological
parameter that is used by the present invention to determine the
onset of arousal or the onset of a sleep disorder. The sensors can
be integrated into one unit or may operate independent of the
others.
[0028] In one embodiment, the present invention is adapted to
determine arousal using physiological parameters such as pulse
transit time (PTT) pulse arterial tonometry (PAT), plethysmographic
wave amplitude, electroencephalogram (EEG), electro-myogram (EMG)
and electro-oculogram (EOG), to name a few.
[0029] In one embodiment, the present invention is also adapted to
monitor, analyze, and compute the sequence of airflow shape and
sound. The breathing waveform profiles or sequence of waveform
profiles or sounds of a patient are matched to various templates
which are correlated to specific arousal events or Sleep Breathing
Disorders.
[0030] In one embodiment the presence of SBD, UAR, shallow
breathing or the onset of the same, can be analyzed and
computed.
[0031] In one embodiment, the present invention receives a
plurality of inputs from sensors and matches the inputs to values
listed in a plurality of tables. The tables identify various
breathing waveform profiles and physiological parameters or
sequence of waveforms and physiological data and matches this
information to a particular arousal event or sleep breathing
disorder. Furthermore, a number of coefficients and equations can
be applied to the values stored in the table in order to
accommodate variations which are patient specific.
[0032] In one embodiment, the present invention has a capability of
operating in three different modes. One mode is a default mode
wherein empirical data establishes thresholds and reference data
used to compute optimal therapeutic control. The present invention
also includes a calibration mode wherein the present invention
tests the response of the patient to various settings in order to
determine patient tolerances. The present invention also includes
an adaptation mode wherein the present invention utilizes optimal
therapeutic control in order to minimize or eliminate arousal
events or SBD.
B. System Configuration
[0033] In one embodiment, the present invention includes three main
components, a sensor for monitoring a physiological parameter, a
therapeutic device for administering a therapeutic treatment, and a
controller for controlling the delivery of the therapeutic
treatment. The present invention is described as having three main
components for the purposes of explanation only. One skilled in the
art can readily appreciate that the three main components of the
present invention can be readily integrated into one or more
devices.
[0034] In one embodiment, the present invention includes a number
of sensors, some of which are used to detect upper airway
resistance and airflow and some of which detect physiological
parameters which are used to determine arousal. The sensor can be
any apparatus known in the art which is capable of detecting,
measuring, or calculating a physiological parameter which is used
to determine arousal. The sensor can be comprised of a single
integrated machine or a plurality of independent ones. The sensors
can communicate with the controller by any known protocol.
[0035] In one embodiment, pressure transducers and a
pneumotachograph are used in cooperation or integration with an
airtube or a patient mask to detect patient airflow and airway
pressure. To detect physiological parameters, the present invention
uses sensors such as, but is not limited to, EEG, EOG, EMG, ECG,
pulse oximetry, blood pressure, carbon dioxide monitoring, bed
transducers for monitoring patient position, video processing
systems and microphones for breathing and breathing sounds.
[0036] Preferably, the sensors are all incorporated onto a single
patient mask. A suitable mask is disclosed in International
Publication Number WO 01/43804 entitled "Bio-mask with Integral
Sensors," the contents of which are hereby incorporated by
reference in its entirety. The mask has sensors integrated therein
which are capable of detecting EMG, EEG, EOG, ECG, surface blood
pressure, temperature, pulse oximetry, patient sounds, and gas
pressure in the mask. The mask can include side-stream or
full-stream gas sampling capability for monitoring in real-time,
concentration of oxygen, CO.sub.2, nitric oxide and other gases or
any combination of the aforesaid gases. In addition, the mask
serves as the conduit for gas delivery to the patient.
[0037] In one embodiment, a mattress device is used to detect
arousal. Currently, there are two commercially available mattresses
which can perform the above functions. One is known as a Static
Charge-sensitive Bed (SCSB) and the other is a polyvinlidene
fluoride (PVDF-piezoelectric plastic) bed.
[0038] In one embodiment, eye activity is used to monitor arousal.
An infrared video monitoring system is employed as a sensor to
determine eye activity via eyelid position. The image signal from
the video monitoring is processed by graphic processing program to
determine the status of the eyes.
[0039] In one embodiment, the present invention utilizes a unique
multi-standard wireless interface system. Typically, two separate
wireless bands are deployed to separate physiological wireless
signals from control data. Furthermore, integrated encryption and
security may be deployed to avoid unauthorized access to data.
[0040] An example of the typical embodiment could be where the 2.4
GHz ISM band is applied for the interface of wireless based sensors
interfaced to a controller. Less critical data, not effecting the
patient therapy, such as user data viewing and reports, could be
interfaced using W-LAN and even Bluetooth.TM. wireless devices. The
multi-radio standard is a particularly important consideration
where operating with existent wireless systems. A further
capability of the present invention is to detect interference from
similar radio band system and switch the critical signal and other
monitoring either to an alternate band or modify the system
analysis adaptation without wireless signals. The present invention
can be used with a range of wireless electrode devices to enable
easy expansion and access to additional physiological signals.
[0041] In one embodiment, the sensors are battery powered for 1 or
2 days while transmitting signals to the controller. The wireless
monitoring capability enables the present invention to monitor
RERA, TERA, and SBD-related (sleep breathing disorder) signals
during a subject's sleep. Furthermore, the ability to monitor these
electrodes during a subject's sleep might give some augmented
information (in addition to the respiratory airflow, pressure and
sound signals normally derived from the subject's mask) and the
ability of the SPAP system to provide optimal therapeutic pressure
or gas delivery control to minimize RERA or TERA, while also
minimizing obstructive sleep apnea-hypopnea (OSAH) and UARs.
[0042] This comparison of sleep efficiency during routine CPAP or
enhanced (additional wireless signals applied, for example) CPAP
operation can provide valuable information to the healthcare worker
and patient in terms of sleep efficiency options for the patient.
