U.S. patent application number 14/185169 was filed with the patent office on 2014-06-19 for method and apparatus for automatic evoked potentials assessment.
This patent application is currently assigned to Brainscope Company, Inc.. The applicant listed for this patent is Brainscope Company, Inc.. Invention is credited to Elvir Causevic.
Application Number | 20140171820 14/185169 |
Document ID | / |
Family ID | 44146948 |
Filed Date | 2014-06-19 |
United States Patent
Application |
20140171820 |
Kind Code |
A1 |
Causevic; Elvir |
June 19, 2014 |
METHOD AND APPARATUS FOR AUTOMATIC EVOKED POTENTIALS ASSESSMENT
Abstract
Systems and methods for assessing a patient's neurologic state
based on auditory evoked responses are provided.
Inventors: |
Causevic; Elvir; (San
Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Brainscope Company, Inc. |
Bethesda |
MD |
US |
|
|
Assignee: |
Brainscope Company, Inc.
Bethesda
MD
|
Family ID: |
44146948 |
Appl. No.: |
14/185169 |
Filed: |
February 20, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12720907 |
Mar 10, 2010 |
8700141 |
|
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14185169 |
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Current U.S.
Class: |
600/544 |
Current CPC
Class: |
G06K 9/00543 20130101;
A61B 5/7203 20130101; A61B 5/04842 20130101; A61B 5/04845 20130101;
A61B 5/726 20130101 |
Class at
Publication: |
600/544 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/0484 20060101 A61B005/0484 |
Claims
1-23. (canceled)
24. A system for processing event-related brain potential signals
of a patient, comprising: one or more neurological electrodes
configured to collect event-related brain potential signals
generated in response to a stimulus provided to the patient; one or
more stimulus generators configured to provide sensory or cognitive
stimuli, or motor events to the patient; a signal processor
operatively connected to the one or more neurological electrodes
and configured to receive the event-related brain potential
signals; and wherein the signal processor s configured to: denoise
the event-related brain potential signals received by the signal
processor; transform the denoised event-related brain potential
signals to a transform domain; and automatically detect locations
of one or more peaks of the event-related brain potential signals
in the transform domain.
25. The system of claim 24, wherein the processor configured to
reject artifacts present in the event-related brain potentials
prior to denoising.
26. The system of claim 25, wherein the processor is configured to
reject artifacts present in the event-related potentials using a
plurality of automated artifact detectors working in parallel.
27. The system of claim 24, wherein the processor is configured to
transform the denoised event-related brain potential signals to a
wavelet transform domain.
28. The system of claim 24, wherein the processor is configured to
extract a set of quantitative features from the event-related brain
potential signals.
29. The system of claim 28, wherein the processor is configured to
compare the set of quantitative features to one or more feature
sets stored in a storage system to allow assessment or monitoring
of the patient's neurological state.
30. The system of claim 24, wherein the processor is configured to
extract features related to the peaks in the patient's
event-related potential signals.
31. The system of claim 30, wherein the processor is configured to
detect the amplitude or the timing of the peaks in the patient's
event-related potential signals.
32. The system of claim 24, wherein the processor is configured to
locate peaks of the event-related potential signals in time
domain.
33. The system of claim 32, wherein the processor is configured to
combined the peak locations in time domain with the peak locations
in the transform domain.
34. The system of claim 24, wherein the stimulus provided to the
patient is an auditory stimulus and the event-related potential
signal generated in an auditory brainstem response.
35. The system of claim 24, wherein the stimulus provided to the
patient is a visual stimulus.
Description
[0001] The present disclosure pertains to devices and methods for
monitoring changes in a neurologic state of a patient, and more
particularly, to systems and methods for monitoring auditory evoked
response.
[0002] There are numerous surgical and medical conditions that can
cause potentially deleterious changes in brain or brain stem
function. For example, moderate or severe central nervous system
injury can results from trauma (e.g., due to an impact or other
injury to the head), metabolic disorders, infections, expanding
intracranial masses, intracranial hemorrhage, illicit or
prescription drug use, and iatrogenic sources (e.g.,
post-operatively or as a medical treatment side effect). Whatever
the cause, it would be desirable to have better noninvasive methods
for evaluating head injury and, when needed, providing appropriate
medical or surgical interventions before potentially serious or
irreversible neurological damage occurs. In addition, portable
neurologic monitors that allow assessment of head injuries at more
remote locations (e.g., on the battlefield or at accident sites)
may allow more appropriate patient assessment and treatment.
