U.S. patent application number 17/019811 was filed with the patent office on 2021-10-07 for eeg recording and analysis.
This patent application is currently assigned to Epitel, Inc.. The applicant listed for this patent is Epitel, Inc.. Invention is credited to Michael K. Elwood, Mitchell A. Frankel, Mark J. Lehmkuhle, Robert Lingstuyl, Tyler D. McGrath, Erin M. West, Jean M. Wheeler.
Application Number | 20210307672 17/019811 |
Document ID | / |
Family ID | 1000005119254 |
Filed Date | 2021-10-07 |
United States Patent
Application |
20210307672 |
Kind Code |
A1 |
Elwood; Michael K. ; et
al. |
October 7, 2021 |
EEG RECORDING AND ANALYSIS
Abstract
One embodiment provides a method, including: obtaining EEG data
from one or more single channel EEG sensor worn by a user;
classifying, using a processor, the EEG data as one of nominal and
abnormal; and providing an indication associated with a
classification of the EEG data. Other embodiments are described and
claimed.
Inventors: |
Elwood; Michael K.;
(Farmington, UT) ; Frankel; Mitchell A.; (Salt
Lake City, UT) ; Lehmkuhle; Mark J.; (Salt Lake City,
UT) ; Wheeler; Jean M.; (Salt Lake City, UT) ;
Lingstuyl; Robert; (Salt Lake City, UT) ; West; Erin
M.; (Midvale, UT) ; McGrath; Tyler D.; (Salt
Lake City, UT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Epitel, Inc. |
Salt Lake City |
UT |
US |
|
|
Assignee: |
Epitel, Inc.
Salt Lake City
UT
|
Family ID: |
1000005119254 |
Appl. No.: |
17/019811 |
Filed: |
September 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
63005405 |
Apr 5, 2020 |
|
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/374 20210101;
A61B 5/0006 20130101; A61B 5/6814 20130101; G06N 20/00 20190101;
A61B 5/742 20130101; A61B 5/4094 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/048 20060101 A61B005/048; G06N 20/00 20060101
G06N020/00 |
Claims
1. A method, comprising: obtaining EEG data from one or more single
channel EEG sensor worn by a user; classifying, using a processor,
the EEG data as one of nominal and abnormal; and providing an
indication associated with a classification of the EEG data.
2. The method of claim 1, wherein the indication is one or more of
an alert, data marking an EEG trace, a count, a report, and a
forecast.
3. The method of claim 2, wherein the providing comprises marking a
segment of an EEG trace of the EEG data, wherein the marking
includes providing one or more of a color code and a label for
display on a display device.
4. The method of claim 1, wherein the classifying comprises:
evaluating a plurality of single channel EEG time segments
individually; identifying a set of the plurality of single channel
EEG time segments to indicate a seizure event lasting longer than
an individual EEG time segment; and creating an annotation list
comprising ordered EEG time segments.
5. The method of claim 1, wherein the classifying comprises
analyzing the EEG data in combination with one or more of
historical data, environmental data, and user supplied data.
6. The method of claim 1, wherein the obtaining comprises receiving
the EEG data at a remote device, wherein the classifying is
performed using the remote device and the indication is provided to
a second remote device.
7. The method of claim 1, wherein the obtaining comprises obtaining
EEG data of four single channel EEG sensors; each of the four
single channel EEG sensors being disposed on a patient at one of a
forehead position and a behind the ear position.
8. The method of claim 7, wherein the classifying comprises using a
model trained using data obtained by one or more of a single
channel EEG sensor and wired EEG sensors.
9. The method of claim 7, wherein the classifying comprises using a
model trained using data obtained by a plurality of single channel
EEG sensors worn by a user and data obtained from wired EEG sensors
worn by the user.
10. The method of claim 1, wherein the obtaining comprises
obtaining EEG data from two or more single channel EEG sensors; the
method comprising providing an instruction for placement of the two
or more single channel EEG sensors.
11. A system, comprising: an output device; a processor operatively
coupled to the output device; and a memory storing instructions
executable by the processor to: obtain EEG data from one or more
single channel EEG sensor worn by a user; classify the EEG data as
one of nominal and abnormal; and provide an indication associated
with a classification of the EEG data.
12. The system of claim 11, wherein the indication is one or more
of an alert, data marking an EEG trace, a count, a report, and a
forecast.
