U.S. patent application number 16/923689 was filed with the patent office on 2021-03-25 for systems and methods for seizure prediction and detection.
The applicant listed for this patent is CeriBell, Inc.. Invention is credited to Xingjuan Chao, Alexander M. Grant, Mehdi Hajinoroozi, Baharan Kamousi, Suganya Karunakaran, Josef Parvizi, Raymond Woo, Jianchun Yi.
Application Number | 20210085235 16/923689 |
Document ID | / |
Family ID | 1000005209936 |
Filed Date | 2021-03-25 |
![](/patent/app/20210085235/US20210085235A1-20210325-D00000.TIF)
![](/patent/app/20210085235/US20210085235A1-20210325-D00001.TIF)
![](/patent/app/20210085235/US20210085235A1-20210325-D00002.TIF)
![](/patent/app/20210085235/US20210085235A1-20210325-D00003.TIF)
![](/patent/app/20210085235/US20210085235A1-20210325-D00004.TIF)
![](/patent/app/20210085235/US20210085235A1-20210325-D00005.TIF)
![](/patent/app/20210085235/US20210085235A1-20210325-D00006.TIF)
United States Patent
Application |
20210085235 |
Kind Code |
A1 |
Kamousi; Baharan ; et
al. |
March 25, 2021 |
SYSTEMS AND METHODS FOR SEIZURE PREDICTION AND DETECTION
Abstract
The present disclosure provides systems and methods for seizure
detection. The method for seizure detection may include receiving a
plurality of electroencephalography (EEG) signals over a plurality
of channels for a subject, preprocessing the plurality of EEG
signals by segmenting the plurality of EEG signals for each channel
into a plurality of temporal data segments, extracting a plurality
of features from each temporal data segment for each channel, and
applying a machine learning algorithm to the plurality of features
to perform a seizure binary classification for each temporal data
segment for each channel. A control policy may be employed to
determine a seizure burden on the aggregated seizure binary
classifications. When the seizure burden is equal to or exceeds a
threshold, a notification may be generated. The notification may be
usable by a healthcare practitioner to assess whether the subject
may be at risk of having a seizure.
Inventors: |
Kamousi; Baharan; (Redwood
City, CA) ; Hajinoroozi; Mehdi; (Santa Clara, CA)
; Karunakaran; Suganya; (Sunnyvale, CA) ; Grant;
Alexander M.; (Redwood City, CA) ; Yi; Jianchun;
(San Jose, CA) ; Woo; Raymond; (Los Altos, CA)
; Parvizi; Josef; (Palo Alto, CA) ; Chao;
Xingjuan; (Palo Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CeriBell, Inc. |
Mountain View |
CA |
US |
|
|
Family ID: |
1000005209936 |
Appl. No.: |
16/923689 |
Filed: |
July 8, 2020 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
16578032 |
Sep 20, 2019 |
10743809 |
|
|
16923689 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 9/542 20130101;
A61B 5/4094 20130101; A61B 5/7225 20130101; A61B 5/369 20210101;
A61B 5/7267 20130101; G06N 20/00 20190101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/0476 20060101 A61B005/0476; G06F 9/54 20060101
G06F009/54; G06N 20/00 20060101 G06N020/00 |
Claims
1. (canceled)
2. A method for seizure detection, comprising: (a) receiving a
plurality of electroencephalography (EEG) signals over a plurality
of channels for a subject; (b) preprocessing the plurality of EEG
signals by segmenting the plurality of EEG signals for each channel
into a plurality of temporal data segments; (c) extracting a
plurality of features from each temporal data segment for each
channel, wherein each temporal data segment is associated with a
time epoch; (d) applying a machine learning algorithm to the
plurality of features to perform a seizure binary classification
for each temporal data segment for each channel, thereby generating
a plurality of classifications for the plurality of temporal data
segments, wherein the seizure binary classification for each
temporal data segment comprises classifying each temporal data
segment for each channel as (1) seizure-positive or (2)
seizure-negative, wherein the plurality of classifications are
compared sequentially across a plurality of time epochs on each
channel; (e) aggregating the classifications for the plurality of
temporal data segments for the plurality of channels over a moving
time window, wherein the moving time window comprises a fixed
period of time between about one minute and about one hour; and (f)
determining a seizure burden as a continuous output measure for the
moving time window based on the aggregated classifications, wherein
the seizure burden comprises a percentage of the temporal data
segments that are classified as seizure-positive, and wherein the
seizure burden is a metric that provides a measure of a degree of
severity or likelihood of a seizure.
3. The method of claim 2, wherein the continuous output measure is
used to generate one or more notifications when the seizure burden
is equal to or exceeds one or more thresholds, wherein the one or
more notifications are indicative of different seizure activities
and are usable for assessing whether the subject is at risk of
having a seizure.
4. The method of claim 2, wherein each temporal data segment has a
duration ranging from about one second to about twenty seconds.
5. The method of claim 4, wherein the duration of each temporal
data segment is about ten seconds.
6. The method of claim 2, wherein the moving time window is about
five minutes.
7. The method of claim 3, wherein the one or more notifications are
generated in the form of visual, audio, and/or textual alerts.
8. The method of claim 3, wherein a first notification indicative
of frequent seizure activity is generated when the seizure burden
is equal to or exceeds a first threshold of 10%.
9. The method of claim 3, wherein a second notification indicative
of abundant seizure activity is generated when the seizure burden
is equal to or exceeds a second threshold of 50%.
10. The method of claim 3, wherein a third notification indicative
of continuous seizure activity is generated when the seizure burden
is equal to or exceeds a third threshold of 90%.
11. The method of claim 2, wherein the plurality of channels
comprises at least three channels.
12. The method of claim 11, wherein the plurality of channels
comprises eight channels.
13. The method of claim 2, wherein the plurality of features
comprises time and/or frequency domain features that are intrinsic
in the plurality of EEG signals.
14. The method of claim 13, wherein the plurality of features
comprises at least twenty different time and/or frequency
features.
15. The method of claim 13, wherein the plurality of features
comprises a plurality of discrete values associated with the time
and/or frequency domain features.
16. The method of claim 2, wherein the machine learning algorithm
comprises a random forest, a boosted decision tree, a
classification tree, a regression tree, a bagging tree, a neural
network, or a rotation forest.
17. The method of claim 2, wherein the machine learning algorithm
is individually applied to the plurality of features extracted for
each channel, such that each channel has a separate iteration of
the machine learning algorithm.
18. The method of claim 2, wherein the preprocessing of the
plurality of EEG signals further comprises: applying a filter to
the plurality of EEG signals over the plurality of channels, prior
to the segmentation of the plurality of EEG signals.
19. The method of claim 18, wherein the filter comprises a bandpass
filter configured to filter the plurality of EEG signals between
about one Hertz (Hz) and about thirty-five Hz.
20. The method of claim 2, further comprising: classifying a
particular time epoch as associated with a potential electrographic
seizure, if the temporal data segments for a subset of the
plurality of channels are classified as seizure-positive.
21. The method of claim 20, wherein the subset comprises at least
half of the plurality of channels.
22. The method of claim 2, wherein sequential periods of time
formed by the moving time window are non-overlapping.
23. The method of claim 2, further comprising: outputting the
seizure burden as a graphical visual element on a display.
24. The method of claim 23, further comprising: displaying one or
more thresholds of the seizure burden in the graphical visual
element.
25. The method of claim 23, wherein the graphical visual element
comprises a time-series plot, bar graph, or chart.
26. The method of claim 24, wherein the time-series plot is
configured to change in color as the seizure burden passes a
threshold of the one or more thresholds.
27. The method of claim 23, further comprising: using the graphical
visual element to (i) assess a condition of the subject, (ii)
determine a course of treatment, (iii) monitor an effectiveness of
a course of treatment if the course of treatment is provided to the
subject, or (iv) monitor a progression of the subject's condition
over time.
Description
CROSS-REFERENCE
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/578,032, filed Sep. 20, 2019; which is
incorporated herein by reference in its entirety.
BACKGROUND
[0002] Monitoring electroencephalography (EEG) signals is an
important task for the early diagnosis in seizures. While analyzing
EEG signals plays an important role in monitoring the brain
activity of patients, an expert is needed to analyze all EEG
recordings to detect seizure activity. This can be tedious and
time-consuming, and a timely and accurate diagnosis of seizure
activity is essential to initiate therapy and reduce the risk of
future seizures and seizure-related complications.
[0003] At the moment, machine learning algorithms provide an avenue
to classify EEG signals and minimize expert intervention. Though
machine learning algorithms require good quality EEG signals to
provide effective classification results. Oftentimes, the EEG
signals provided to machine learning algorithms need to be
optimized to make the machine learning algorithms more effective in
predicting seizures.
SUMMARY
[0004] There is a need to optimize the quality of EEG signals
provided to machine learning algorithms to make them more effective
in preventing seizures. To optimize the quality of EEG signals, the
EEG signals can be used to build features. Each derived feature can
focus on a distinct characteristic of an EEG signal that allows the
machine learning algorithm to more easily discern measured EEG
signals. Through the combination of using features and then
classifying them using machine learning algorithms, allows for a
more effective means for predicting seizures. Further, by
implementing a control policy and seizure burden calculation after
classification, allows for an even greater means of accurately
determining seizures by removing false positives or inaccurate
readings from the EEG signals provided.