[0003] In the clinical setting, changes in neurologic state may be
suspected based on declining mental status, abnormal neurological
signs, and other physical findings, such as changes in the
appearance of the optic nerve when viewed through an
ophthalmoscope. However, monitoring neurologic status through these
methods presents a number of challenges. For example, many surgical
patients or seriously ill medical patients will be sedated or
unconscious, thereby making it impossible to evaluate certain
changes in mental status. In addition, changes in physical exam
findings, such as a change in the optic nerve, may be discovered
after significant neurologic damage has occurred, thereby
preventing timely intervention. In addition, plantable monitors are
less desirable since they require an invasive procedure and impart
other potential risks (e.g., infection).
[0004] The systems and methods of the present disclosure to provide
easy-to-use tools for assessing and monitoring head injuries.
SUMMARY
[0005] A system for monitoring brain electrical activity is
provided. The system comprises a set of electrodes, at least one
auditory stimulus generator, and a detection system operatively
connected to the set of electrodes and configured to receive
electrical signals detected by the electrodes after production of
an auditory stimulus by the stimulus generator, the electrical
signals representing an auditory evoked response. The system
further comprises a processor circuit including electrical
circuitry configured to perform the steps of: removing artifact
noise from the signal; performing a nonlinear denoising step on the
signal; performing a non-linear transform on the signal; producing
a set of nonlinear features related to the patient's auditory brain
stem response; and comparing the set of nonlinear features to one
or more feature sets stored in a storage system and determining if
the non-linear features are indicative of an abnormal neurologic
state.
[0006] A method for monitoring brain electrical activity is
provided. The method comprises applying a set of electrodes to a
patient, generating at least one auditory stimulus that can be
detected by the patient, and recording an electrical signal
detected by the electrodes after production of an auditory stimulus
by the stimulus generator, the electrical signal representing an
auditory evoked response; removing artifact noise from the signal.
The method further comprises performing a non-linear denoising step
on the signal; performing a non-linear transform on the signal;
producing a set of non-linear features of the signal; and comparing
the set of non-linear features to one or more feature sets stored
in a storage system and determining if the non-linear features are
indicative of an abnormal neurologic state.
DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1A illustrates a brain electrical activity monitoring
system according to one embodiment of the present disclosure.
[0008] FIG. 1B illustrates a schematic diagram of the monitoring
system of FIG. 1A, illustrating additional components.
[0009] FIG. 2A illustrates an electrode set for use with the brain
electrical activity monitoring system of the present
disclosure.
[0010] FIG. 2B illustrates the electrode set of FIG. 1B, as applied
to a patient.
[0011] FIG. 3 illustrates a method for automatically processing a
signal to assess a neurologic state of a patient, according to
certain embodiments.
DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0012] The present disclosure provides a system and method for
monitoring brain electrical activity, including assessment of
auditory brainstem responses (ABR) to assess neurologic function.
In certain embodiments, the system and method allow rapid,
automatic, and/or continuous monitoring of ABR signals, or other
evoked potential signals. The system and method can assist in
determining the severity of certain injuries due, for example, to
trauma, infection, medical disorders (e.g., inflammatory disorders,
adverse drug reactions), and/or post-surgical complications. In
certain embodiments, the systems and methods allow rapid assessment
of the severity and/or progression of problems due to traumatic
brain injury due, for example to an impact to the head. In certain
embodiments, the systems and methods allow continuous, non-invasive
monitoring of intracranial pressure (ICP).
[0013] As described further below, the system includes a set of
electrodes and an auditory stimulus for generating and detecting
ABR and other auditory evoked potential signals from a patient. The
system and method further include processes for automatically
removing raw artifact noise from the signal and performing a
non-linear denoising step on the signal to generate a sufficiently
high signal-to-noise ratio to allow automatic ABR evaluation. The
system and method can further include non-linear processing
techniques including, for example, performing a non-linear
transform on the signal and producing a set of non-linear features
related to the patient's auditory brain stem response. These
non-linear features can be compared to one or more feature sets
stored in a storage system to determine if the non-linear features
are indicative of an abnormal neurologic state.