13. The system of claim 12, wherein the output device is a display
device, and wherein the instructions are executable by the
processor to mark a segment of an EEG trace of the EEG data,
including providing one or more of a color code and a label for
display on the display device.
14. The system of claim 11, wherein the instructions executable by
the processor to classify comprise: instructions for evaluating a
plurality of single channel EEG time segments individually;
instructions for identifying a set of the plurality of single
channel EEG time segments to indicate a seizure event lasting
longer than an individual EEG time segment; and instructions for
creating an annotation list comprising ordered EEG time
segments.
15. The system of claim 11, wherein the instructions are executable
by the processor to analyze the EEG data in combination with one or
more of historical data, environmental data, and user supplied
data.
16. The system of claim 11, wherein the output device acts to
communicate the indication over a network to a remote device.
17. The system of claim 11, wherein the instructions are executable
by the processor to obtain the EEG data from four single channel
EEG sensors; each of the four single channel EEG sensors being
disposed on a patient at one of a forehead position and a behind
the ear position.
18. The system of claim 11, wherein the instructions executable by
the processor to classify use a model trained using data obtained
by one or more of a single channel EEG sensor and wired EEG
sensors.
19. The system of claim 18, wherein the instructions executable by
the processor to classify use a model trained using data obtained
by a plurality of single channel EEG sensors worn by a user and
data obtained from wired EEG sensors worn by the user.
20. A method, comprising: obtaining EEG data from two or more
single channel EEG sensors worn by a user; transmitting the EEG
data to a remote device; classifying, using a processor of the
remote device, the EEG data as one of nominal and abnormal; and
providing, from the remote device to a display associated with a
remote user, data comprising a montage of the EEG data.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. provisional patent
application Ser. No. 63/005,405, filed on Apr. 5, 2020 and having
the same title, the contents of which are incorporated by reference
in their entirety herein.
BACKGROUND
[0002] There are thousands of hospitals across the U.S. Many of
these hospitals are community or rural hospitals. These community
or rural hospitals conventionally are part of a hospital system or
network. An example of one such network includes several community
hospitals with one major tertiary hospital. A community or rural
hospital outside of any large hospital network would typically
contract with a large tertiary hospital for emergent and
intensive-care solutions outside of the areas of expertise of the
community or rural hospital.
[0003] Electroencephalogram (EEG) monitoring is conventionally only
available in the large tertiary hospitals that support a neurology
department with an EEG service. Many hospitals do not offer EEG
monitoring. These hospitals make arrangements with larger tertiary
hospitals or their partners when such monitoring is required or
desirable for patients. This conventionally takes the form of a
referral of the patient to the tertiary hospital for expert of
specialist services. Often this includes travel or transport of the
patient to the tertiary hospital for services.
BRIEF SUMMARY
[0004] In summary, one embodiment provides a method, comprising:
obtaining EEG data from one or more single-channel EEG sensor worn
by a user; classifying, using a processor, the EEG data as one of
nominal and abnormal; and providing an indication associated with a
classification of the EEG data.
[0005] Another embodiment provides a system, comprising: an output
device; a processor operatively coupled to the output device; and a
memory storing instructions executable by the processor to: obtain
EEG data from one or more single-channel EEG sensor worn by a user;
classify the EEG data as one of nominal and abnormal; and provide
an indication associated with a classification of the EEG data.
[0006] A further embodiment provides a method, comprising:
obtaining EEG data from two or more single channel EEG sensors worn
by a user; transmitting the EEG data to a remote device; and
providing, from the remote device to a display associated with a
remote user, data comprising a montage of the EEG data.
[0007] The foregoing is a summary and thus may contain
simplifications, generalizations, and omissions of detail;
consequently, those skilled in the art will appreciate that the
summary is illustrative only and is not intended to be in any way
limiting.
[0008] For a better understanding of the embodiments, together with
other and further features and advantages thereof, reference is
made to the following description, in conjunction with the
accompanying drawings.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0009] FIG. 1A illustrates an example sensor.
[0010] FIG. 1B illustrates an example sensor.
[0011] FIG. 2 illustrates an example system.
[0012] FIG. 3 illustrates an example of EEG monitoring and
indicating.
[0013] FIG. 4A and FIG. 4B illustrate example EEG data.
[0014] FIG. 5 illustrates an example of EEG data
classification.
[0015] FIG. 6 illustrates an example method of EEG monitoring and
indicating.
[0016] FIG. 7A and FIG. 7B illustrate example application views or
screens.