In a similar manner the patient may choose to utilize a wireless
position sensor which could be attached to the therapeutic
breathing mask or other parts of the patient therapeutic equipment
or clothing.
[0043] The said wireless electrode contains several key functions
enabling this wireless technology to be used with relative
trouble-free ease within the patient's home or the clinical
environment alike. The present invention electrodes can be packaged
such that the removal of the disposable electrode outer package
activates the battery. This automatic wireless electrode activation
function enables automatic preservation of the battery life,
particularly during storage. Use-by dating of the disposable
electrode packaging ensures that both the electrode quality and
battery life is used within a suitable period of time, protecting
the user from battery age deterioration and electrode
deterioration. The wireless interfaced electrodes of the present
invention can be provided with self-gelled properties to simplify
electrode attachment. The disposable self-adhesive (or reusable)
electrode systems can be attached by the patient using simple
visual guides.
[0044] The sensors input their physiological data to the controller
(incorporates pre-processing required for treatment control), which
receives the data and determines arousal or onset of arousal. In
one embodiment, the controller includes an analog processing
circuit which converts analog signals from the sensors into a
digital signal. The analog processing circuit utilizes known
preamplifying, amplifying, conditioning, and filtering
configurations to enable the analog sensor signal to be converted
into a digital signal. In some instances, the sensor may directly
input a digital signal.
[0045] In one embodiment, the controller also includes a processor
which receives the digital signal and determines the patient state
and an appropriate setting for the gas delivery or other
therapeutic device. The processor employs a plurality of tables
stored in a database. The tables include a plurality of entries
which correlate the inputted signal from a sensor with arousal.
Typically, a number of different physiological parameters are
inputted simultaneously and all of the parameters are factored in
determining arousal. The processor can employ a weighting system
for each parameter and the appropriate action is determined by the
derived index value. In another embodiment, the processor can link
a chain of physiological values together and compare it to a table
which correlates the linked set of values to arousal.
[0046] In one embodiment, the present invention includes memory
devices containing tables holding stored profiles containing normal
or acceptable limits for physiological parameters such as: [0047]
sleep fragmentation, apnea-hypopnea index (AHI), RERA, sleep
architecture, cortical arousals, sub-cortical arousals, PTT values,
PAT values, HRV values, central sleep apnea (CSA) occurrence, apnea
occurrence, mixed apnea occurrence, hypopnea occurrence, EEG spike
occurrence, EEG spindle occurrence, EEG K-complex occurrence, EEG
seizure occurrence, bi-coherence or bispectral index values,
auditory evoked potential index, patient posture optimal pressure
values, patient sleep propensity, patient sleep state.
[0048] The controller communicates with the therapeutic device to
control the treatment level to the patient. The controller
determines an appropriate instruction set by using treatment levels
found in the table entry corresponding to the patient's
physiological condition. The processor then communicates the
instruction set to the gas delivery apparatus which then executes
the instruction set.
B. Arousal Monitoring
[0049] As shown in FIG. 2, the present invention uses various
physiological inputs to determine arousal in a patient and to
tailor the delivery of air to the patient to minimize such arousal.
Due to the complex and varying states of sleep and broad range of
sleep disorders that can be diagnosed, many different physiological
parameters may be monitored and analyzed to determine arousal.
[0050] The minimization of arousals includes the capability to
automatically adjust the therapeutic treatment while monitoring at
least one physiological parameter or signal where the monitored
physiological parameter(s), signal(s) or measures can include (but
are not limited to): [0051] Blood pressure, patient movement,
patient vibration, patient tremor, patient shake, Pulse oximetry,
pulse-wave, EEG, EOG, EMG, patient position, patient movement,
breathing sounds, airflow signal, respiratory effort signal(s),
pharyngeal pressure signals, expired PCO.sub.2 signal,
diaphragmatic EMG, transthoracic impedance, electrocardiogram
(ECG), reflective oximetry, pulse oximetry, oxygen saturation,
nasal pressure, airflow pressure, breathing mask airflow, breathing
mask pressure, breathing mask sound, breathing sound, breathing
pressure, respiratory inductive plethysmography,
plethysmography-wave, oesophageal pressure, nasal cannular sensor
signals, nasal and oral cannular sensor signals, oral cannular
sensor signals, thermocouple sensor signals, thermistor sensor
signals, PVD temperature sensor signals, PVD sound and vibration
sensor signals, PVD breathing or airflow signals, Pneumotach
calibrated flow, or other routine or research application of
polysomnograph (PSG) monitoring sensors electrodes or signals.
[0052] In one embodiment, arousals are monitored using an EEG.
Typically, the forebrain is monitored to determine cortical
arousals and the brainstem is monitored to measure subcortical
arousals. The onset of arousal is characterized by bursts of higher
frequency EEG signals or a shift to alpha or theta activity from a
slower background frequency, and, occasionally, transient increase
in skeletal muscle tone. Standard EEG electrode placements and
protocols may be used to measure arousals.
[0053] In one embodiment, the present invention includes the
capability to distinguish a periodic leg movement (PLM) related
arousal from a respiratory related arousal. Distinguishing PLM
arousals from arousal associated with respiratory events can be
important, particularly where optimum treatment control may not
respond to a PLM related arousals but may need to respond to a
respiratory event related arousal.
[0054] The present invention detects and distinguishes PLM and/or
PLM arousals by means of comparing sub-cortical arousals inferred
from blood pressure variations with cortical arousals (EEG).
Cortical arousals are used to distinguish sleep-fragmentation and
neurological related arousals versus sub-cortical arousals which
generally include both sleep-fragmentation and neurological related
arousals and PLM related arousals.