[0014] FIG. 1A illustrates a brain electrical activity monitoring
system 10, according to certain embodiments of the present
disclosure. As shown, the brain electrical activity monitoring
system 10 can include an enclosure 20 containing electrical
circuitry configured to perform data processing, stimulus
generation, and analysis for diagnosis and patient monitoring. In
addition, the enclosure 20 may further include a display system 30,
such as an LCD or other visual display to provide real-time,
easy-to-interpret information related to a patient's clinical
status.
[0015] In some embodiments, the brain electrical activity
monitoring system 10 will include circuitry configured to provide
real-time monitoring of brain electrical activity. The system 10
will provide rapid data acquisition, processing, and analysis to
allow point-of-care diagnosis and assessment. For example, as
shown, the display system 30 can include one or more indicators 35,
or visual displays, that are configured to display an
easy-to-interpret indication of a patient's status. In one
embodiment, the indicators 35 will include an indication of where a
patient's status lies relative to a normal data set, a patient's
status relative to a base line, and/or one or more indicators of
the origin of any abnormalities. In some embodiments, the
indicators provide a scale (from normal to severely abnormal).
Accordingly, the scale provides an indication of the severity of an
injury, elevation in ICP, or abnormality in brain stem
function,
[0016] In addition, the brain electrical activity monitoring system
10 may include one or more alert systems for notifying a caregiver
of an abnormality or changing condition. In some embodiments, the
system further includes a communication device configured to
automatically generate a signal representing the patient's
neurologic state. Such communication devices can include visual
display systems and/or audible alerts that may be easily understood
by patient care givers. In addition, in some embodiments, the alert
systems can be remote from the monitoring system, to allow remote
monitoring and intervention by health care personnel.
[0017] In certain embodiments, the visual display indicates a
deviation from a baseline measurement, as described further below.
In addition, the system can include at least one second visual
display indicating at least one diagnostic state. For example, the
diagnostic state can indicate, elevated intracranial pressure,
cerebral edema, compromised brainstem function, or dysfunction of
higher parts of the neural auditory pathway, including the
cognitive function relating to auditory stimulus perception. In
some embodiments, the communication system includes a visual
display indicating a deviation from a baseline measurement
indicative of ICP for a patient.
[0018] FIG. 1B illustrates a schematic diagram of the monitoring
system of FIG. 1A, illustrating additional components. As shown,
the enclosure 20, can include a number of component parts. For
example, the enclosure 20 may include a memory unit or storage
system 22 configured to store data related to patient brain
electrical activity data measurements, or a database of normal
and/or pathological readings. Further, the enclosure will include
circuitry configured to process and evaluate electrical signals and
data 24, and a transmitter unit 26.
[0019] The circuitry 24 can include a number of circuitry types.
For example the circuitry 24 can include processing circuitry
configured to receive electrical signals from electrodes, as shown
in FIGS. 2A-2B, and to convert such signals into data that can be
further evaluated. In some embodiments, the circuitry can be
configured to enable nonlinear processing, including nonlinear
amplifiers. Further, the circuitry 24 can also include components
configured to allow analysis of processed data and comparison of
brain electrical activity data to normal data, or to previous or
future measurements, as described in more detail below. Further, it
will be understood that, although shown as a single component,
multiple components can be included, either on a single chip or
multiple chips.
[0020] The transmitter unit 26 can include a number of transmitter
types. For example, the transmitter 26 may include a hardware
connection for a cable or a telemetry system configured to transmit
data to a more distant receiver 28, or a more powerful transmission
system to redirect data to a database 32 that may be stored nearby
or at a remote or distant location. In certain embodiments, the
data can be transmitted and stored and/or evaluated at a location
other than where it is collected.