[0017] FIG. 8 illustrates an example system.
DETAILED DESCRIPTION
[0018] It will be readily understood that the components of the
embodiments, as generally described and illustrated in the figures
herein, may be arranged and designed in a wide variety of different
configurations in addition to the described example embodiments.
Thus, the following more detailed description of the example
embodiments, as represented in the figures, is not intended to
limit the scope of the claims, but is merely representative of
those embodiments.
[0019] To optimize treatment following initial seizure diagnosis,
epileptologists would ideally obtain high-quality, long-term EEG
studies in the hospital with 19+-channel, wired EEG arrangement in
the standard international `10-20` system. Such studies can be
difficult to perform because the process is prohibitively
expensive, time consuming, and extremely inconvenient for patients.
Additionally, the time spent in the epilepsy monitoring unit may
not capture any or all seizure activity that a person normally has
over long periods of time. However, optimal treatment often depends
upon identifying the full extent of a patient's convulsive
(clinical) and non-convulsive (sub-clinical) seizure activity.
Currently a technique for collecting seizure activity history
outside of the hospital is the seizure diary--a home based,
self-reported record that may be incomplete. Seizure diaries can be
difficult to maintain accurately, and the diary can be inaccurate,
making clinical decisions on appropriate pharmacological treatment
difficult. Further, conventional EEG techniques do not adequately
address the need for monitoring in many emergent care scenarios,
particularly those encountered by emergency responders or
clinicians in small or rural hospitals.
[0020] Turning now to the figures, representative example
embodiments are described to provide a better understanding of the
appended claims.
[0021] Referring to FIG. 1A, an embodiment provides a sensor 101a.
The sensor 101a may take a variety of forms; however, the example
of FIG. 1A shows a wearable sensor that adheres to the skin of a
patient. The sensor 101a may include a data analytics platform that
takes EEG monitoring out of the hospital. The sensor 101a features
a small size and can be located in a variety of positions, reducing
the burden on the user during prolonged wear. This permits EEG data
collection using a small, convenient sensor 101a that provides
real-time EEG data capture, as shown in FIG. 1A. This is in
contrast to a complex wired electrode arrangement typically used in
a hospital setting featuring many electrodes and measurement
locations.
[0022] In an example shown in FIG. 1A, a small, connected
health-wearable sensor 101a provides for sensing single-channel EEG
data that can be analyzed for occurrences of both convulsive
(clinical) and non-convulsive (subclinical) seizures. The sensor
101a senses EEG data of the user by detecting electrical activity
of the brain, e.g., by sensing voltage differentials between two
electrode contacts. The sensor may include two electrodes that are
separated by the sensor housing, forming a single bipolar channel
("single channel") of a typical EEG montage. In an embodiment, the
sensor 101a is a single-channel differentially-amplified
transmitter and data logger with 6 mm diameter gold electrodes and
18 mm electrode spacing, similar to a bipolar pair in high-density
EEG. The electrode contacts may be located on the surface of the
device that is placed on the skin of the patient, e.g., adhered to
the patient via a sticker or other adhesive that includes material
such as hydrogel permitting voltage sensing. The sensor 101a is
located at an appropriate place on the user, e.g., on the scalp
below the hairline, in order to sense and record the single channel
EEG data. The EEG data may be analyzed on-board, e.g., via
application of an analysis or machine learning model stored in the
sensor 101a or may be analyzed by a local device or remote device
or a combination of the foregoing. By way of example, the sensor
101a may communicate through a wireless network, e.g. secure
BLUETOOTH Low Energy (BLE), to a local device using a personal area
network (PAN), such as communicating data to a smartphone or a
tablet. Similarly, the sensor 101a may communicate with a remote
device using a wide area network (WAN), such as communicating EEG
data to a remote server or cloud device over the Internet, with or
without communicating via an intermediary device such as a local
device.
[0023] In an embodiment, an EEG monitoring sensor 101a is a
self-contained recording patch including a first electrode and a
second electrode, where the first and second electrodes cooperate
to measure a voltage. The sensor 101a includes circuitry for
generating an EEG signal from the measured voltage, amplifying the
EEG signal, digitizing the EEG signal, and retrievably storing the
EEG data in a memory. The sensor 101a may also include a power
source and an enclosure that houses the circuitry, the power
source, as well as the first and second electrodes, in a unitary
package. The sensor 101a may be worn on the scalp, e.g., forehead
or bi-parietal region, of the user to capture EEG data over a long
period as the user goes about his or her regular, daily
activities.