[0005] An aspect of the disclosure provides a method for seizure
detection. The method may include receiving a plurality of
electroencephalography (EEG) signals over a plurality of channels
for a subject. The method may further include preprocessing the
plurality of EEG signals by segmenting the plurality of EEG signals
for each channel into a plurality of temporal data segments. In
some cases, the method may extract a plurality of features from
each temporal data segment for each channel. In some cases, the
method may apply a machine learning algorithm to the plurality of
features to perform a seizure binary classification for each
temporal data segment for each channel.
[0006] In some embodiments, the preprocessing of the plurality of
EEG signals may further comprise applying a filter to the plurality
of EEG signals over the plurality of channels, prior to the
segmentation of the plurality of EEG signals. In some cases, the
filter may comprise a bandpass filter configured to filter the
plurality of EEG signals between 1 Hz and 35 Hz.
[0007] In some embodiments, the seizure binary classification may
comprise classifying each temporal data segment for each channel as
(1) seizure-positive or (2) seizure-negative. In some cases, each
temporal data segment may be associated with a time epoch. In some
cases, a cluster of seizure-positive classifications may be
indicative of a potential electrographic seizure for the
corresponding time epoch. In some cases, the method may further
comprise comparing the classifications sequentially across a
plurality of time epochs on each channel and discarding a subset of
the classifications if the subset comprises fewer than three
seizure-positive classifications in a row. In some cases, the
method may further comprise classifying a particular time epoch as
associated with a potential electrographic seizure, if the temporal
data segments for a subset of the plurality of channels are
classified as seizure-positive. In some cases, the subset may
comprise at least half of the plurality of channels. In some cases,
the subset may comprise at least half of the plurality of
channels.
[0008] In some embodiments, the method may further comprise
aggregating the seizure binary classifications for the plurality of
temporal data segments for the plurality of channels over a moving
time window. In some cases, the moving time window may range from
about one minute to ten minutes. In some cases, the moving time
window may be about five minutes. In some cases, the plurality of
channels may comprise at least three channels. In some cases, the
plurality of channels may comprise eight channels. In some
embodiments, each temporal data segment may have a duration ranging
from about one second to twenty seconds. In some cases, the
duration of each temporal data segment may be about ten
seconds.
[0009] In some embodiments, the plurality of features may comprise
time and/or frequency domain features that are intrinsic in the
plurality of EEG signals. In some cases, the plurality of features
may comprise at least twenty different time and/or frequency
features. In some cases, the plurality of features may comprise a
plurality of discrete values associated with the time and/or
frequency domain features.
[0010] In some embodiments, the machine learning algorithm may
comprise a random forest, a boosted decision tree, a classification
tree, a regression tree, a bagging tree, a neural network, or a
rotation forest. In some cases, the machine learning algorithm may
be individually applied to the plurality of features extracted for
each channel, such that each channel has a separate iteration of
the machine learning algorithm.
[0011] In some embodiments, the method may further comprise
determining a seizure burden for the moving time window based on
the aggregated seizure binary classifications. In some cases, the
seizure burden may comprise a percentage of the time epochs that
are classified as seizure-positive. In some cases, determining the
seizure burden may comprise averaging the seizure-positive
classifications over the moving time window.
[0012] In some embodiments, the method may further comprise
generating one or more notifications when the seizure burden is
equal to or exceeds one or more thresholds. In some cases, the one
or more notifications may be usable by a healthcare practitioner to
assess whether the subject is at risk of having a seizure. In some
cases, the one or more notifications may be generated in the form
of visual, audio, and/or textual alerts. In some cases, a first
notification indicative of frequent seizure activity may be
generated when the seizure burden is equal to or exceeds a first
threshold of 10%. In some cases, a second notification indicative
of abundant seizure activity may be generated when the seizure
burden is equal to or exceeds a second threshold of 50%. In some
cases, a third notification indicative of continuous seizure
activity may be generated when the seizure burden is equal to or
exceeds a third threshold of 90%.
[0013] In another aspect, the present disclosure provides a seizure
detection system. The seizure detection system may include a
preprocessing module configured to receive a plurality of
electroencephalography (EEG) signals over a plurality of channels
for a subject. The preprocessing module may also be configured to
preprocess the plurality of EEG signals by segmenting the plurality
of EEG signals for each channel into a plurality of temporal data
segments. The seizure detection system may also include a
processing module in communication with the preprocessing module.
The processing module may be configured to receive the plurality of
temporal data segments corresponding to the plurality of channels.
The processing module may be configured to also extract a plurality
of features from each temporal data segment for each channel. The
processing module may be also configured to apply a machine
learning algorithm to the plurality of features to perform a
seizure binary classification for each temporal data segment for
each channel.
[0014] In some embodiments, the seizure detection system may
further comprise an output module in communication with the
preprocessing module. The output module may be configured to
aggregate the seizure binary classifications for the plurality of
temporal data segments for the plurality of channels over a moving
time window. The output module may be also configured to determine
a seizure burden for the moving time window based on the aggregated
seizure binary classifications. The output module may be also
configured to generate one or more notifications when the seizure
burden is equal to or exceeds one or more thresholds.
[0015] In another aspect, the present disclosure provides a system
comprising one or more computer processors. The system may also
comprise memory comprising machine-executable instructions that,
upon execution by the one or more computer processors, implements a
method for seizure detection. The method may comprise receiving a
plurality of electroencephalography (EEG) signals over a plurality
of channels for a subject. The method may also comprise
preprocessing the plurality of EEG signals by segmenting the
plurality of EEG signals for each channel into a plurality of
temporal data segments. The method may also further comprise
extracting a plurality of features from each temporal data segment
for each channel. The method may also further comprise applying a
machine learning algorithm to the plurality of features to perform
a seizure binary classification for each temporal data segment for
each channel.
[0016] In another aspect, the present disclosure provides a
non-transitory computer readable-medium comprising
machine-executable instructions that, upon execution by one or more
computer processors, implements a method for seizure detection. The
method may comprise receiving a plurality of electroencephalography
(EEG) signals over a plurality of channels for a subject. The
method may comprise preprocessing the plurality of EEG signals by
segmenting the plurality of EEG signals for each channel into a
plurality of temporal data segments. The method may include
extracting a plurality of features from each temporal data segment
for each channel. The method may also include applying a machine
learning algorithm to the plurality of features to perform a
seizure binary classification for each temporal data segment for
each channel.
[0017] In another aspect, the present disclosure provides a method
for seizure detection. The method may include receiving a plurality
of electroencephalography (EEG) signals over a plurality of
channels for a subject. The method may also include preprocessing
the plurality of EEG signals by segmenting the plurality of EEG
signals for each channel into a plurality of temporal data
segments. The method may also include extracting a plurality of
features from each temporal data segment for each channel. The
method may also include applying a machine learning algorithm to
the plurality of features to perform a seizure binary
classification for each temporal data segment for each channel. The
method may also further include determining a seizure burden for
the moving time window based on the aggregated seizure binary
classifications. The method may also further include presenting the
seizure burden to the user.
[0018] Additional aspects and advantages of the present disclosure
will become readily apparent to those skilled in this art from the
following detailed description, wherein only illustrative
embodiments of the present disclosure are shown and described. As
will be realized, the present disclosure is capable of other and
different embodiments, and its several details are capable of
modifications in various obvious respects, all without departing
from the disclosure. Accordingly, the drawings and description are
to be regarded as illustrative in nature, and not as
restrictive.
INCORPORATION BY REFERENCE
[0019] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference. To the extent publications and patents
or patent applications incorporated by reference contradict the
disclosure contained in the specification, the specification is
intended to supersede and/or take precedence over any such
contradictory material.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings (also "figure" and
"FIG." herein), of which:
[0021] FIG. 1 shows an EEG device configured to provide EEG signals
to a seizure detection module, in accordance with embodiments of
the present disclosure.
[0022] FIG. 2 shows an illustration of the EEG signal work flow for
seizure detection, in accordance with embodiments of the present
disclosure.
[0023] FIG. 3 shows a seizure burden graph, in accordance with
embodiments of the present disclosure.
[0024] FIG. 4 shows an illustration of the EEG device with a
display visualization of the seizure detection output, in
accordance with embodiments of the present disclosure.
[0025] FIG. 5 shows an illustration of the EEG device software with
the a display visualization of the seizure detection output, in
accordance with embodiments of the present disclosure.
[0026] FIG. 6 shows a computer system that is programmed or
otherwise configured to implement methods provided herein, in
accordance with embodiments of the present disclosure.
DETAILED DESCRIPTION
[0027] While various embodiments of the invention have been shown
and described herein, it will be obvious to those skilled in the
art that such embodiments are provided by way of example only.
Numerous variations, changes, and substitutions may occur to those
skilled in the art without departing from the invention. It should
be understood that various alternatives to the embodiments of the
invention described herein may be employed.