[0021] The brain electrical monitoring system 10 may be configured
to attach to various patient interfaces. For example, FIGS. 2A-2B
illustrate an electrode set 50 for use with the brain electrical
activity monitoring system 10 of the present disclosure. As shown,
the electrode set 50 includes one or more electrodes 60 for
placement along the patient's forehead and mastoid region. As
shown, the electrode set 50 includes a limited number of electrodes
60 to facilitate rapid and easily repeated placement of the
electrodes 60 for efficient, but accurate, patient monitoring.
Further, in one embodiment, the electrodes 60 may be positioned on
a head band 70 that is configured for easy and/or rapid placement
on a patient, as shown in FIG. 2B. Further, it will be understood
that other electrode configurations may be selected, which may
include fewer or more electrodes.
[0022] As noted, the electrode set 50 will be operably connected to
the monitoring system 10. Generally, the electrodes 60 will be
electrically coupled with the monitoring system 10 to allow signals
received from the electrodes to be transmitted to the monitoring
system 10. Such an electrical coupling will generally be through
one or more electrical wires, but nonphysical connections may also
be used.
[0023] Further, as shown, a signal production device 80 may be
provide, and may be attached to the head band 70 or contained
separately. As shown, the device 80 includes an auditory stimulus
generator configured to produce audible signals to facilitate
measurement of brain electrical activity in response to auditory
stimuli. Further, the monitoring system 10 may also include other
stimulation generating systems such as visual, tactile, taste, and
olfactory stimulation systems. Further, the stimulation devices may
be attached to the electrode set 50 or may be contained in separate
components.
[0024] In some embodiments, the electrode set 50 will include
electrodes positioned to allow detection of various types of brain
electrical activity. For example, various forehead or scalp
electrodes may be included to allow detection of cortical activity,
or to assist in identification of signal artifacts to be removed
during raw denoising. Further, other electrodes may be positioned
to allow detection of brain stem functions (e.g., mastoid or
occipital electrodes). In some embodiments, the electrode set is
positioned on a head band and includes at least two electrodes
positioned on the head band to allow detection of auditory evoked
response signals when the headband is positioned on a patient.
[0025] FIG. 3 illustrates a method for automatically processing a
signal to assess a neurologic state of a patient, according to
certain embodiments. As shown, the process includes application of
an electrode set to a patient's head, as shown at Step 310. Next,
the brain activity monitoring system is connected to the electrode
set, as shown at Step 320, and data collection is begun, as shown
at Step 330. As noted above, the data collection can include
measurement and recording of ABR signals after generation of
audible stimuli produced by a stimulus generator.
[0026] After collection of ABR or other evoked auditory response
signal data, the data can be processed to allow automatic
neurologic assessment and monitoring. Accordingly, raw denoising is
first performed, as shown at Step 340 to reduce signal artifacts.
The raw denoising can be performed using an automated process that
does not require a trained technician, as described in more detail
below.
[0027] After raw denoising, a rapid non-linear denoising process is
used to produce a suitable signal-to-noise ratio. In certain
embodiments, a wavelet denoising algorithm is used. For example, a
suitable denoising algorithm include Cyclic Shift Tree Denoising
(CTSD), which is described by Causevic et al. in "Fast Wavelet
Estimation of Weak Biosignals," Biomedical Engineering, Vol. 52(6):
1021-32, 2005. In certain embodiments, to facilitate automatic,
real-time monitoring, a CTSD process may be performed real time,
such that incoming data is buffered and the algorithm is completed
on the buffered date. In addition, as new data is received (i.e., a
new data frame comes in), the new data can be is added to the
buffer on a first in/first out basis, and the algorithm is
repeated.
[0028] In some embodiments, the CTSD method is adapted for
continuous measurement, such that new frames are adapted into the
algorithm in real time in batches. For example, a time at which the
CTSD is performed can be set, and as each level of CTSD progresses,
a new epoch of fresh ABR data is inserted into the process in
parallel. In certain embodiments, a linear averaging process can be
employed to arrive at an averaged waveform, synchronized to the
auditory stimulus. This result can be combined with the CTSD
result, sample by sample, or averaged.
[0029] After denoising, the signal can be further processed to
identify certain non-linear features, as shown at step 360. In
certain embodiments, a non-linear transform is performed followed
by a process for detecting the location of ABR peaks in the
non-linear domain. In addition, various other non-linear features
can be identified and stored for comparison to patient baseline,
normative, or population data, as described further below.