[0024] As illustrated in FIG. 1B, in an embodiment, the sensor 101b
is designed to be discreet and water-resistant, allowing for
continuous use in all facets of a person's normal daily life. In
the example of FIG. 2, the sensor 101b may be located below the
hairline in a location such as behind the ear. The placement of the
sensor 101b may be aided by a prior diagnosis, e.g., following a
formal evaluation using a full montage of sensors (via monitoring
with a typical wired sensor array in a cap) or via refined location
estimates facilitated by adjusting the location of the sensor 101b
or a set of sensors over time. By choosing an appropriate placement
for the sensor 101b, the usefulness of single channel EEG data is
improved by locating the sensor 101b proximate to brain activity or
focus of seizure activity in the brain.
[0025] By way of non-limiting example, a patient may initially
receive a diagnosis of seizure activity with a location indicated,
e.g., from a clinician. Thereafter, the patient may be asked to
monitor for seizure activity using a sensor 101b. In an embodiment,
a data source indicating a diagnosed location of seizure activity
is accessed, such as an electronic medical record (EMR) or in an
application that receives user input to a location map image or
illustration. The location data may be supplied to the patient,
e.g., via a companion mobile application that facilitates pairing
and data communication between the sensor 101b and a local device.
In another example, the location information is supplied to an
online application that can be viewed by the user (e.g., patient or
clinician). The location data may therefore be provided to the user
in the form of an instruction that indicates acceptable
placement(s) of the sensor(s), e.g., sensor 101b, to maximize the
likelihood that a subsequent seizure will be detected using a
single channel sensor, e.g., sensor 101b.
[0026] Due to the small size of the sensor 101b, for example on the
order of an inch in length and width, and 1/2 inch in a depth
dimension, the sensor 101b may be worn continuously for a period of
days before it needs to be removed, e.g., for charging an on-board
power source such as a rechargeable battery. This permits the
capture, recording and analysis of a large amount of single channel
EEG data using detection processes that identify seizures,
including seizures a user wearing the sensor may not consciously
know they are having, such as seizures that occur while
sleeping.
[0027] The sensors 101a, 101b illustrated in FIG. 1A and FIG. 1B
are suitable for use in any patient, adult, adolescent, children or
newborns. A single sensor 101a, 101b may be used to facilitate more
accurate recording of EEG data for real-time or later analysis. The
sensor is also waterproof or water resistant, making it suitable
for wear during activities where the sensor 101a, 101b may get wet.
This further facilitates long-term wear and comprehensive EEG data
collection for seizure diaries, seizure forecasting and seizure
alerting.
[0028] A consideration in making the sensor 101a, 101b a viable,
long-term wearable sensor is power consumption. An example target
is approximately three days of operation without recharging the
sensor 101a, 101b. To enable continued monitoring, the users may
have two (or multiple) sensors 101a, 101b and will use one while
the other is being recharged. Such an arrangement will allow for
continuous EEG data capturing and monitoring.
[0029] To facilitate long term wear, several techniques may be
employed. For example, a power consuming operation of the sensor
101a, 101b is transmitting the EEG data from the sensor 101a, 101b
to another device. To reduce the power used for data communication,
the sensor 101a, 101b may transmit the captured EEG data at
intervals. For example, the sensor 101a, 101b can capture EEG data
for a predetermined amount of time (e.g., 6 seconds) and then
transmit that captured EEG data, e.g., transmit a page or pages of
EEG data. By transmitting the data at intervals, the sensor 101a,
101b only needs to activate the transmission capability for a short
time (e.g., 1 second). As another example, a power-efficient
microprocessor may be selected for use in the sensor. For example,
certain microprocessors may include a sleep processor core or
capability while transmitting data by DMA to low power SRAM for
data communication. This feature may significantly reduce power
consumption. Further, sensors 101a, 101b may have certain
components omitted, e.g., wireless radio, and may include other
components, e.g., USB or other data communication element, such as
near-field or RFID, in a variety of combinations, to facilitate
power conservation and adequate data transfer for the given use
case. In a case where a physical communication port is included, it
may be covered to prevent water or contaminating element entry,
such as locating it beneath a removable hydrogel or sticker that
adheres the sensor 101a, 101b to the user's skin. The thickness of
the sticker may be modified, e.g., the thickness of the sticker or
materials thereof (e.g., hydrogel areas) may be increased to
accommodate the use context, such as placement on skin that curves,
whereas a thinner sticker may be used on a relatively flat
surface.