[0055] In one embodiment, the onset of arousal is determined using
Pulse Transit Time (PTT). Studies have shown that sleep disorders
such as apnea, hypopnea or upper airway resistance result in an
accompanying arousal, and this arousal is accompanied by changes in
heart rate, a transient burst of sympathetic activity, and a surge
in blood pressure. Obstructive sleep apnea can be correlated with
an obvious and measurable increase in intrathoracic pressure
associated with obstructive effort and cardiobalistogram effect.
The cardiobalistogram effect is created when the lungs apply
pressure to the heart. This compresses the heart and reduces the
volume of blood pumped by the heart. These cardiovascular changes
are recognizable by way of a transient but significant dip in the
patient's baseline PTT value.
[0056] PTT is the time taken for the pulse wave to travel between
two arterial sites. The blood pressure is directly proportional to
the speed that the arterial pressure wave travels. A rise in blood
pressure relates to faster pulse wave and thus shorter PTT.
Conversely, a drop in blood pressure results in a slowing of the
pulse wave and an increase in PTT.
[0057] In one embodiment, PTT is obtained using sensors located on
the above-mentioned bio-mask. A sensor receives input from the mask
and generates a plethysmography waveform. A second sensor receives
input from the mask and generates an ECG signal. The waveform and
the signal are inputted into the controller and a PTT reading is
calculated.
[0058] The PTT is derived by utilizing a plethysmography waveform
obtained by using pulse oximetry techniques in combination with an
ECG signal. In one embodiment, the ECG R or Q wave can be used as
the start point for the PTT measurement and the end point of the
PTT measurement can be the point representing 25% or 50% of the
height of the maximum pulse wave value.
[0059] In one embodiment, EMG measurements are used to detect
levels of sleep in a patient. EMG monitoring enables the present
invention to detect sleep-related changes in a patient's muscle
tonicity. Sleep states will typically be accompanied by changes of
tonicity in certain muscles. Arousals will typically result in
increased muscle tonicity.
[0060] In one embodiment, ECG and an EMG signal from the diaphragm
are monitored in combination to detect respiratory effort
associated with central apnea versus obstructive apnea. The ECG
electrodes are configured on the patient in order to distinguish
diaphragm related respiratory effort from thoracic respiratory
effort. During central apnea, there will be a cessation of
breathing without respiratory effort. This is distinctly different
from obstructive apneas wherein muscle activity increases as a
result of increased breathing effort to overcome the obstructed
airway.
[0061] In one embodiment, a patient's eye movements are monitored
to assist in determining arousal. One technique involves the use of
digital video recording and known graphic processing techniques to
determine eye lid activity (i.e. whether the eye lids are closed,
open, or degree of openness).
[0062] In one embodiment, arousals are detected by monitoring the
presence of waveform signal disturbance evident on a high bandwidth
analysis (DC to 200 Hz or higher bandwidth) of the airflow waveform
and pressure waveform obtained within a breathing mask. Apnea
events, shallow breathing, upper airway resistance and hypopnea
events can also be detected and preempted by analysis of the change
in shape of the high bandwidth monitoring of the airflow waveforms
and pressure waveforms.
[0063] In addition to monitoring arousal, in one embodiment, other
physiological parameters may be monitored to determine the
patient's physical state. The present invention can utilize sensors
in the biomask to determine heart rate, ECG, respiration rate,
snoring sounds, airflow, air pressure, and O.sub.2 saturation.
Conventional methods may also be incorporated into the present
invention to monitor blood pressure, and CO.sub.2. The patient's
sleeping position may also be monitored using pressure transducers
or a mattress device.
[0064] In one embodiment, wherein a patient is undergoing CPAP
treatment, arousal monitoring also includes monitoring pressure and
airflow associated with a patient's breathing in order to determine
UAR (which may induce RERA). To prevent RERA, it is necessary to
detect a number of patterns which are indicative of sleep apnea
symptoms, namely inspiratory flow limitation (flattening), snoring
and flow amplitude reduction (hypopneas and apneas). As detailed in
FIG. 3, the present invention analyzes the airflow to and from the
patient in order to determine the existence of UAR.
[0065] A "Breath detection" component performs real time detection
and characterization of individual breaths. Detection of
inspiration and expiration peaks includes "local" smoothing (to
separate real breaths from noise) and "global" detection of
respiration peaks based on relatively long context which may
include up to six consecutive breaths. Breath analysis includes
accurate detection of inspiration interval and characterization of
flow during inspiration, namely indices of flattening, snoring and
inspiratory amplitude as well as a few others.
[0066] A "Time interval based processing" component performs
analysis of pressure and flow derived signals based on expiration
of time intervals rather than breaths. It is necessary in cases
when breaths are not discernible such as apneas or when the mask
comes off the face.
[0067] The controller generates pressure adjustment signal on the
basis of per breath and per time interval information provided by
the two above components. The controller is implemented as a
collection of rules which cover various combinations of indicators
of flow limitation, snoring, breath amplitude, pressure leak and
other parameters.
[0068] In one embodiment, the main strategy in breath detection is
to use maximum and minimum points (flow signal level) as the
indicators of inspiration and expiration intervals. Inspiration is
associated with positive flow signal deflection and expiration is
associated with the negative deflection. However, the flow signal
could be contaminated by large amount of noise, and it is necessary
to smooth flow data before detecting actual breath patterns (box
10050). For accurate detection of the inspiratory and expiratory
peaks it is also necessary to use a relatively long context to
prevent confusing them with local maxima and minima in the flow
signal.
[0069] There are two main tasks in the local smoothing. First, all
local maximum and minimum points from the flow signal are detected,
and each maximum point is defined as an initial candidate for the
location of an inspiration peak, and each minimum point for an
initial candidate for possible location of expiration peak. The
second task is to smooth some maximum and minimum points with
"relatively small amplitude", which are likely to be noise signal.