[0030] In various embodiments, automatic peak detection can be
performed by using a set of non-linear methodologies, such as a
non-linear transform (e.g., a wavelet transform), while keeping the
CTSD coefficients in the non-linear/wavelet domain and searching
for local peaks in that domain independently. The peaks information
in the non-linear domain is then combined with the time domain peak
detection methods in a single classifier, or a using a voting
classifier scheme.
[0031] In addition, various other non-linear features can be
extracted from the signal, such that in addition to the actual peak
locations, other qualitative information about the peaks is
calculated, including, for example, various local and global maxima
of the non-linear features (including number and location, nth
order moments, vanishing moments, area under the curve of
non-linear coefficients, etc. In certain embodiments, linear
features of the waveform can be extracted such as amplitude, power,
phase, frequency spectrum, or others, and those features can be
combined with non-linear features.
[0032] After peak detection and feature extraction are performed,
the non-linear and/or linear features can be compared to data
stored in a database and/or to prior data obtained from the same
patient to allow assessment and monitoring of the patient's
neurologic state, as shown at Step 380. In addition, if
abnormalities are detected, an alarm or indicator can be active, as
shown at step 390, or a normal condition can be indicated. Further,
if no abnormality is detected, or if continued monitoring of an
abnormal patient is needed, measurements can be repeated
continuously or periodically, to allow ongoing patient monitoring.
In some embodiments, this comparison may include a multivariate
comparison.
[0033] In some embodiments, the database includes prior auditory
evoked response measurements for the same patient, and the set of
non-linear features are compared to one or more feature sets stored
from the prior measurements and determine if any changes have
occurred.
[0034] In various embodiments the database includes ABR or other
evoked potential data from a group of other patients having an
identified neurologic state. For example, the database can include
a database of normal patients and patients with a variety of
different abnormalities, including, for example, traumatic brain
injury at various times after injury, infection, edema, elevated
ICP. In some embodiments, the system includes a database of
auditory evoked response data for a group of patients, and the
patient's neurologic state is classified based on a similarity
between one or more non-linear features of the patient's auditory
evoked response and one or more non-linear features of at least one
other patient having a known neurologic state.
[0035] A variety of non-linear features can be used to assess the
patient's neurologic state. For example, the timing of ABR peaks
has been shown to change due to trauma and/or increased
intracranial pressure. However, automatic detection of ABR peaks is
difficult, and therefore, automatic assessment of brain
abnormalities has not been successful. The signal processing
techniques of the present disclosure allow automatic peak detection
and feature extraction in the non-linear domain, and therefore,
facilitate automatic neurologic monitoring. Accordingly, in certain
embodiments, the non-linear features set identified as described
above, can include the location, amplitude or time of one or more
peaks in an auditory evoked response.
[0036] As noted above, in various embodiments, the system and
method of the present disclosure can provide an indication of an
elevation in ICP. In some embodiments, the indication can be based
on a sliding scale from normal to severely abnormal, without
providing an absolute value of ICP. In this way, the system
provides information of clinical significance, for example, warning
of potential deleterious changes in brain stem function, as
indicated by changes in ABR, without the need for an invasive ICP
monitor. In other embodiments, a correlation between ICP and the
ABR data is made to provide an estimation of ICP.
EXAMPLE
Sample Algorithm
[0037] One typical specific algorithm for feature extraction and
classification is described below. This algorithm may be used to
identify abnormalities in ICP or assess the severity of a traumatic
brain injury:
[0038] (1) Take CTSD averaged ABR waveform, with sufficiently high
estimated SNR (e.g., Fsp>3.1), of length 15 ms, including saved
wavelet coefficients
[0039] (2) Calculate "string length" of the entire waveform and
save.
[0040] (3) Calculate first half "string length" 0-7.5 ms and second
half 7.5-15 ms, and save.
[0041] (4) Calculate a Pearson correlation coefficient (r) with a
series of sinewaves of length 15 ms, starting from 1 Hz to 1000 Hz,
save vector of top ten r's (highest correlation), and slide index
(likely peak location).