[0030] The sensors 101a, 101b are suitable for use by adults,
adolescents, children and neonates. In the example of FIG. 2, a
system for monitoring in a clinical or emergency care setting is
illustrated.
[0031] The example of FIG. 2 uses an example patient; however, this
non-limiting example may be extended to other clinical or
non-clinical scenarios. Seizures are common in emergency care
scenarios or asphyxiated neonates (particularly within the first
two days of life). Full-montage clinical EEG systems use many
(eleven or more) tethered (wired) electrode leads for monitoring.
These leads must be positioned by an EEG technician and can take up
to 60 min to place. A reduced set (3-lead) amplitude integrated
electroencephalography (aEEG) recording system provides real-time
EEG from two channels along with a history of EEG activity
displayed as a filtered, rectified, and averaged signal. However,
electrodes are conventionally placed by a specialist. The aEEG
leads are placed in the bi-parietal region of the standard 10-20
EEG system. An aEEG can be used to diagnose seizures as well as
other background EEG abnormalities associated with encephalopathy.
A persistently abnormal aEEG for as little as 48 hours is
associated with an adverse neurodevelopmental outcome.
[0032] In FIG. 2, an embodiment is shown in which multiple
single-channel sensors (collectively indicated at 201) are placed
on a patient's scalp, spaced from one another to be approximately
over the eyes in the bi-parietal region and behind each ear for
creating an EEG montage. Alternatives are possible, for example,
two sensors may be placed over roughly the bi-parietal region in
the 10-20 EEG system to create three channels: (1) C3-P3, (2)
C4-P4, and (3) a hybrid of C3P3-C4P4. The output of the sensors 201
is synchronized and organized by an application (as further
described herein) and may be viewed both in real-time and
converted, e.g., to aEEG, by software.
[0033] As with a post-diagnosis instruction, an instruction for
positioning the sensors 201 may be provided using a device 202 such
as a desktop computer, tablet, other hospital monitor or mobile
device, which runs an application 203 and displays an instruction
as a graphic indicating placement information for the sensors 201
on the patient's scalp, e.g., on the forehead, behind the ears, a
combination thereof, or other or additional locations. The
placement information in this case may be generalized, e.g.,
approximate location for seizure monitoring in patients thought to
have suffered a given type of brain injury or traumatic event, or
may be customized in some fashion if access to additional data is
available, e.g., specific type of incident suspected for the
patient or in another clinical scenario, such as for an adult or
pediatric emergent care patient. By way of specific example, an
embodiment may provide a graphical instruction such as that
illustrated in FIG. 2, where an emergent care screening is to be
conducted on a patient using four sensors, two on the forehead and
two behind the ears. With this four-sensor arrangement, an
embodiment may create a desired montage (as described herein),
e.g., via subtracting the EEG signal from one sensor relative to
another to create a 10-channel "longitudinal-transverse" montage,
as further described in connection with FIG. 4B.
[0034] In the example illustrated in FIG. 2, an application 203
runs on the device 202, e.g., medical grade tablet. The application
may be programmed to facilitate collecting EEG data for emergent
care, although a version of the application may be used for home
use in a seizure diary context. In one example, the application 203
provides instructions for EEG data collection by non-experts, such
as clinicians in a local or rural hospital unaccustomed to EEG
monitoring. By way of example, the application 203 may provide a
graphic such as illustrated in FIG. 2 that indicates placement and
orientation of four single-channel sensors 201. In some examples,
the single-channel sensors 201 may be directional and keyed, such
as via inclusion of a marking. In the example of FIG. 2, the
graphic illustrates gold dots 207 that correspond to similar
markings on the single-channel sensors 201, which allow correct
orientation of the electrodes on the underside of the device (not
shown) for placement on the patient's scalp. That is, a keyed
device permits the user to appropriately align the single-channel
sensors 201 on the patient.