As a result of local smoothing, only maximum and minimum points
with "relatively large amplitude" are retained, and the flow signal
is considered to be sufficiently smoothed. This sequence of local
smoothing be described as follows: [0070] 1. Detect all local
maximum points from a set of flow data. [0071] 2. For each maximum
point, form a pattern called max-peak, in which the maximum point
is located in the center, and data in its left side increase
monotonously, and decrease monotonously in its right side. For the
current flow data set, obtain a set of max-peak patterns. [0072] 3.
For the same data set, detect all local minimum points and obtained
a serial of min-peak patterns using the similar method. [0073] 4.
Calculate a number of parameters such as signal variation and
duration for each max-peak and min-peak, and these parameters are
used as the measurements to test whether some of detected max-peak
and min-peak patterns are in fact noise. [0074] 5. Analysis
sequences of adjacent max-peaks and min-peaks (in every sequence
the number of max-peaks should exceed the number of min-peaks by
one or alternatively the number of min-peaks should exceed the
number of max-peaks by one) and check if a sequence could be
approximated by a single max-peak or min-peak so that an
approximation error is significantly less than variation and
duration parameters of a resulting max-peak or min-peak [0075] 6.
For the noise signal smoothing, use piecewise linear methods to
approximate the flow signal. [0076] 7. The max-peaks with relative
large amplitudes are retained, and for each "retained" max-peak,
both `increasing period` (left side) and `decreasing period` (right
side) are not shorter than a pre-defined threshold (0.75 s). [0077]
8. Same method is applied to min-peak smoothing processing.
[0078] The local smoothing is basically designed for excluding
noise signals that have a relatively small amplitude and short
duration. As a result, a large amount of maximum and minimum points
can be excluded from a list of "candidates" for inspiration and
expiration peaks. The local smoothing processing can form separate
"increasing periods" or "decreasing periods", and the signal within
an "increasing period" or a "decreasing period" corresponds to a
"likelihood" of the half duration of inspiration or expiration.
This "half-duration" smoothing processing is one approach for
deleting small noise signal. On the other hand, the "half-duration"
approach lacks capability of smoothing flow data containing some
relatively large noise and artifacts.
[0079] Another difficult problem in breath detection is related to
the change of respiration patterns. The flow signal is often
affected by patients that change their "way" of breathing, in other
words, some periods of the increasing or decreasing signal level
are related to change of patient's respiratory "behavior" rather
than to inspiration or expiration. Local smoothing is unable to
exclude these types of max-peaks or min-peaks. However, it is
possible to use some global measurements to effectively detect
these "unlikely" max-peaks or min-peaks, and this is the idea
behind the global detection of respiratory peaks (global smoothing)
(box 10080). In the global smoothing, multiple consecutive breaths
are checked to further disqualify some max-peaks or min-peaks from
the list of candidates for inspiration or expiration peaks.
[0080] For a relatively long time interval (up to 3.5 minutes),
conditions are tested for pairs of successive max-peaks (min-peaks
as well), and a set of so-called max-pairs (or min-pair) is formed.
Then a number of conditions for series of max-pairs to obtain a set
of max-pairs with similar `patterns`, denoted as max-train are
developed. The same processing is carried out to generate
min-trains out of min-pairs. Therefore there are two main parts in
the global smoothing, namely generation of max-pairs (min-pairs)
(box 10100) and max-trains (min-trains) (box 10110). The following
paragraphs outline max-pair and max-train processing briefly
(min-pair and min-train generation employ the same respective
methods).
[0081] From the starting point of the max-peak set, a pair of
max-peak patterns is determined, which must meet the following
conditions: [0082] (1). The duration between two max-peaks must be
longer than the minimum duration of a breath (0.75 s). [0083] (2).
The duration between two max-peaks must be shorter than the maximum
duration of a breath (10 s). [0084] (3). There is not any
intermediate max-peak within a max-pair. An intermediate max-peak
pattern is defined as that the signal level of the maximum point in
a intermediate max-peak pattern is larger than 80% of signal level
of the maximum point in the max-pair itself. This search processing
is carried out through the whole max-peak pattern set to obtain a
sequence of max-pairs. [0085] (4). For each max-pair, a number of
statistical measurements are calculated, and these measurements are
based on the difference between the original flow signal and the
approximation lines within each max-pair.
[0086] There are two main outputs in this processing, one is a set
of max-pairs which is one step closer to the final set of
inspiratory peaks, and another is a number of statistical
measurements which is used to represent the "shape" of the
max-pair. In the subsequent max-train processing, we will rely on
these statistical measurements to carry out `similarity` test.
[0087] The main method used in the max-train processing is called
"similarity" test, i.e., we measure the "similarity" within a
sequence of max-pairs to form a sequence of max-pairs with
"similar" pattern, denoted as max-train. Only the max-pair that
passes a "similarity test" can be included into the max-train. The
idea behind the max-train is that [0088] i. A sequence of normal
breaths over a successive period of time (3-6 breath durations)
should have similar shapes, and this pattern should not be changed
significantly over a short period of time as well. [0089] ii. If a
max-pair is not similar to this normal breath pattern, it could be
rather like a respiratory event (apnea or hypopnea), or some
noise/artifact pattern in flow signal. The brief algorithm of
max-train processing is then as follows: [0090] 1. Each candidate
max-pair must first meet minimum duration requirement that is
defined as the distance between candidate and the reference
max-pair. [0091] 2. Starting from each single candidate, we
calculate a number of parameters such as duration, variation of
signal level, the shape of max-peak (or min-peak). We then
calculate some statistical measurements for this group of
candidates such as mean, deviation, average and maximum error for
all the elements to check the similarity among of these candidates.
[0092] 3. If the condition of the similarity is met, the group of
max-pair is formed as a max-train (box 10120). Otherwise, the
processing is moved into next max-pair until all max-pair are
checked. [0093] 4. The same method is applied to the min-train
processing. [0094] 5. Using max-train and min-train sets, we are
now able to detect the locations of global maximum points of flow
signal level that are related to the inspiration periods, and a
number of minimum point that is associated with the expiration
periods.