[0042] (5) Produce wavelet coefficients using biorthogonal
wavelets, save top 50% of coefficients by amplitude.
[0043] (6) Produce nearest-neighbor search for peak detection (edge
detection/sign change), filter to find local maxima, save.
[0044] Put together all the features in a vector, multiplying each
of the features with a pre-determined weight factor based on a
training data set with manually pre-identified peaks and invasive
ICP recordings, and then classify new signals.
Raw Denoising:
[0045] Most systems that rely on quantitative analysis of brain
electrical activity typically assume that a trained technologist
has manually edited the raw data to remove artifacts. However, the
editing process can be time-consuming and is inherently subjective.
In addition, the need for technologist editing prevents automated
monitoring or assessment, and therefore, is not suitable for
continuous and rapid monitoring, or for use in many settings (e.g.,
in a field hospital, at a sporting event, or in typical primary
care settings). The following processing techniques can be used for
raw data denoising to allow automatic denoising. Further, suitable
methods for editing or denoising EEG or other signals are described
in U.S. patent application Ser. No. 12/720,861, which is titled,
"Method and Device for Removing EEG Arifacts," was filed on Mar.
10, 2010, and is incorporated by reference in its entirety. This is
accomplished using standard signal processing components, which
include digital filtering (low-pass filtering, bandpas filtering,
etc.), thresholding, peak detection, and frequency-based
processing.
[0046] There are seven typical types of noise that can contribute
to poor signal quality. These include (1) horizontal lateral eye
movements (HEM), (2) vertical eye movements (e.g. blinks) (VEM),
(3) cable or electrode movement causing over-range artifacts (PCM),
(4) impulse artifacts (for example due to electrode "pops") (IMP),
(5) electromyographic activity (also referred to as "muscle
activity") (EMG), (6) significantly low amplitude signal (for
example as a result of the suppression component of "burst
suppression") (SLAS), and (7) atypical electrical activity pattern
(for example due to paroxysmal brain activity) (REAP). Out of these
seven artifact types, two are non-physiological (type 3, type 4),
three are physiological but are not brain-generated (type 1, 2,
type 5) and two are brain-generated (type 6, type 7). All of these
artifacts reflect a non-brain electrical activity, or abnormal
brain-electrical activity. Further, in addition to recognizing
artifacts of the types listed above, technologists typically remove
short segments of the signal located (in time) immediately before
and after the artifact. These segments are traditionally referred
to as guardbands.
[0047] The automated denoising process described below includes
artifact detector algorithms that can be used to independently
identify the artifacts described above. These artifact detectors
can work in parallel on a raw ABR data stream. In some embodiments,
the duration of each artifact segment is computed to a resolution
of 150 ms, each 15 ms segment referred to as a "sub-epoch". Each
artifacting module produces a binary mask of size 1.times.10
indicating presence or absence of the artifact type in each of the
sub-epochs.
[0048] The seven types of artifacts and the algorithms used for
their detection are described below.
[0049] (1) Horizontal/Lateral Eye Movement (HEM/LEM):
[0050] To remove HEM artifacts, each electrode channel is band-pass
filtered using an FIR filter with passband 0.5-3 Hz. The high-pass
cut-off frequency of 0.5 Hz is chosen to ignore the influence of
low-frequency activity occurring at frequencies below the
delta.sub.--1 band (0.5-1.5 Hz). Candidate HEM sub-epochs are
identified wherever the difference signal F7f F8f exceeds a
threshold of 24 .mu.V. An additional measure, the
mean-squared-error (mse) between -F7f and F8f is computed to help
filter-out false detection of HEM. Cases where the mse is large
(above a threshold) are indicative of an asymmetry between the two
leads, which is likely to reflect presence of pathology rather than
presence of HEM.
[0051] (2) Vertical eye movement(VEM)/Eye Opening/Eye Closing
(EOEC):
[0052] Detection of vertical eye movement (VEM) (of which eye
opening/closing is a sub-type) is performed by locating large
"bumps" on leads Fp1 and Fp2, which are located right above the
eyes. Since both eyes generally move in unison, the algorithm makes
sure that only bumps that occur concurrently and in the same
direction (same polarity) on Fp1 and Fp2 are identified as vertical
eye movements. Each of the two signals, Fp1 and Fp2, is first
low-pass filtered in the range 0.5-5 Hz.