[0035] The instructions and wireless single-channel sensors permit
rapid collection and analysis (even remote analysis) in the field
or in a clinical setting by non-experts. This avoids or reduces the
need to transport the patient to another location, such as a larger
hospital with conventional EEG monitoring equipment and
specialists. For example, upon seizure suspicion, emergency
department staff in a community hospital can place four sensors
201, as instructed by the graphic illustrated in FIG. 2, on the
scalp and below hairline. The sensors begin transmitting EEG data
to a tablet or other device, which may be the same device that
displays the graphic, i.e., 202 in the example of FIG. 2. The
tablet or other device 202 then relays the EEG data and patient
information to a secure cloud server 204, e.g., running an EEG
reviewing platform software. The emergency department staff then
orders a neurology consult with the tertiary hospital EEG service
either within or outside of their hospital network. The
epileptologist on call at the tertiary hospital logs on to a mobile
application 206 running on a device 207 to review the EEG in real
time or substantially in real time, while also being able to use
the quantitative EEG analysis features in the EEG reviewing
platform. Other or additional data may be similarly provided, e.g.,
by other sensors or devices, such as images or video of the patient
captured with a camera, heart rate, pulse oximetry, or temperature
data captured by suitable devices, etc. An embodiment may provide
an alert or forecast based on the EEG data, e.g., preliminary
diagnoses, suggested options such as transport patient,
continuation or discontinuation of pharmaceutical treatment or
intervention, etc., or simply indicate areas in the EEG trace data
where a seizure or other EEG abnormality is suspected.
[0036] In one example, the application 203 connects to the sensors
201 through BLE, receives the EEG data, buffers the EEG data, and
transmits the EEG data over WIFI to the cloud 204, where it may be
retrieved and viewed by another clinician in a remote location. For
example, the EEG data may be retrieved from the cloud 205 and
reviewed by a remote specialist on a medical grade tablet or other
device 205. This facilitates review of the EEG data as displayed in
an application 206 running on the device 205. EEG data from both
sensors 204 may be synchronized, e.g., by the BLE commands from the
tablet 202.
[0037] The cloud device 204 may provide a server instance running
EEG-review software that allows a specialist such as a
neonatologist or pediatric epileptologist to log on to a tablet 205
running a mobile application 206 to view the EEG in real-time and
as aEEG. The aEEG data may include indications, such as a marking
described in connection with FIG. 4A.
[0038] The general principals of the example in FIG. 2 may be
extended to other scenarios. For example, in an embodiment for
intensive care in pediatric and adults, two sensors, four sensors,
eight sensors, or various combinations of sensors may be used. Data
flow and operations are similar to the example of FIG. 2, and an
adult epileptologist or pediatric epileptologist can log on to a
device 205 to monitor EEG data in real-time, enabling them to make
more accurate decisions faster, advising community hospital staff
on appropriate care. Similarly, for a triage use case, four sensors
in roughly F7, F8, TP9, and TP10, as in the example of FIG. 2,
approximation of the international 10-10 system gives a total of 8
electrodes producing 10 channels of EEG, as further described in
connection with FIG. 4B. In an embodiment, the EEG data obtained by
various single-channel sensors may be synchronized and combined to
create desired differential montages. This may be accomplished at
least in part an application or software program, e.g., that
determines a differential montage available (e.g., 10-10) given the
number and placement of the sensors, displays this to a user, and
permits a user to select a desired configuration or automatically
configures the montage for the user.
[0039] In an embodiment, an application, e.g., running on device
202, visually guides a user, e.g., emergent care staff, with
step-by-step visuals. This may include but is not necessarily
limited to guiding the user through scanning a barcode on a patient
bracelet and each sensor, placing sensor(s) on the scalp, and
ensuring quality signals are being recorded and relayed, e.g., to
the cloud. In some embodiments, additional or reduced data may be
provided. For example, EEG data may not be shown at the
point-of-care, e.g., at device 202. This may be done to accommodate
emergency room staff and doctors, which may consider such data
display to be a distraction. In other embodiments, such data may be
displayed, e.g., in connection with an alert to a specific action
such as a seizure type detection, a suggested treatment or
transport option, etc. Therefore, in an embodiment, the application
running on device 202 may be designed to only interact with the
user when there is a problem in the form of a user alert, such as
poor signal quality coming from an individual sensor or the like.
These user alerts are designed to indicate, e.g., flash an LED red
or blue on each sensor, to staff that interaction with the
application is required to guide them through solving a problem,
e.g., obtain guidance to relocate or reorient a sensor as described
in connection with FIG. 6.