[0028] Whenever the term "at most about" or "at least about"
precedes the first numerical value in a series of two or more
numerical values, the term "at most about" or "at least about"
applies to each of the numerical values in that series of numerical
values. For example, at most about 3, 2, or 1 is equivalent to at
most about 3, at most about 2, or at most about 1.
Overview
[0029] Manual inspection of electroencephalography (EEG) brain
signals can be a time-consuming and laborious process. Valuable
time can be lost manually analyzing such EEG signals when a patient
may be experiencing a seizure. Automatic techniques that can
analyze EEG signals can help provide a timely and accurate
diagnosis of seizure activity to assist clinicians to initiate
therapy and reduce the risk of future seizures and seizure-related
complications.
[0030] Machine learning algorithms provide an avenue to
automatically classify EEG signals and minimize expert
intervention. Though machine learning algorithms require good
quality EEG signals to provide effective classification results.
Additionally, the EEG signals provided to the machine learning
algorithm may need to be given as features that describe a
characteristic of the EEG signal that pertains to seizure activity.
Furthermore, post-classification of the features by the machine
learning algorithm, a control policy comprising a set of rules
along with a seizure burden calculation allows the method and/or
system to more accurately depict that the subject is experiencing
or potentially experiencing a seizure.
[0031] The seizure detection system and methods described herein
provide a user with an EEG detection device coupled to a seizure
detection module capable of automatic and accurate seizure
detection. Additionally, the seizure detection module is capable of
notifying the user of an impending/active seizure. The seizure
detection module intakes and preprocesses the EEG signals from an
EEG device. The EEG signals are then analyzed and valuable EEG
features are extracted. The EEG features are classified using a
machine learning algorithm module. The classification of the
features for a given time epoch are then governed by a control
policy to calculate a seizure burden value. If the seizure burden
value is equal to or exceeds one or more thresholds, the method
and/or system generates one or more notifications that can used by
a healthcare practitioner to assess whether the subject may be at
risk of having a seizure. Increasing values of seizure burden may
be used by a healthcare practitioner as an indication of an
increasing severity of seizures. Seizure burden values equal to or
exceeding one or more thresholds may be used by a healthcare
practitioner as an indication of a medical condition, for example,
status epilepticus. The change in seizure burden over time, or the
characteristic shape of a seizure burden graph, may be used by a
healthcare practitioner to determine the course of treatment for
the subject or to evaluate the effectiveness of a course of
treatment for the subject.
Seizure Detection Module
[0032] I. Signal acquisition and pre-processing [0033] II. Signal
analysis [0034] III. Seizure burden calculation and output [0035]
IV. Post Seizure Detection [0036] V. Computer systems
I. Signal Acquisition and Pre-Processing
(a) EEG Signals/Acquisition
[0037] For ease of explanation, the figures and corresponding
description below are described below with reference to analysis of
signals representing brain activity (e.g., electroencephalography
(EEG) signals) and/or heart activity (e.g., electrocardiography
(ECG) signals) of a living subject. However, one of skill in the
art will recognize that signals representing other bodily functions
(e.g., an electromyography (EMG) signal, or an
electronystagmography (ENG) signal, a pulse oximetry signal, a
capnography signal, and/or a photoplethysmography signal) may be
substituted, or used in addition to (e.g., in conjunction with),
one or more signals representing brain activity and/or heart
activity.
[0038] A system for measuring bioelectrical signals may generally
comprise one or more electrodes electrically coupled via
corresponding conductive wires to a controller and/or output
device. In other variations, the electrodes may be coupled to the
controller and/or output device wirelessly. The electrodes may be
contained within an electrode carrier system that is secured around
the head of the patient. The electrode carrier system may be
configured as a headband or incorporated into any number of other
platforms or positioning mechanisms for maintaining the electrodes
against the patient body. Individual electrode assemblies may be
spaced apart from one another so that, when the headband is
positioned upon the patient's head, the electrode assemblies may be
aligned optimally for receiving EEG signals.
[0039] The controller and/or output device may generally comprise
any number of devices for receiving the electrical signals such as
electrophysiological monitoring devices and may also be used in
combination with any number of brain imaging devices, e.g., fMRI,
PET, NIRS, etc. In one particular variation, the electrode
embodiments described herein may be used in combination with
devices such as those which are configured to receive electrical
signals from the electrodes and process them.
[0040] In some embodiments, signals corresponding to brain
electrical activity are obtained from a human brain and correspond
to electrical signals obtained from a single neuron or from a
plurality of neurons. In some embodiments, sensors include one or
more sensors affixed (e.g., taped, attached, glued) externally to a
human scalp (e.g., extra-cranial sensor). For example, an
extra-cranial sensor may include an electrode (e.g.,
electroencephalography (EEG) electrode) or a plurality of
electrodes (e.g., electroencephalography (EEG) electrodes) affixed
externally to the scalp (e.g., glued to the skin via conductive
gel), or more generally positioned at respective positions external
to the scalp Alternatively, dry electrodes can be used in some
implementations (e.g., conductive sensors that are mechanically
placed against a living subject's body rather than planted within
the living subject's body or held in place with a conductive gel).
An example of a dry-electrode is a headband with one or more
metallic sensors (e.g., electrodes) that is worn by the living
subject during use. The signals obtained from an extra-cranial
sensor may sometimes be called EEG signals or time-domain EEG
signals. In some cases, a sensor may be an accelerometer or an
inertial measurement unit (IMU) that may measure the mechanical
movement of the subject and/or the device (e.g., produce one or
more electrical signals corresponding to mechanical movement of the
subject and/or device). The system may be configured to utilize one
or more sensors to aid in seizure detection as described elsewhere
herein.
[0041] In an aspect, the present disclosure provides a method for
seizure detection. In some cases, the method may include receiving
a plurality of signals (e.g. EEG signals, EKG signals, EMG signals,
etc) over a plurality of channels for a subject. The method may
include receiving a plurality of electroencephalography (EEG)
signals over a plurality of channels for a subject. The plurality
of EEG signals may be provided to a seizure detection module. FIG.
1 shows an illustration of the workflow of EEG signal collection by
the EEG device module 110 to the seizure detection module 115. FIG.
2 shows an in-depth illustration of the workflow by the EEG device
module and seizure detection module for seizure prediction. As
shown in FIG. 2, the seizure detection module can comprise a
pre-processing module, signal analysis module, and seizure burden
calculation and output module. The EEG device module 110 can have a
plurality of channels 205 for EEG signal acquisition from a
subject. In some cases, the plurality of channels may have between
1 to 256 channels. In some cases, the plurality of channels may
have between 8 to 256 channels. In some cases, the plurality of
channels may have more than 256 channels. In some cases, the
plurality of channels may have 8, 10, 16, 20, 32, 64, 128, or 256
channels.
(b) Preprocessing of EEG Signals
[0042] In some embodiments, the EEG device module may have one or
more analog front ends configured to receive sensor EEG signals
from sensors. The EEG signals may be preprocessed as described
elsewhere herein. In some embodiments, a separate (e.g.,
independent) analog front end may be provided for interfacing with
each of a set of sensors. In some embodiments, one or more analog
front ends may be provided for interfacing with a set of EEG
sensors.
[0043] In some embodiments, the method may include preprocessing
the plurality of signals by segmenting the plurality of signals for
each channel into a plurality of temporal data segments. In some
embodiments, the method may include preprocessing the plurality of
EEG signals by segmenting the plurality of EEG signals for each
channel into a plurality of temporal data segments. FIG. 2 shows an
illustration of the seizure detection module 120. The seizure
detection module intakes EEG signals from a plurality of channels
from the EEG device module. The seizure detection module may
preprocess the EEG signals from a plurality of channels with a
preprocessing module 210 configured to preprocess EEG signals. As
shown in FIG. 2, the preprocessing module can include a signal
filtering module 215, signal segmenting module 220, and signal
adjustment module 225.
[0044] In some embodiments, the filtering module 215 may be
configured to may filter EEG signals from the incoming set of
channels from the EEG device module as described elsewhere herein.
In some cases, preprocessing may be, for example, segmenting the
EEG signals, filtering the EEG signals based on frequency,
adjusting the EEG signals, or as described elsewhere herein,
etc.
[0045] In FIG. 2, the signal segmentation module 220 can be
configured to segment EEG signals. In some embodiments, the
plurality of EEG signals may be segmented to between 1 to 100000
data segments. In some cases, the number of EEG data segments may
depend on the duration of the EEG recordings. In some cases, the
number of EEG data segments may be fixed regardless of the duration
of the EEG recordings.
[0046] In some embodiments, each temporal data segment may have a
duration of between about 1 second to 1 hour. In some cases, each
temporal data segment may have a duration of between about 1 second
to 30 seconds. In some cases, each temporal data segment may have a
duration of between about 1 second to 10 seconds. In some cases,
the duration of each temporal data segment may be fixed for the
entire EEG recording. In some cases, the duration of each temporal
data segment may be variable or adaptive during an EEG
recording.