[0095] The global max-peak and min-peak arrays provide estimated
locations of each breath, i.e., inspiration and expiration peaks.
In order to detect respiratory events, one needs to closely look at
these breaths, which includes: [0096] 1. Detect the start and end
points of inspiration interval (box 10130). [0097] 2. Perform flow
flattening (box 10150) and snoring analysis (box 10160) as well as
calculation of other breath parameters.
[0098] During smoothing processing a linear approximation method is
used to smooth flow data except of maximum and minimum points in
max-train and min-train data sets. However, for purpose of breath
analysis `recover` raw flow data using maximum and minimum points
as references is needed prior to carrying out breath analysis
processing.
[0099] There are two steps involved in detecting inspiration,
namely estimation and fine-tune processing. For the inspiratory
interval estimation, the assumption is that the amount of in-taking
flow during inspiration period should be same as that of
`expiring-out` flow during expiration period. Using the maximum and
minimum points in flow signal as references we estimate the
interval of inspiration, i.e., the start and end points of
inspiration based on calculating the areas of flow data.
[0100] However, this method has inherently two problems that could
effect the accuracy in inspiration detection. Firstly, when the
flow is measured at the mask the amounts of flow during inspiration
period and the followed expiration period may not be the same,
especially when patients use their mouth to breathe, and we call
this problem as "flow imbalance". Secondly, there may be "area
insensitivity" problem. When patients start inspiration the flow
signal level rapidly increases, but the measurement of flow area is
an integration processing that is much slower than the change of
flow signals itself. In other words, the change of flow area is not
sensitive enough to accurately measure the start point of
inspiration where flow signal is changed rapidly.
[0101] The flow area is first calculated to estimate the
inspiration interval, which includes the start point and the end
point of an inspiration. The start point of an expiration period is
simply defined as the end point of the previous inspiration period,
and the end point of the expiration period is the start point of
the following inspiration period or can be ignored as this point
does not play any role in our control algorithms. Starting from the
estimated start point inspiration period, linear approximation
methods to detect the "break point" during flow signal increasing
period, and this break point is then defined as the start point of
the inspiration interval. The end point of the same inspiration
period is simply defined as a point at which the signal level is
the same as that of the start point of the inspiration but it has
passed the maximum point.
[0102] As mentioned previously, the present invention needs to
detect three types of respiratory events, namely apneas and
hypopneas, snoring, and inspiration flow limitation. The first type
of events (apneas and hypopneas) is associated with reduction of
inspiration flow and this can be resulted directly from the breath
detection. Both snoring and inspiration flow limitation are more
likely to occur during "abnormal" breath period. For a "normal"
breath, the "shape" of signal on the top of inspiration flow
appears "rounded" and relatively smooth. When snoring is present
the high frequency flow signal is visible during inspiration as
shown in FIG. 4. Inspiration flow limitation is defined as the
event that the patient is unable to generate continuous flow
increase during the first half of an inspiration period. As a
result, the flow signal on the peak of inspiration flow becomes
`flat` as shown in FIG. 5. In flattening analysis, we determine a
reference "flat" line which can be best fitted for the flow signal
on the top of inspiration according the least square error (LSE),
and the difference of the flow signal and the reference "flat" line
during this period is then calculated as a flattening error. There
are a number of flattening errors for different selections of the
reference line. The flattening error with the smallest value is
defined as a flattening index. The flattening index is then used to
measure flow limitation, and the smaller the flattening index, the
more severe is the inspiration flow limitation. A snoring index is
also utilized to indicate the degree of the snoring. The snoring
index is defined as measurement of the amount of high frequency
signal on the top of inspiration flow.
C. Operation
[0103] An operational flow chart of one embodiment of the present
invention is shown in FIG. 6. For the purposes of explanation only,
the present invention will be described in an embodiment which is
adapted for use with a CPAP machine. One skilled in the art can
readily appreciate that the subject invention is easily adapted for
use with, or incorporated within, other known therapeutic
devices.
[0104] The present invention checks to make sure that it is
receiving valid signals from its sensors. (box 2) Once the signal
is verified, the signals are analyzed in order to determine if the
onset of arousal has been detected. (boxes 5, 6, 7, and 8). The
data used in the analysis is determined by the user.
[0105] In one embodiment, the present invention has the capability
to use different forced oscillation treatment (FOT) to determine
patient-specific threshold values for arousals and SBD. Results
from the FOT are used to create templates which are used to
determine the appropriate therapeutic response to avoid the onset
of or eliminate the incidence of CSA, OSA, OSAHS, RERA, and TERA.
(box 3) These templates or profiles are determined from
patient-specific diagnostic studies or the appropriate FOT
treatment at each particular stage in a subject's sleep or
breathing status.
[0106] The present invention can obtain patient-specific FOT
templates and profiles by utilizing forced oscillation of pressure,
or changes in airflow pressure, to determine whether the changes in
the airflow shape resulting from these subtle treatment changes are
able to counteract the shape or profile characteristics indicative
of the incidence or on-set of arousals (TERA or RERA) or OSAH and
UAR. The present invention can vary the pressure change value and
rate of change to countermeasure such events.
[0107] The present system provides a means to down-load from sleep
laboratory studies or other types of previous sleep, respiratory
and/or cardiac related investigations. The specific data is
associated with a subject's breathing and sleep arousal parameters
and is used to customize a gas delivery device to be more sensitive
and accurate for both minimizing incidence of UARS, OSAHS, RERAs
and TERAs, while still minimizing sleep fragmentation and
optimizing sleep quality. (box 23) Each patient has a unique
respiratory breathing circuit and associated pathways. Subsequently
breathing waveforms during all stages of sleep of a patient will
vary from patient to patient. The present invention ability to
accommodate the patient's personal empirical data provides a means
to produce more sensitive and effective treatment algorithm.