[0053] Sub-epochs are then analyzed one at a time. In each
sub-epoch, runs of samples exceeding a threshold of 24 .mu.V are
identified. In each such run, the global extremum is located and
its value is compared to average signal values on either side of
it. If t.sub.e denotes the time location of the extremum (in
milliseconds), these average are taken over temporal windows
[t.sub.e-320, t.sub.e-100] and [t.sub.e+100, t.sub.e+320]. If the
absolute difference between the extremum and either average exceeds
the threshold, the sub-epoch is identified as a candidate VENT
artifact. After this processing has occurred on both leads, the
results are combined to turn candidate VEMs to true VEMs wherever
they occurred concurrently on Fp1, Fp2 as described above.
[0054] (3) Patient cable or electrode movement (PCM):
[0055] This is simply done by detecting excessively large signal
magnitudes (also called "over-range") in any recorded channel. The
default magnitude threshold is set to 120 .mu.V. Generally, no
guardband is implemented for artifacts of this type.
[0056] (4) Impulses (IMP):
[0057] To remove impulse artifacts, any recorded channel is first
high-pass filtered with cutoff frequency at 15 Hz. This is done in
order to remove the alpha component of cerebral electrical activity
so that "sharp alpha" waves are not labeled as spikes. In each
sub-epoch, the algorithm then looks at high-frequency activity.
Successive windows of 100 ms width with 50% overlap are examined.
Within each window, the value (max-min) is computed and compared to
a threshold equal to 75 .mu.V. Data greater than the 75 .mu.V
threshold is removed.
[0058] (5) Muscle activity (EMG):
[0059] To remove EMG artifacts, any recorded channel is first
band-pass filtered within the range 25-35 Hz (subband: .beta.2) to
produce a first signal (E2) and band-pass filtered in the range
15-25 Hz (subband: .beta.1) to produce a second signal (E1). The
variance (energy) of signal E1 on each lead, over the entire 2.5
second long epoch is computed. For each sub-epoch, the variance of
signal E2 is also computed on each lead, and the relative energy of
this signal with respect to the energy of signal E1 over the entire
epoch is compared to a fixed threshold. The default threshold value
is 155%. If, in at least one lead, the relative energy is larger
than the threshold and the energy of E2 is larger than a minimum
energy (currently set to 14 .mu.V2), EMG detection is
triggered.
[0060] (6) Significantly Low Amplitude Signal (SLAS):
[0061] This is meant to capture extremely low-amplitude EEG signals
(at all frequencies), which occur, for example, when the brain is
in burst suppression mode; a condition which can occur (but should
be avoided) during anesthesia. No additional filtering of the EEG
is used for detection of this activity. It can be detected by
looking for signal epochs with mean-square energy below a
threshold. Sub-epochs are examined four at a time, corresponding to
a window size of 1 second. The overlap between consecutive groups
of sub-epochs is 75%. The maximum signal energy (across leads) is
computed and compared to a fixed threshold. The default threshold
value is 12 .mu.V2.
[0062] (7) Atypical Electrical Activity Pattern (AEAP):
[0063] This artifact type is meant to detect unusual patterns of
activity in the EEG such as those which occur in the EEG of
epileptic subjects during a convulsive or non-convulsive seizure.
The algorithm is sensitivite to Spike-Wave complexes occurring in
bursts over several hundred milliseconds. In this method raw EEG
data is cleaned, and linear and non-linear averaging of that
pre-cleaned data is performed. Then the linear and non-linear
features of the single final averaged waveform is used to detect
peak location and amplitude (using direct methods of peak detection
and classification based on features, and to compare the features
of the present averaged waveform to the features of pre-stored
waveforms already correlated to ICP levels or other
abnormalities.
[0064] Other embodiments will be apparent to those skilled in the
art from consideration of the specification and practice of the
devices and methods disclosed herein. It is intended that the
specification and examples be considered as exemplary only, with a
true scope being indicated by the following claims. A number of
patents, patent publication, and nonpatent literature documents
have been cited herein. Each of these documents is herein
incorporated by reference.
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