[0047] In some embodiments, the preprocessing of the plurality of
EEG signals may comprise applying one or more filtering steps to
the plurality of EEG signals over the plurality of channels. The
preprocessing of the plurality of EEG signals may comprise using at
least 1 filter, 2 filters, 3 filters, 4 filters, 5 filters, 6
filters, 7 filters, 8 filters, 9 filters, 10 filters, 15 filters or
more. The preprocessing of the plurality of EEG signals may
comprise using at most about 15 filters, 10 filters, 9 filters, 8
filters, 7 filters, 6 filters, 5 filters, 4 filters, 3 filters, 2
filters or less. The preprocessing of the plurality of EEG signals
may comprise using anywhere between 1 to 15 filters, 1 to 10
filters, 1 to 5 filters, or 1 to 3 filters.
[0048] In some embodiments, the one or more filtering steps may be
applied before, during, and/or after the segmentation of the
plurality of EEG signals. One or more of the filtering steps may
include, for example, a digital filter, an analogue filter, or a
combination thereof. One or more of the filtering steps may
include, for example, a bandpass filter, low-pass filter, a
high-pass filter, a band-stop filter, an all-pass filter, a Kalman
filter, an adaptive filter, or a notch filter, etc. In some cases,
the low frequency cutoff of the filters may be between 0.1 Hz and 5
Hz. In some cases, the high frequency cutoff of the filters may be
between 5 Hz and 200 Hz. In some cases, the notch filter frequency
may match the local power line frequency. In some cases, the notch
filter frequency may be 50 Hz or 60 Hz to match the local power
line frequency.
[0049] In some embodiments, each temporal data segment may be
associated with a time epoch. For each corresponding time epoch, a
cluster of seizure-positive classifications may be indicative of a
potential electrographic seizure. In some cases, a cluster of
seizure-positive classifications may comprise of between about 1 to
50 seizure positive classifications. In some cases, a cluster of
seizure positive classifications may comprise of between 1 to 10
seizure positive calculations.
[0050] In some embodiments, the method may further comprise
comparing the classifications sequentially across a plurality of
time epochs on each channel. In some cases, before/after/during
comparing the classifications sequentially across a plurality of
time epochs on each channel, the classifications sequentially
across a plurality of time epochs on each channel may be discarded.
In some cases, a subset of the classifications may be discarded. In
some cases, a subset of fewer than about 1 to 20 classifications
may be discarded. In some cases, a subset of fewer than 3
classifications may be discarded.
[0051] In some embodiments, the subset of seizure-positive
classification may be discarded because, for example, they may be
random readings, of low reliability, inaccurate classification,
incorrect classification, calibration, system error, disconnected
electrodes, artifactual signals, system interference, or other
signals, etc.
[0052] In some embodiments, the subset of seizure-positive
classification may be discarded to, for example, conserve memory
space, improve processing speed, reduce energy usage, reduce heat
of the system, reduce calculation costs, save processing power,
save processing time, increase reliability, or decrease random
access memory usage, etc
[0053] In some embodiments, the greater number of seizure-positive
classifications in a row may be indicative of high reliability. The
greater the reliability of seizure-positive classifications, the
more accurate determination of detecting a seizure in a patient. In
some cases, the greater reliability of seizure-positive
classifications may be indicative of the machine learning algorithm
accuracy, quality of data (EEG signals), or health status of the
EEG detecting system, etc. In some embodiments, a particular time
epoch may be classified as associated with a potential
electrographic seizure if the temporal data segments for a subset
of the plurality of channels are classified as seizure-positive. In
some cases, the subset may be at least 5%, 10%, 20%, 30%, 40%, 50%
or more of the plurality of channels. In some cases, the subset may
be at most about 50%, 40%, 30%, 20%, 10%, 5%, or less of the
plurality of channels.
(c) EEG Signal Adjustment
[0054] FIG. 2 shows a signal adjustment module 225 configured to
adjust an EEG signal. In some embodiments, the method may adjust
any EEG signal. Adjusting an EEG signal may include, for example,
increasing and/or decreasing the amplitude of the EEG signal,
adding or decreasing the noise level of the EEG signal, increasing
and/or decreasing the time epoch the EEG signal, increasing and/or
decreasing the intensity of the EEG signal, increasing and/or
decreasing the signal frequency of the EEG signal, increasing
and/or decreasing the voltage of the EEG signal, changing the
morphology of the EEG signal (e.g. the shape of the EEG signal),
increasing and/or decreasing the periodicity of the EEG signal,
increasing or decreasing the synchrony of the EEG wave, spectral
subtraction, standardizing etc.
[0055] In some cases, the EEG signal may be reduced. In some cases,
the EEG signal may be down-sampled to a lower sampling frequency.
For example, EEG data recorded at a sampling frequency of 500 Hz
may be down sampled by a factor of 2 to 250 Hz.
[0056] In some cases, the EEG signal may be subjected to bit-width
reduction. In some cases, the level of resolution at which the EEG
signals are recorded may not be required by the method to achieve
accurate seizure detection. In some cases, the bit-width reduction
may reduce the EEG signal to a lower number of bits per sample
through standard quantization of the EEG signal, for example, from
32 bits per sample to 12 bits per sample. In some cases, bit-width
reduction may be advantageous if the method is to be implemented in
a portable system, as it may be useful for reducing power
consumption due to decreased processing load.
[0057] In some cases, spectral subtraction may be used to reduce
the amount of additive noise in the EEG signal. In some cases, the
noise may be caused by external surroundings. In some cases, the
noise may be caused by the measurement equipment. In some cases,
the noise may be caused by the user. In some cases, an average
frequency spectrum of non-seizure EEG signal may be computed over a
period of time to provide a base level estimate of the noise
frequency spectrum. In some cases, as the EEG signals are recorded,
the EEG signals may be converted to the frequency domain. In some
cases, the average noise spectrum may then be subtracted from the
EEG frequency spectrum. In some cases, the resulting spectrum and
phase information from the original noisy signal may be combined.
In some cases, the resulting spectrum may be transformed back into
time domain to produce a de-noised signal.
[0058] In some embodiments, the EEG signal may be standardized by
eliminating the effect of the montage that was used in gathering
the EEG signals. In some cases, independent component analysis
(ICA) or principal component analysis (PCA) methods may be used to
provide the montage elimination. In some cases, the ICA or PCA
method may separate the EEG signal into a set of sources
independent of the montage used to record them. In some cases,
using standardized EEG data may remove errors introduced by the
varying practices of clinicians.
[0059] In some cases, a non-negative matrix factorisation (NMF)
method may be applied to each channel as a form of artifact
removal. In some cases, the spectrum of the signal may be
decomposed into the extracted bases to obtain weights. In some
cases, the spectrum may be reconstructed using the bases of
artifacts and the corresponding weights removed from the initial
EEG signal.
II. Signal Analysis
(a) Feature Extraction
[0060] In FIG. 2, the seizure detection module may comprise a
signal analysis module. The signal analysis module may comprise a
feature extraction module 245 and a machine learning classification
module 250. The feature extraction module 245 may be configured to
take preprocessed measured data (e.g. EEG signals) from the
preprocessing module 210 to build derived values (e.g. features).
In some embodiments, feature extraction may start from an initial
set of measured data (e.g. EEG signals, EEG signals of a given time
epoch, etc) and may build derived values (e.g. features) intended
to be informative and non-redundant. In some cases, the feature
extraction module may include extracting a plurality of features
from each temporal data segment for each channel individually. In
some cases, the feature extraction module may include extracting a
plurality of features from each temporal data segment for all
channels together. In some cases, the feature extraction module may
include extracting a plurality of features from each temporal data
segment of one or more groupings with each grouping consisting of
one or more channels. As shown in FIG. 2, the extracted features
can be relayed to a machine learning classification module 250 that
may be configured to analyze and classify the extracted features as
described elsewhere herein. In some cases, feature extraction may
facilitate the subsequent learning and generalization steps of a
machine learning algorithm. In some cases, feature extraction may
lead to better human interpretations. In some cases, feature
extraction may be related to dimensionality reduction.
[0061] In some cases, when the input data (e.g. EEG signals) to the
machine learning algorithm is too large to be processed and
suspected to be redundant (e.g. the same measurement in both Hz and
seconds, or the repetitiveness of a characteristic), the data can
be transformed into a reduced set of features.
[0062] In some cases, determining a subset of the initial features
may be called feature selection. In some cases, the selected
features may be expected to contain the relevant information from
the input data (e.g. EEG signals). In some cases, the selected
features may be expected to contain the relevant information from
the input data so that the desired task can be performed by using
this reduced representation instead of the complete initial
data.
[0063] In some embodiments, feature extraction may involve reducing
the number of resources required to describe a large set of data
(e.g. EEG signals). In some cases, analysis with a large number of
variables may require a large amount of memory and computation
power. In some cases, it may cause a machine learning algorithm to
overfit to training samples and generalize poorly to new samples.
In some cases, feature extraction may construct combinations of the
variables to accurately describe the data with sufficient accuracy.
In some cases, feature extraction may construct combinations of the
variables to accurately describe the data with sufficient accuracy
while preventing overfitting.