[0108] In one embodiment, if the onset of arousal has been
determined (block 11), the present invention then determines if the
CPAP has caused a pressure change (box 13) or if the event is
caused by the existence of UAR (box 14). If there was no pressure
change attributed to the CPAP machine, the present invention will
likely determine that the onset of arousal was caused by RERA or
another form of arousal. If there was a CPAP related pressure
change, the present invention would make a determination if the
onset of arousal was attributed to the pressure change or some
other event (box 15). An appropriate remedy will then be selected
based on the based on the physiological signals and the patient
respiratory flow. (box 18)
[0109] In one embodiment, the present invention is able to utilize
the determination to adapt the the empirical data. (box 20) This
enables the present invention to become more acutely sensitive to
the physiological response of the patient.
[0110] The minimization of arousals includes the capability to
automatically adjust the therapeutic treatment based on at least
one index or derived data set wherein the index or derived data set
can include the following: [0111] Upper Airway Resistance (UAR),
Respiratory Effort-Related Arousal (RERA), Therapeutic-control
Event-Related Arousal (TERA), Respiratory Disturbance Index (RDI)
Respiratory Arousal Index (RAI), Apnea-hypopnea index (AHI),
Arousals (Micro-arousals), Arousals (Cortical), Arousals
(subcortical), Arousals (Total), Total Arousal Time, Sleep stage,
REM sleep, Sleep on-set, Body movement, Percentage of arousal
disrupted sleep (breakdown of all disrupted stages), Sleep
efficiency Index, Sleep Fragmentation Index (new-SFI-Total Sleep
Fragmenting arousals per hour), Airflow Shape trend, Airflow Shape
Template Type, Flattening Index, Forced oscillation event, Pressure
change event, Pressure change rate, Pressure change event curve,
Pressure change event maximal and minima, Mixed Sleep Apnea events,
Central Sleep Apnea events, Upper Airway Resistance Syndrome (UARS)
events, Obstructive sleep apnea and hypopnea syndrome (OSAHS)
events, Respiratory Effort-Related Arousals (RERA) with screen
linked qualification of associated respiratory effort arousals,
Therapeutic control Related Arousals (TERA) with screen linked
qualification of pressure changes and arousals, Sleep Quality Index
(new-hourly sleep index factor) Quality associated arousals, Oxygen
Desaturation, Pulse Transit Time (PTT), Pulse Arterial Tone (PAT),
Pulse Wave Amplitude (PWA), desaturation events and SpO.sub.2
artifacts--accurate detection of cascaded desaturations,
desaturations with SpO.sub.2 artifacts inside, SpO.sub.2 artifact
start and end positions, detection sequences of respiratory events
with partial or short recovery, classification of respiratory
events with noisy or poor quality effort signals, detection
episodes of Cheyne-Stokes breathing, Concordinance capability to
allow score comparisons between any two designated data sets,
Pneumotach calibrated flow, Thermal sensor flow, Sum of respiratory
effort signals, EEG Arousals, PTT, Plethysmographic wave,
Transthoracic impedance, Detection and allowance screen grid
highlighting for an expanded set of automatic events to include the
following events, Obstructive Sleep Apnea/Hypopnea event or
syndrome (OSA, OSH, OSAHS), Respiratory effort related arousal,
Central Sleep Apnea (CSA), Central Sleep Hypopnea (CSH),
Cheyne-Stokes breathing, Hypoventilation, Yawn, Unstable breathing
related to sleep state changes or onset of deeper stages of sleep,
Swallowing, Coughing, Spontaneous or irregular but normal shaped
breathing signals, Derived tidal volume (from nasal pressure or
calibrated flow), Derived flow limitation index (from nasal
pressure or calibrated flow), Derived snoring (from nasal pressure
or calibrated flow), Derived diaphragmatic EMG amplitude, Derived
upper airway resistance (from mask pressure, pharyngeal pressure
and calibrated flow), Derived subcortical arousals (from PTT or
pleth wave amplitude), Breathing mask and/or airflow sound analysis
with segmentation into various breathing disorders such as cough,
wheeze, strider, apnea and hypopnea.
[0112] For every selected event (combination of a set of expanded
group of events from above and current set of events), a user is
allowed to select the set of measurement signals and to set the
parameters of detection for the event. This enables the present
invention to use more than one signal at the same time to detect an
event and more than one scenario to detect an event. The following
are examples of defined events:
RERA--
[0113] 1. Break in the flat inspiratory profile after a few flow
limited breaths [0114] 2. Frequency shift in EEG, amplitude
increase in EMG [0115] 3. Subsequent leg movement activity [0116]
4. No pressure augmentation (CTRL signal) Leg Movement Related
Arousal-- [0117] 1. Increase in leg movement activity [0118] 2.
Frequency shift in EEG, amplitude increase in EMG [0119] 3. Break
in the inspiratory profile not necessarily during inspiration
[0120] 4. No pressure augmentation (CTRL signal) Spontaneous
Arousal-- [0121] 1. Frequency shift in EEG, amplitude increase in
EMG [0122] 2. No increase (or after EEG/EMG changes) in leg
movement activity [0123] 3. Break in the inspiratory profile not
necessarily during inspiration [0124] 4. No pressure augmentation
(CTRL signal) Pressure Augmentation Related Arousal-- [0125] 1.
Pressure increase according to the titration algorithm [0126] 2.