[0064] In some embodiments, results may be improved using
constructed sets of application-dependent features. In some cases,
the constructed sets may be built by an expert. In some cases,
general dimensionality reduction techniques may be used. In some
cases, general dimensionality reduction techniques may be, for
example, independent component analysis, isomap, kernel PCA, latent
semantic analysis, partial least squares, principal component
analysis, multifactor dimensionality reduction, nonlinear
dimensionality reduction, multilinear principal component analysis,
multilinear subspace learning, semidefinite embedding, autoencoder,
etc.
[0065] In some cases, a set of numeric features may be described by
a feature vector. In some cases, a feature vector may be an
n-dimensional vector of numerical features that represent some
object.
[0066] In some embodiments, data analysis software packages may
provide for feature extraction. In some cases, data analysis
software packages may provide for dimension reduction. In some
cases, data analysis software packages may include programming
environments such as MATLAB, SciLab, NumPy, or the R language, etc.
In some cases, a programming language script may be used to extract
features from EEG signals. In some cases, the programming language
script may be, for example, MATLAB, python, java, javascript, Ruby,
C, C++, or Perl, etc.
[0067] In some cases, the plurality of features may be intrinsic in
the plurality of EEG signals. Intrinsic may be a feature of an EEG
signal that may be measured, for example, the amplitude of the EEG
signal, the duration of the EEG signal, the variation of the EEG
signal, the power of the EEG signal, the local maxima/minima of the
EEG signal, the pattern of the EEG signal, the regularity of the
EEG signal, the spectral power distribution of the EEG signal, or
the frequency of the EEG signal, etc. In some cases, the plurality
of features may be a measurement of the power of a signal with a
particular frequency. The frequency may be, for example, from about
0 Hz to 100 Hz. In some cases, the power of a signal may be
normalized to a total power. In some cases, the power of a signal
may be a ratio of power between one or more signals. In some cases,
a feature may be a function performed on a signal to obtain a
value. For example, a function may measure the root mean square
(RMS) of a signal (e.g. EEG signal) to obtain the RMS value of the
signal. In some cases, a feature may compare one signal (e.g. EEG
signal) to one or more signals. In some cases, a feature may
compare one or more signals (e.g. EEG signals) to one or more
signals. In some cases, a feature may measure an attribute of a
signal (e.g. EEG signal). In some cases, a feature may compare one
or more attributes of a signal (e.g. EEG signal) with one or more
attributes of a signal. An attribute may be, for example, an
intrinsic property of the EEG signal. In some case, the feature of
an EEG signal may be continuous and/or discrete in time.
[0068] In some cases, the plurality of features may include at
least twenty different time and/or frequency features. In some
cases, the plurality of features may include at most one thousand
time and/or frequency features. In some cases, the plurality of
features may include between about 10 features to 200 features. In
some cases, the plurality of features may include between about 10
features to 100 features. In some cases, the plurality of features
may include between about 10 features to 50 features.
[0069] In some cases, the plurality of features may include a
plurality of discrete values associated with the time domain,
frequency domain, time-frequency domain, information theory, and
nonlinear-dynamics system theory features. In some cases, the
plurality of features may include a plurality of discrete values
associated with the time and/or frequency domain features. The
plurality of features may include a plurality of continuous values
associated with the time and/or frequency domain features.
[0070] In some cases, the plurality of signals may be converted
into a digital signal. In some cases, the plurality of signal may
be converted into a digital signal and then an analog signal.
[0071] In some cases, the features may be sampled from a portion of
the EEG signal. Features may be sampled from a portion of the EEG
signal to reduce processing time and power required.
[0072] In some embodiments, a feature may be pertaining to a
certain weight value. The weight value may give one feature a
higher score for detecting a seizure. The higher score may indicate
that the feature may be more relevant in predicting seizure
activity. The method may adjust the weight value of any feature at
any given time. The method may adjust by increasing and/or
decreasing the weight value of any feature at any given time.
(b) Classification Using Machine Learning
[0073] In some embodiments, the method may include applying a
machine learning algorithm to the plurality of features to perform
a seizure classification for each temporal data segment for each
channel individually. In some cases, the machine learning
classification module may include performing seizure classification
for each temporal data segment for all channels together. In some
cases, the machine learning classification module may include
performing seizure classification for each temporal data segment of
one or more groupings with each grouping consisting of one or more
channels. FIG. 2 shows the machine learning classification module
250 that may take the features collected/extracted from the
preprocessing step and classify the features. In some cases, the
features may be extracted without a preprocessing step.
[0074] In some cases, machine learning algorithms may need to
extract and draw relationships between features as conventional
statistical techniques may not be sufficient. In some cases,
machine learning algorithms may be used in conjunction with
conventional statistical techniques. In some cases, conventional
statistical techniques may provide the machine learning algorithm
with preprocessed features.
[0075] In some embodiments, the plurality of features may be
classified into any number of categories. A temporal segment may be
classified as, for example, seizure-positive, seizure-negative,
seizure-like, uncertain seizure activity, etc. In some cases, the
plurality of features may be classified into between 1 to 20
categories. Individual categories may also be divided into
sub-categories. For example, a temporal segment classified as
seizure-positive may be further sub-divided into focal versus
generalized seizure events.
[0076] In some embodiments, the method may include applying a
machine learning algorithm to the plurality of features to perform
a seizure binary classification for each temporal data segment for
each channel.
[0077] In some embodiments, the one or more features collected may
be discarded prior to or during machine learning
classification.
[0078] In some embodiments, a human may select, and discard
features prior/during machine learning classification. In some
cases, a computer may select and discard features. In some cases,
the features may be discarded based on a threshold value.
[0079] In some embodiments, any number of features may be
classified by the machine learning algorithm. The machine learning
algorithm may classify at least 10 features. In some cases, the
plurality of features may include between about 10 features to 200
features. In some cases, the plurality of features may include
between about 10 features to 100 features. In some cases, the
plurality of features may include between about 10 features to 50
features In some embodiments, the machine learning algorithm may
be, for example, an unsupervised learning algorithm, supervised
learning algorithm, or a combination thereof. The unsupervised
learning algorithm may be, for example, clustering, hierarchical
clustering, k-means, mixture models, DBSCAN, OPTICS algorithm,
anomaly detection, local outlier factor, neural networks,
autoencoders, deep belief nets, hebbian learning, generative
adversarial networks, self-organizing map, expectation-maximization
algorithm (EM), method of moments, blind signal separation
techniques, principal component analysis, independent component
analysis, non-negative matrix factorization, singular value
decomposition, or a combination thereof. The supervised learning
algorithm may be, for example, support vector machines, linear
regression, logistic regression, linear discriminant analysis,
decision trees, k-nearest neighbor algorithm, neural networks,
similarity learning, or a combination thereof. In some embodiments,
the machine learning algorithm may comprise a deep neural network
(DNN). The deep neural network may comprise a convolutional neural
network (CNN). The CNN may be, for example, U-Net, ImageNet,
LeNet-5, AlexNet, ZFNet, GoogleNet, VGGNet, ResNet18 or ResNet,
etc. Other neural networks may be, for example, deep feed forward
neural network, recurrent neural network, LSTM (Long Short Term
Memory), GRU (Gated Recurrent Unit), Auto Encoder, variational
autoencoder, adversarial autoencoder, denoising autoencoder, sparse
auto encoder, boltzmann machine, RBM (Restricted BM), deep belief
network, generative adversarial network (GAN), deep residual
network, capsule network, or attention/transformer networks,
etc.
[0080] In some embodiments, the machine learning algorithm may be,
for example, a random forest, a boosted decision tree, a
classification tree, a regression tree, a bagging tree, a neural
network, or a rotation forest. The machine learning algorithm may
be individually applied to the plurality of features extracted for
each channel, such that each channel may have a separate iteration
of the machine learning algorithm.
[0081] In some embodiments, the method may apply one or more
machine learning algorithms. In some embodiments, the method may
apply one or more one machine learning algorithms per channel.
[0082] In FIG. 2, the machine learning classification module 250
may comprise any number of machine learning algorithms. In some
embodiments, the random forest machine learning algorithm may be an
ensemble of bagged decision trees. In some cases, the ensemble of
bagged decision trees may classify each temporal data segment for
each channel as (1) seizure-positive or (2) seizure-negative. The
ensemble may be at least about 1, 2, 3, 4, 5, 10, 20, 30, 40, 50,
60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, 500, 1000 or
more bagged decision trees. The ensemble may be at most about 1000,
500, 250, 200, 180, 160, 140, 120, 100, 90, 80, 70, 60, 50, 40, 30,
20, 10, 5, 4, 3, 2 or less bagged decision trees. The ensemble may
be from about 1 to 1000, 1 to 500, 1 to 200, 1 to 100, or 1 to 10
bagged decision trees.
[0083] In some embodiments, the method may include applying a
machine learning classifier to any number of channels. The method
may include applying a machine learning classifier to at least
about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 50, 100, 500, 1000
or more channels. The method may include applying a machine
learning classifier to at most about 1000, 500, 100, 50, 25, 20,
15, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less channels. The method may
include applying a machine learning classifier from about 1 to
1000, 1 to 100, 1 to 25, or 1 to 5 channels.
[0084] In some embodiments, the method may include applying a
machine learning classifier to a subset of channels. The subset of
channels may be at least about 1%, 5%, 10%, 20%, 30%, 40%, 50% or
more of the total set of channels. The subset of channels may be at
most about 50%, 40%, 30%, 20%, 10%, 5%, 1% or less of the total set
of channels. The subset of channels may be from about 1% to 50%, 1%
to 40%, 1% to 30%, 1% to 20%, 1% to 10%, or 1% to 5% of the total
set of channels.
[0085] In some embodiments, the machine learning algorithm may have
a variety of parameters. The variety of parameters may be, for
example, learning rate, minibatch size, number of epochs to train
for, momentum, learning weight decay, or neural network layers
etc.
[0086] In some embodiments, the learning rate may be between about
0.00001 to 0.1.
[0087] In some embodiments, the minibatch size may be at between
about 16 to 128.
[0088] In some embodiments, the neural network may comprise neural
network layers. The neural network may have at least about 2 to
1000 or more neural network layers.
[0089] In some embodiments, the number of epochs to train for may
be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75,
80, 85, 90, 95, 100, 150, 200, 250, 500, 1000, 10000, or more.
[0090] In some embodiments, the momentum may be at least about 0.1,
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or more. In some
embodiments, the momentum may be at most about 0.9, 0.8, 0.7, 0.6,
0.5, 0.4, 0.3, 0.2, 0.1, or less.
[0091] In some embodiments, learning weight decay may be at least
about 0.00001, 0.0001, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006,
0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07,
0.08, 0.09, 0.1, or more. In some embodiments, the learning weight
decay may be at most about 0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04,
0.03, 0.02, 0.01, 0.009, 0.008, 0.007, 0.006, 0.005, 0.004, 0.003,
0.002, 0.001, 0.0001, 0.00001, or less.
[0092] In some embodiments, the machine learning algorithm may use
a loss function. The loss function may be, for example, regression
losses, mean absolute error, mean bias error, hinge loss, Adam
optimizer and/or cross entropy.
[0093] In some embodiments, the parameters of the machine learning
algorithm may be adjusted with the aid of a human and/or computer
system.
[0094] In some embodiments, the machine learning algorithm may
prioritize certain features. The machine learning algorithm may
prioritize features that may be more relevant for detecting
seizures. The feature may be more relevant for detecting seizures
if the feature is classified more often than another feature. In
some cases, the features may be prioritized using a weighting
system. In some cases, the features may be prioritized on
probability statistics based on the frequency and/or quantity of
occurrence of the feature. The machine learning algorithm may
prioritize features with the aid of a human and/or computer
system.
[0095] In some embodiments, one or more of the features may be used
with machine learning or conventional statistical techniques to
determine if a segment is likely to contain artifacts. FIG. 2 shows
the artifact rejection module 255 which identifies segments
containing artifacts. The identified artifacts may be a result of
electrical interference, electrode instability or movement, subject
movement, subject eye movement or blinking, subject chewing,
subject muscle tensing, subject electrocardiographic artifact, etc.
In some cases, movement sensors or other sensors may be used as an
additional input to the artifact rejection module. In some cases,
the identified artifacts can be rejected from being used in seizure
classification. In some cases, the identified artifacts can be
reduced, cancelled, or eliminated and the remaining signal may
still be processed for seizure classification.
[0096] In some cases, the machine learning algorithm may prioritize
certain features to reduce calculation costs, save processing
power, save processing time, increase reliability, or decrease
random access memory usage, etc.
III. Seizure Burden Calculation and Output
(a) Control Policy and Seizure Burden
[0097] In some embodiments, the seizure binary classification may
include classifying each temporal data segment for each channel as
(1) seizure-positive or (2) seizure-negative. The seizure binary
classification may use machine learning algorithms as described
elsewhere herein. The method may include aggregating the seizure
binary classifications for the plurality of temporal data segments
for the plurality of channels over a moving time window. The
aggregated seizure classifications may be subjected to a control
policy module 275 of the seizure burden calculation and output
module 270, as shown in FIG. 2. FIG. 2 shows the seizure burden
calculation and output module 270. As shown in FIG. 2, the seizure
burden calculation and output module 270 may comprise a control
policy module 275, a seizure burden calculation module 280, a
seizure burden plot module 285, and a seizure burden notification
module 290. The control policy module 275 may be configured to
implement a control policy, the seizure burden calculation module
280 may be configured to calculate a seizure burden value, the
seizure burden plot module 285 may be configured to plot seizure
burden values, and the seizure burden notification module 290 may
be configured to provide notifications as described elsewhere
herein, respectively.
[0098] In some cases, the moving window may have a period of time
between 1 minute and 1 hour. In some cases, the period of time of
the moving window may be dynamic or adjustable instead of fixed. In
some cases, the period of time of the moving window may be
dependent on the subject.
[0099] In some embodiments, a cluster of seizure-positive
classifications on one or more channels may be subjected to a
control policy module 275 to result in an overall determination of
a seizure for the patient for a corresponding time epoch.
[0100] The control policy may be a set of rules that result in an
overall determination of seizure for the patient for a
corresponding time epoch. The control policy may take a set of
parameters as input and act on the set of parameters according to
the set of rules to result in an overall determination of a seizure
for the patient for a corresponding time epoch. The set of rules
may be as described elsewhere herein. The set of rules may be
adjusted at any point of time to act on more parameters or to act
on less parameters. The set of rules may be adjusted at any point
of time to include more rules or to remove rules. The set of rules
may be at least about 1, 2, 3, 4, 5, 6 7, 8, 9, 10, 15, 20, 25, 50,
100, 500, 1000, or more rules. The set of rules may be at most
about 1000, 500, 100, 50, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2 or
less rules. The set of rules may be from about 1 to 1000, 1 to 500,
1 to 100, 1 to 25, 1 to 10, 1 to 5, or 1 to 3 rules.
[0101] In some embodiments, the input of parameters for the control
policy may include, the quantity of classification of channels as
seizure-positive, the quantity of classification of channels as
seizure-negative, the classification of channels as
seizure-positive, the classification of channels as
seizure-negative, the corresponding time epoch, the quantity of
channels, the machine learning algorithm used for classification, a
moving window time length, the quality of the connection of each
channel, information derived from EKG signals, information derived
from EMG signals, information regarding the patient's demographics,
information regarding the patient's current or previous condition,
information regarding treatments or medications applied to the
patient, information derived from movement sensors (e.g. an
accelerometer or inertial measurement unit), etc.
[0102] In some embodiments, the control policy may have any number
input of parameters. The control policy may have an input of at
least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20,
25, 50, 100, 500 1000 or more parameters. The control policy may
have an input of at most about 1000, 500, 100, 50, 25, 20, 15, 14,
13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less parameters. The
control policy may have an input from about 1 to 1000, 1 to 500, 1
to 100, 1 to 50, 1 to 25, 1 to 15, 1 to 10, or 1 to 5
parameters.
[0103] In some embodiments, the set of rules may dictate that the
control policy discards the classification of a channel. For
example, if the control policy receives an input of a single a
seizure-positive classification for a corresponding time epoch, the
set of rules may discard the seizure-positive classification for
the corresponding time epoch. In some cases, the control policy may
receive at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 500,
or more seizure-positive classifications and the set of rules may
discard each seizure-positive classification for the corresponding
time epoch. In some cases, the control policy may receive at most
about 500, 100, 50, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less
seizure-positive classifications and the set of rules may discard
each seizure-positive classification for the corresponding time
epoch. In some cases, the control policy may receive from about 1
to 500, 1 to 100, 1 to 50, 1 to 10, or 1 to 5 seizure-positive
classifications and the set of rules may discard each
seizure-positive classification for the corresponding time
epoch.
[0104] In some embodiments, the set of rules may dictate that the
control policy output a seizure-positive classification for a set
of channels corresponding to a time epoch. For example, if the
control policy receives a set of four or more channels that each
register a seizure-positive classification for the corresponding
time epoch, the set of rules may output a seizure-positive
classification for the corresponding time epoch. In some cases, the
control policy may receive a set of seizure-positive
classifications of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
15, 20, 25, 50, 100, 1000 or more channels, the set of rules may
output a seizure-positive classification for the set of
seizure-positive classifications for the corresponding time epoch.
In some cases, the control policy may receive a set of
seizure-positive classifications of at most about 1000, 100, 50,
25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, or less channels, the set
of rules may output a seizure-positive classification for the set
of seizure-positive classifications for the corresponding time
epoch. In some cases, the control policy may receive a set of
seizure-positive classifications from about 1 to 1000, 1 to 500, 1
to 100, 1 to 50, 1 to 25, 1 to 10, or 1 to 5, the set of rules may
output a seizure-positive classification for the set of
seizure-positive classifications for the corresponding time
epoch.
[0105] In some embodiments, the method may include calculating the
seizure burden of the patient as the percentage of seizure-positive
classifications within a specified period of time. As shown in FIG.