Subsequent frequency shift in EEG, amplitude increase in EMG [0127]
3. No increase (or after EEG/EMG changes) in leg movement activity
[0128] 4. Break in the inspiratory profile not necessarily during
inspiration
[0129] The present invention significantly reduces arousal by
restricting the application of pressure treatment until a patient
is in a stage of sleep where this pressure is not experienced or
causes no adverse patient discomfort. Pressure of air delivered to
a patient is ramped up or down depending upon the patient's sleep
state. Pressure is ramped up slowly while physiological parameters
are monitored. Once the physiological parameters indicate the onset
of arousal (microarousal) the pressure is maintained or reduced
until the patient is in deeper levels of sleep enabling continued
ramping up of pressure. Pressure is also ramped downwards
accordingly.
[0130] The controller 12 is implemented as a combination of rules
for pressure change. Every pressure change rule specifies the
magnitude and sign of a pressure change and the allowed range of
pressure values within which the pressure change can be activated
as well a number of additional parameters including time constants,
timeouts and forced oscillation logic. Every pressure change rule
is activated if a respective logical combination of its conditions
is true. In one embodiment, pressure change rules are combined via
logical OR--pressure changes if any single rule in the set of rules
is satisfied. If more than one rule is satisfied the rule with a
higher priority takes precedence.
[0131] Conditions for various pressure change rules represent a
number of physiological scenarios: [0132] Flow limitation
(flattening) over a number of subsequent breaths--pressure increase
[0133] Flow limitation (flattening) and snoring over a number of
subsequent breaths--pressure increase [0134] Snoring over one or
two breaths--pressure increase [0135] Hypopneas--pressure increase
(it is recommended to use additional information such as PTT, band
or mattress signals to discriminate obstructive vs central
hypopneas) [0136] Detection of apnea start--start forced
oscillation [0137] Low level of upper airway conductance with
forced oscillation--pressure increase (obstructive apnea detected)
[0138] No flow limitation (rounded breath shape)--gradual pressure
reduction [0139] Large leak--pressure reduction to 4 cmH2O [0140]
No airflow over 3 minutes--pressure reduction to 4 cmH2O
[0141] The present invention is capable of overcoming varying
arousal dependent factors by applying adaptive algorithm
techniques. The adaptive algorithm technique has the capability to
apply empirical clinical data to establish standard threshold
configurations, which in turn determine a device's response and
performance in terms of gas delivery characteristics. The adaptive
algorithm technique also has the capability to apply a set of
threshold characteristics.
[0142] In one embodiment, these threshold characteristics can vary
parameters such as the rate of pressure change, the absolute amount
of pressure change, the minimum delivered pressure values, the
maximum delivered pressure values. These rates and absolute
pressure changes can vary in accordance to various states of said
patient including (for example only) the patient's current sleep
state or the patient's relative blood pressure or arrhythmia
detection. The present invention can be configured in a
predetermined mode of operation where the algorithm adaptation
function can be disabled and replaced by an algorithm that relies
on a fixed set of reference data designed to predict the onset or
detect the occurrence of TERA and RERA, while minimizing sleep
breathing disorders.
[0143] In one embodiment, the present invention enables medical
specialists to set various thresholds, which may prevent
undesirable medical conditions for each particular patient. For
example, if central sleep apnea is detected in combination with an
increase or undesirable change or measure in ECG, pulse-wave or
arrhythmia, the operation of the gas delivery device is augmented
to stabilize the patient's condition. In some instances this
stabilization may include the immediate cessation of pressure
delivery. During events such as central sleep apnea (cessation of
breathing activated by the brain commands versus airway
obstruction), for example, forced pressure delivery without airway
obstruction may otherwise aggravate the subject's blood pressure or
cardiac function.
[0144] The present invention is capable of operating with or
without any previous patient data (a specific airflow shape
characteristics or various thresholds, for example). In the case
where a subject has no previous data or threshold indications the
present invention could commence operation with standard empirical
data threshold settings. During device generated pressure changes
or whenever there is a respiratory disturbance the present
invention can adapt its control characteristics to minimize the
respiratory and arousal disturbance.
[0145] In one embodiment, monitoring of arousals enables the
subject invention to augment a CPAP's sleep disorder detection
capabilities. False negatives often occur during mild hypopnea
events. There is typically such minimal airflow limitation that
CPAP machines are unable to detect the breathing disorder. However,
such mild events often create sufficient UAR to cause arousal in a
patient. The detection of the onset of such arousals enables the
present invention to initiate a corrective response from the CPAP
unit, even though the CPAP is unable to detect the event.
[0146] In one embodiment, a treatment mode includes utilizing
breathing pattern templates stored in the table to augment current
CPAP settings. These dynamically allocated breathing pattern
templates supplement the CPAP algorithm by changing the control
characteristics of the CPAP unit. The templates satisfy the
particular patient's pressure requirements while optimizing the
patient's sleep and minimizing patient arousal.
[0147] In one embodiment, the present invention enables the
commencement of treatment to be determined by a pre-defined state
of sleep, arousal activity level, and/or pre-determined sleep
disordered breathing activity. One of the difficulties experienced
by patients with existent state of the art gas delivery treatment
devices is the discomfort experienced from the positive air
pressure applied to a patient, while they are attempting to fall
asleep.
[0148] Existent state of the art devices have the capability to
provide a delayed start function. This delay function provides a
time delay before the treatment pressure slowly increases or ramps
up to a prescribed value of start pressure. However, patients are
not always able to predict their sleep onset time as drowsiness of
a patient varies from one night to the next. The concept of a
prescribed delay time can also provide a psychological anxiety, as
the patient is always aware that if they do not succumb to a
satisfactory sleep state in an appropriate amount of time, they
risk experiencing the unpleasant sensation of excessive positive
air pressure during their sleep preparation time.
[0149] The present invention enables the detection of sleep state
and/or arousals as a mean to determine pressure activation.
Treatments are applied only when the patient is in a preselected or
deep state of sleep and subsequently is oblivious to the said
commencement of treatment. The determination of sleep state can be
the methods which are known in the art, including those methods
disclosed in the U.S. Pat. No. 6,397,845, the contents of which are
hereby incorporated in its entirety.