2, the seizure burden calculation module 280 may be configured to
calculate the seizure burden of the patient. In some cases, the
period of time used for seizure burden calculation may be between 1
minute and 1 hour. In some cases, the period of time used for
seizure burden calculation may be the entirety of the recording
session. In some cases, the period of time used for seizure burden
calculation may be dynamic or adjustable instead of fixed.
[0106] In some embodiments, the seizure burden may form a
continuous output measure by calculating seizure burden for a
moving window of time to result in a seizure burden value for
individual sequential periods of time. In some cases, the period of
time of the moving window may be between 1 minute and 1 hour. In
some cases, the period of time of the moving window may be dynamic
or adjustable instead of fixed. In some cases, the sequential
periods of time formed by the moving window may be overlapping. In
some cases, sequential periods of time formed by the moving window
may be non-overlapping. In some cases, the moving window may move
in time increments between 1 second and 1 hour. In some cases, the
moving window may pause or skip periods of time such that the
resulting seizure burden values are not continuous or not
sequential in time.
[0107] FIG. 2 shows a seizure burden plot module 285 configured to
plot the seizure burden of a subject. As shown in FIG. 3, the
seizure burden output may be displayed to the user as a time-series
plot 310 where each point represents the seizure burden for a
period of time. In some embodiments, the seizure burden output may
display the one or more thresholds (e.g. 10%, 50%, 90%, etc) to the
user on the time-series plot. In some embodiments, the seizure
burden output may be displayed to the user as a time-series plot,
bar graph, or chart etc. In some embodiments, the time-series plot
may be depicted in a certain color to note the threshold that has
been passed. As shown in FIG. 3, when the time-series plot of the
seizure burden passes the 50% threshold for a period of time, the
time-series plot may change from gray to orange. In some cases,
when the time-series plot passes 90%, the time-series plot may
change from orange to red. The time-series plot may be of any color
and the passing of a threshold may be illustrated in any color. In
some cases, the seizure burden plot module may display a wide
variety of information, for example, the time period measured, the
date, or the initial time acquisition, etc. In some cases, the
seizure burden plot may be usable by a healthcare practitioner to
assess the condition of the subject and determine a course of
treatment. The seizure burden plot may also be usable by a
healthcare practitioner to monitor the progression of the subject's
condition over time or to monitor the effectiveness of courses of
treatment.
[0108] FIG. 2 shows a seizure burden notifications module 290
configured to generate notifications. In some embodiments, the
method may include generating one or more notifications when
seizure-positive classifications have been made or when the seizure
burden value is equal to or exceeds one or more thresholds. As
shown in FIG. 4, when the seizure burden value is equal to or
exceeds a threshold (e.g. the 90% threshold), the system may
display to a subject (e.g. patient) or user (e.g. healthcare
practitioner, doctor, nurse, etc) a notification 410 that the
system has detected continuous seizure activity. The notification
may also include any color. For example, the background of the
screen displaying the notification may be red. The text of the
notification may be any color, for example, white. The color of the
background of the screen may correlate with the value of the
seizure burden calculation. For example, if the seizure burden is
equal to or above a certain threshold, the selected color for the
background of the screen may indicate that the seizure burden is
equal to or above a threshold. The color of the text of the
notification may correlate with the value of the seizure burden
calculation. For example, if the seizure burden is equal to or
above a certain threshold, the selected color for the text of the
notification may indicate that the seizure burden is equal to or
above a threshold.
[0109] The system may also display a wide variety of information to
the subject or user in addition to the notification of detected
continuous seizure activity. The system may display the seizure
burden plot 415, the percentage of seizure burden calculated 420,
the time period for which the continuous seizure activity was
detected (e.g. 7:40 pm to 7:50 pm), etc. The one or more
notifications may be usable by a healthcare practitioner to assess
the condition of the subject and determine a course of treatment.
In some cases, one or more notifications may be generated when the
seizure burden value is equal to or exceeds one or more thresholds
as described elsewhere herein. In some cases, the one or more
notifications may be generated in the form of visual, audio, and/or
textual alerts. The device may include speakers 425 to provide
audio notifications. In some cases, the one or more notifications
may be delivered via networked communication technology such as the
internet, telephone, facsimile, pager, short message service, etc.
In some cases, the form, content, or delivery mechanism of the one
or more notifications generated may depend on the seizure burden
value. In some cases, the user may be able to select the form,
content, or delivery mechanism of the one or more notifications
generated.
[0110] FIG. 5 shows an illustration showing seizure detection
output 510 provided by the methods and systems described herein.
The interface may provide indication of the EEG signal activity for
the plurality of channels from the EEG device module 110. FIG. 5
shows examples of parameters that a user may adjust, for example,
the time display, the scale, the high pass frequency, the low pass
frequency, or the notch value, etc. The interface may also provide
a seizure burden plot as described elsewhere herein. The interface
may also provide seizure burden results over different time
periods. The interface may also depict the seizure determination
for each time segment. The interface may also provide a mechanism
for the user to accept or reject the algorithm derived seizure
determination or seizure burden calculation. The interface may also
provide a mechanism for the user to input their own determination
of seizure containing segments or seizure burden. In some cases,
the seizure burden may be adjusted as a result of user entered
information regarding seizure episodes or seizure burden. The
displayed seizure burden and seizure burden notifications may be
based solely on algorithm derived seizure determination, solely on
user entered seizure determination, or on a combination of
algorithm and user seizure determination.
[0111] In some embodiments, the seizure burden calculation module
may calculate a seizure burden value. The seizure burden
notification module may output a notification if the seizure burden
value crosses a threshold value. For example, if the seizure burden
value crosses a threshold of 10%, a notification may be generated.
In another example, if the seizure burden value crosses a threshold
of 50%, a notification may be generated. In another example, if the
seizure burden value crosses a threshold of 90%, a notification may
be generated. In some cases, a first notification indicative of
frequent seizure activity may be generated when the seizure burden
is equal to or exceeds a first threshold of 10%. In some cases, a
second notification indicative of abundant seizure activity may be
generated when the seizure burden is equal to or exceeds a second
threshold of 50%. In some cases, a third notification indicative of
continuous seizure activity may be generated when the seizure
burden is equal to or exceeds a third threshold of 90%. In some
cases, notifications may be generated to a specific person that the
method is programmed to notify.
[0112] In some embodiments, the threshold for notification may be
of any percentage. The threshold for notification may be at least
about 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,
60%, 65%, 70%, 80%, 90%, 95%, 99% or more. The threshold for
notification may be at most about 99%, 95%, 90%, 85%, 80%, 75%,
70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%,
5%, 1% or less. The threshold for notification may be from about 0%
to 100%, 1% to 99%, 5% to 95%, 10% to 90%, 20% to 80%, 30% to 70%,
or 40% to 60%. The threshold for notification may also be user
adjustable.
[0113] In some embodiments, the seizure burden notification module
may have any number of thresholds. The seizure burden notification
module may have at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,
20, 25, 50, 100, 500, 1000, 5000 or more thresholds. The seizure
burden notification module may have at most about 5000, 1000, 500,
100, 50, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2 or less. The
seizure burden notification module may have from about 1 to 5000, 1
to 1000, 1 to 500, 1 to 100, 1 to 50, 1 to 25, 1 to 10, or 1 to 5
thresholds. The quantity of thresholds may be increased or
decreased at any point in time.
[0114] In some embodiments, the seizure burden notification module
may provide any number of notifications. The seizure burden
notification module may provide at least about 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 15, 20, 25, 30, 35, 40, 50, 100, 500, 1000 or more
notifications. The seizure burden notification module may provide
at most about 1000, 500, 100, 50, 40, 35, 30, 25, 20, 15, 10, 9, 8,
7, 6, 5, 4, 3, 2 or less notifications. The seizure burden
notification module may provide from about 1 to 1000, 1 to 500, 1
to 100, 1 to 50, 1 to 25, 1 to 10, or 1 to 5 notifications.
[0115] In some embodiments, the seizure burden calculation and
output module may criteria in addition to the seizure burden
threshold to output a notification. In some cases, the duration of
time that seizure burden is over a threshold may be used to
determine if a notification is output. In some cases, amount of
time since the previous crossing of a seizure burden threshold may
be used to determine if a notification is output. In some cases,
dynamic criteria may be applied with a combination of time based,
seizure burden based, and other policies to determine if a
notification is output.
[0116] In some embodiments, the system may be coupled with other
systems. In some cases, the systems may be eye trackers, movement
sensors (e.g. an accelerometer or an inertial measurement unit),
electromyography (EMG), electrocardiogram (ECG or EKG), etc.
IV. Post Seizure Detection
[0117] In some embodiments, the method may include generating one
or more notifications as described elsewhere herein.
[0118] In some embodiments, the method may provide a user a
response to minimize or prevent the detected seizure. The method
may provide a response to minimize or reduce the risk of the onset
of a seizure. In some cases, a therapeutic may be delivered to the
subject to prevent and/or mitigate the predicted seizure. In some
cases, a neuromodulation signal pattern may be applied to the
subject to prevent and/or mitigate the predicted seizure. In some
cases, the method may adjust the neuromodulation or quantity of
therapeutic delivered to the subject.
V. Computer Systems
[0119] The present disclosure provides computer systems that are
programmed to implement methods of the disclosure, including the
control of the seizure detection system, control hardware
components, receive and process data, interface with a user, etc. A
seizure detection system and its various components may include
computer hardware (and associated firmware) that may be
electrically connected to a computer control system. A control unit
may include such a computer system.
[0120] FIG. 6 shows a computer system 601 that is programmed or
otherwise configured to operate and/or control the EEG device
module and the seizure detection module. The computer system 601
can regulate various aspects of the seizure detection system of the
present disclosure, such as, for example, processing EEG signals,
segmenting EEG signals, extracting features, processing features
with machine learning algorithms, implementing the control policy
and seizure burden, calculating the seizure burden value, plotting
the seizure burden, providing notifications, etc. The computer
system 601 can be an electronic device of a user or a computer
system that is remotely located with respect to the electronic
device. The electronic device can be a mobile electronic
device.
[0121] The computer system 601 includes a central processing unit
(CPU, also "processor" and "computer processor" herein) 605, which
can be a single core or multi core processor, or a plurality of
processors for parallel processing. The computer system 601 also
includes memory or memory location 610 (e.g., random-access memory,
read-only memory, flash memory), electronic storage unit 615 (e.g.,
hard disk), communication interface 620 (e.g., network adapter) for
communicating with one or more other systems, and peripheral
devices 625, such as cache, other memory, data storage and/or
electronic display adapters. The memory 610, storage unit 615,
interface 620 and peripheral devices 625 are in communication with
the CPU 605 through a communication bus (solid lines), such as a
motherboard. The storage unit 615 can be a data storage unit (or
data repository) for storing data. The computer system 601 can be
operatively coupled to a computer network ("network") 630 with the
aid of the communication interface 620. The network 630 can be the
Internet, an internet and/or extranet, or an intranet and/or
extranet that is in communication with the Internet. The network
630 in some cases is a telecommunication and/or data network. The
network 630 can include one or more computer servers, which can
enable distributed computing, such as cloud computing. The network
630, in some cases with the aid of the computer system 601, can
implement a peer-to-peer network, which may enable devices coupled
to the computer system 601 to behave as a client or a server.
[0122] The CPU 605 can execute a sequence of machine-readable
instructions, which can be embodied in a program or software. The
instructions may be stored in a memory location, such as the memory
610. The instructions can be directed to the CPU 605, which can
subsequently program or otherwise configure the CPU 605 to
implement methods of the present disclosure. Examples of operations
performed by the CPU 605 can include fetch, decode, execute, and
writeback.
[0123] The CPU 605 can be part of a circuit, such as an integrated
circuit. One or more other components of the system 601 can be
included in the circuit. In some cases, the circuit is an
application specific integrated circuit (ASIC).
[0124] The storage unit 615 can store files, such as drivers,
libraries and saved programs. The storage unit 615 can store user
data, e.g., user preferences and user programs. The computer system
601 in some cases can include one or more additional data storage
units that are external to the computer system 601, such as located
on a remote server that is in communication with the computer
system 601 through an intranet or the Internet.
[0125] The computer system 601 can communicate with one or more
remote computer systems through the network 630. For instance, the
computer system 601 can communicate with a remote computer system
of a user (e.g., seizure detection system manager, seizure
detection system user, seizure detection data acquirer, seizure
detection system scribe, etc). Examples of remote computer systems
include servers, personal computers (e.g., portable PC), slate or
tablet PC's (e.g., Apple.RTM. iPad, Samsung.RTM. Galaxy Tab),
telephones, Smart phones (e.g., Apple.RTM. iPhone, Android-enabled
device, Blackberry.RTM.), or personal digital assistants. The user
can access the computer system 601 via the network 630. In some
cases, the EEG device module and the seizure detection module will
be located within the same computer system. In some cases, the EEG
device module will be located within one computer system which is
networked to a remote computer system containing the seizure
detection module. After performing seizure detection, the remote
computer system can then transmit seizure detection results to the
computer system containing the EEG device module as well as other
remote computer systems that may be used for seizure detection
results display. In some cases, different parts of the seizure
detection module may be located in different computer systems which
have been networked together.
[0126] Methods as described herein can be implemented by way of
machine (e.g., computer processor) executable code stored on an
electronic storage location of the computer system 601, such as,
for example, on the memory 610 or electronic storage unit 615. The
machine executable or machine readable code can be provided in the
form of software. During use, the code can be executed by the
processor 605. In some cases, the code can be retrieved from the
storage unit 615 and stored on the memory 610 for ready access by
the processor 605. In some situations, the electronic storage unit
615 can be precluded, and machine-executable instructions are
stored on memory 610.
[0127] The code can be pre-compiled and configured for use with a
machine having a processor adapted to execute the code, or can be
compiled during runtime. The code can be supplied in a programming
language that can be selected to enable the code to execute in a
pre-compiled or as-compiled fashion.
[0128] Aspects of the systems and methods provided herein, such as
the computer system 601, can be embodied in programming. Various
aspects of the technology may be thought of as "products" or
"articles of manufacture" typically in the form of machine (or
processor) executable code and/or associated data that is carried
on or embodied in a type of machine readable medium.
Machine-executable code can be stored on an electronic storage
unit, such as memory (e.g., read-only memory, random-access memory,
flash memory) or a hard disk. "Storage" type media can include any
or all of the tangible memory of the computers, processors or the
like, or associated modules thereof, such as various semiconductor
memories, tape drives, disk drives and the like, which may provide
non-transitory storage at any time for the software programming.
All or portions of the software may at times be communicated
through the Internet or various other telecommunication networks.
Such communications, for example, may enable loading of the
software from one computer or processor into another, for example,
from a management server or host computer into the computer
platform of an application server. Thus, another type of media that
may bear the software elements includes optical, electrical and
electromagnetic waves, such as used across physical interfaces
between local devices, through wired and optical landline networks
and over various air-links. The physical elements that carry such
waves, such as wired or wireless links, optical links or the like,
also may be considered as media bearing the software. As used
herein, unless restricted to non-transitory, tangible "storage"
media, terms such as computer or machine "readable medium" refer to
any medium that participates in providing instructions to a
processor for execution.
[0129] Hence, a machine readable medium, such as
computer-executable code, may take many forms, including but not
limited to, a tangible storage medium, a carrier wave medium or
physical transmission medium. Non-volatile storage media include,
for example, optical or magnetic disks, such as any of the storage
devices in any computer(s) or the like, such as may be used to
implement the databases, etc. shown in the drawings. Volatile
storage media include dynamic memory, such as main memory of such a
computer platform. Tangible transmission media include coaxial
cables; copper wire and fiber optics, including the wires that
comprise a bus within a computer system. Carrier-wave transmission
media may take the form of electric or electromagnetic signals, or
acoustic or light waves such as those generated during radio
frequency (RF) and infrared (IR) data communications. Common forms
of computer-readable media therefore include for example: a floppy
disk, a flexible disk, hard disk, magnetic tape, any other magnetic
medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch
cards paper tape, any other physical storage medium with patterns
of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other
memory chip or cartridge, a carrier wave transporting data or
instructions, cables or links transporting such a carrier wave, or
any other medium from which a computer may read programming code
and/or data. Many of these forms of computer readable media may be
involved in carrying one or more sequences of one or more
instructions to a processor for execution.
[0130] The computer system 601 can include or be in communication
with an electronic display 635 that comprises a user interface (UI)
640 for providing, for example, a login screen for an administrator
to access software programmed to control the seizure detection
system and functionality and/or for providing the operation status
health of the seizure detection system. Examples of UI's include,
without limitation, a graphical user interface (GUI) and web-based
user interface.
[0131] Methods and systems of the present disclosure can be
implemented by way of one or more algorithms. An algorithm can be
implemented by way of software upon execution by the central
processing unit 605. The algorithm can, for example, be component
of software described elsewhere herein and may modulate the seizure
detection system parameters (e.g. processing EEG signals, machine
learning algorithms, control policy, seizure burden, notifications,
etc)
[0132] While preferred embodiments of the present invention have
been shown and described herein, it will be obvious to those
skilled in the art that such embodiments are provided by way of
example only. It is not intended that the invention be limited by
the specific examples provided within the specification. While the
invention has been described with reference to the aforementioned
specification, the descriptions and illustrations of the
embodiments herein are not meant to be construed in a limiting
sense. Numerous variations, changes, and substitutions will now
occur to those skilled in the art without departing from the
invention. Furthermore, it shall be understood that all aspects of
the invention are not limited to the specific depictions,
configurations or relative proportions set forth herein which
depend upon a variety of conditions and variables. It should be
understood that various alternatives to the embodiments of the
invention described herein may be employed in practicing the
invention. It is therefore contemplated that the invention shall
also cover any such alternatives, modifications, variations or
equivalents. It is intended that the following claims define the
scope of the invention and that methods and structures within the
scope of these claims and their equivalents be covered thereby.
* * * * *