[0150] In one embodiment, a present invention has an integrated
diagnostic and treatment mode where adjustments to the delivered
air are changed in real time (i.e. changes are instantaneously made
depending upon the values of the monitored parameters).
[0151] The present invention's control algorithm has the capability
to be adapted during real-time operation based on any combination
of a) empirical clinical data, b) individual patient collected or
collected data (from diagnostic study within sleep laboratory or
other alternative site or c) real-time monitored and analyzed
data.
D. Alternative Embodiments
[0152] In one alternative embodiment, as shown in FIG. 7, the
present invention is used to deliver medication to a patient.
Previous methods for determining sedative or tranquilizer dosage
requirements for a subject are often estimated on a generalized
patient group or a specific sample patient group.
[0153] A subject's sleep or vigilance propensity is highly complex
and dependent on many parameters. It has been shown, for example
that a person's sleep propensity can be related to sleep
deprivation, alcohol, anxiety, stress, environmental factors, body
mass index, gender, hereditary and other factors.
[0154] The consequence of over-sedation include increased recovery
time, attention deficit risks associated with excessive drowsiness,
increased costs of drugs, and reduction in the quality of life due
to the extended drowsy state of a subject.
[0155] The drug administration can deliver a range of drugs
utilizing methods such as (but not limited to) orally, transdermal,
fluid drip delivery, vapor delivery and gas delivery. Utilizing the
integration of a drug delivery system with the present invention, a
drug dosage can be optimized for a predetermined level or an
appropriate level of drowsiness, vigilance or attention state.
[0156] The user or health-care provider could adjust drug
administration dosage in consultation with the patient and the
monitored patient data.
[0157] A further capability of the present invention is to contain
sensors such as sensitive movement devices that together with
signal analysis (such as but not limited to spectral, phase and
amplitude) can detect shaking, tremors and other signs indicating
appropriate drug usage. In the case of Parkinson's and other
disease types the present system could be programmed to administer,
for example, adequate drugs to minimize tremors and shaking, while
at the same time provide the subject with a degree of vigilance
during the day and sleep quality during the night that is most
conducive to each individual's quality of life requirements or
desires.
[0158] The present invention can be adapted in a number of
configurations with different combination of physiological
recording channels, sensors, analysis, storage and display
capabilities. These capabilities could vary subject to the specific
disease or disorder being treated, along with each subject's
specific health-specialist requirements for information.
[0159] In another alternative embodiment, as shown in FIG. 7, the
present invention includes a pacemaker control algorithm which
minimizes RERA and TERA while optimizing a subject's heart pacing.
The present invention enables the detection of RERA and TERA from
the electrocardiogram or pulse-wave signals, for example.
Alternatively, more comprehensive signals can be deployed. The
present invention enables the conventional optimization of ECG
pacing while at the same time minimizing arousals and sleep
fragmentation. Pacemaker control can also be utilized to assist in
the elimination of some sleep disordered breathing. The present
invention can provide important feedback as to the causation of
sleep fragmentation such as inappropriate pacemaker control causing
promotion of sleep fragmenting arousals.
[0160] The present invention is also able to monitor heart rate,
blood-pressure variations and sleep fragmentation arousals
throughout sleep and determine whether these said variations relate
to normal sleep physiology, or whether these changes suggest
modification of pacemaker control in order to optimize heart
function, while at the same time minimizing sleep
fragmentation.
[0161] In another embodiment, the present invention has the
capability to provide optimal sleep during oxygen concentration
treatment by utilizing cortical, subcortical, airflow shape or
waveform characteristics as a marker for optimizing the treatment.
The present invention can control the titration algorithm to
minimize RERA and TERA while optimizing a subject's breathing
therapy. The present invention enables the detection of RERA and
TERA by monitoring any combination of breathing mask or hose
sounds, airflow or pressure signals. Alternatively more
comprehensive signals can be deployed. SOC enables the conventional
optimization of blood-gas status of a subject, while minimizing
arousals. An inappropriate mixture of oxygen and air, or rate of
delivery of gas to a subject could promote arousals, for
example.
[0162] Inappropriate gas delivery could in turn cause mechanical or
chemical receptors within the patient's breathing anatomy to
activate sleep fragmentation arousals (TERA). The monitoring of the
airflow wave shape can be used to predict the onset or incidence of
TERA or RERA and allow the gas treatment to be controlled in such a
manner to minimize such arousals (while still optimizing breathing
therapy).
[0163] In one embodiment, the present invention is utilized as
purely a diagnostic tool for determining sleep disordered breathing
and sleep quality. The present invention is adapted to record,
meter, index or display, in real time or on a replay or review
basis, a number of sleep or arousal related physiological data or
statistics. Statistics and indexes such as RAI, AHI, RERA, RDI,
arousals, sleep fragmentation, or sleep architecture index are
derived from the monitored parameters and this information is
stored for analysis.
[0164] In one embodiment, monitored physiological parameters
monitored which were utilized to determine the statistics and
indexes may also be stored in order to assist in the analysis. The
present invention can also include graphical and statistical tools
which are known in the art to enable a user to manipulate and
display raw data or derived values in a meaningful format. In one
embodiment, the present invention has the capability of displaying
raw data and then using visual clues to mark the occurrence of an
event such as arousal in the raw data. The present invention can
also link an event or events to specific index values or derived
values which reflect the occurrence of the event.
[0165] The matter set forth in the foregoing description and
accompanying drawings is offered by way of illustration only and
not as a limitation. While a particular embodiment has been shown
and described, it will be apparent to those skilled in the art that
changes and modifications may be made without departing from the
broader aspects of applicants' contribution. The actual scope of
the protection sought is intended to be defined in the following
claims when viewed in their proper perspective based on the prior
art.
* * * * *