[0040] Collection of EEG data with one or more single-channel
sensors allows EEG data to be reviewed, along with event markers
(as further described herein) to quickly determine areas within the
EEG data that are indicative of seizure. As further described
herein, the type and nature of detection, analysis or
classification the EEG data is subjected to may change depending on
the use case or desired outcome. For example, for triage event
marking, a model with higher false positive rate may be employed as
compared to a use case in which real time seizure prediction is
desired. Likewise, for in-home seizure diary use, a simple
thresholding process may be suitable for producing seizure counts
and markers on EEG data traces.
[0041] Referring to FIG. 3, an example of EEG monitoring and
indicating is illustrated. Often it is difficult to diagnose a
seizure disorder through short-term or even long-term monitoring in
an epilepsy monitoring unit with video-EEG. Furthermore, it is
estimated that 20-30% of people seen in epilepsy centers are
diagnosed with psychogenic non-epileptic seizures (PNES).
Therefore, a mechanism to facilitate long term monitoring, e.g.,
home seizure monitoring, is desirable.
[0042] In an embodiment, EEG data is obtained from a single channel
EEG sensor at 301. As described herein, this may be a single sensor
that is placed post-diagnosis, a single sensor that is placed
pre-diagnosis, or multiple sensors used in a variety of contexts.
Single sensor usage may be more appropriate for in home usage,
whereas multiple sensors may be more appropriate for clinical or
supervised scenarios. Each such sensor provides single channel EEG
data.
[0043] The single channel EEG data is classified at 302. The
classification performed at 302 may be implemented using a variety
of devices. For example, the classification at 302 may be performed
by the sensor, at a local device communicating with the sensor via
a PAN, at a remote or cloud device communicating with the sensor
via a WAN, or a suitable combination of the foregoing.
[0044] The classification performed at 302 may take a variety of
forms. For example, a detection model may take the form of simple
thresholding to detect brain activity over a certain amount or
duration for general seizure detection. One or more models may be
designed to detect specific types of brain activity known to be
useful in specific clinical settings, e.g., emergent care settings
as described in connection with FIG. 2. For example, many signal
processing techniques have been studied over the past 40 years to
analyze and extract information from captured EEG data. The signal
processing techniques can be used to discriminate between ictal
(seizure) and inter-ictal (non-seizure) states and there are a
breadth of scientific papers and studies describing various signal
processing techniques that can be applied to EEG data. The most
common pieces of information extracted from the EEG data are
spike-wave occurrence, time-domain characteristics (e.g., range,
variance, skew), frequency-domain characteristics,
time-frequency-domain characteristics (e.g., wavelet
decompositions), complexity measures (e.g., entropy, fractal
dimension), correlative measures (e.g., cross-channel
correlations), and state dynamics.
[0045] An embodiment uses machine learning techniques for use with
single channel EEG data at 302. In an embodiment, a machine
learning model may be trained using EEG data from one or more
sensors, e.g., sensor 101a, in combination with other EEG data,
such as collected via a wired or tethered EEG system. By way of
example, two-second segments of inter-ictal (pre-seizure) and ictal
(seizure) EEG data may be extracted from the sensor recordings and
used to identify seizure occurrences, as further described in
connection with FIG. 5.
[0046] In an embodiment, in identifying ictal states by classifying
the EEG data at 302, correlation(s) with additional data, such as
historical data (e.g., a pattern or trend, medical record
information obtained from an EMR, etc.), environmental data (e.g.,
weather data), or a user's activity (e.g., behavioral) may be used
to assist in determining the onset of seizure activity or
indicating its past occurrence, as indicated at 304. For example,
psychological, behavioral, and environmental cues can be identified
as potential information correlative to the user's ictal (seizure)
state. This additional data may optionally be used to make a
classification of the EEG data or improve the confidence of an
independently made classification.
[0047] By way of example, user feedback may be used to improve a
seizure detecting process. Initially, an automated detection of
general seizure activity may be performed at 302. If an embodiment
detects general seizure activity, user supplied input may be used
to improve the accuracy of the detection (e.g., with respect to
time, severity or the like). For example, an input interface, such
as a small button included on the sensor 101a or an input element
included in a mobile application, may be used to provide an
indication when a user feels that seizure activity is occurring, is
about to occur or just has occurred. Likewise, an interface may
allow the user to record the severity, duration, or other data
regarding the event. This feedback is subjective, and it may not be
desirable to use as a reliable source to determine an occurrence of
seizure activity. However, user supplied data may provide insight
into the user's experience of non-epileptic seizures. Therefore, it
may be used to confirm a detected seizure or lack of detection. For
example, repeated indications by a user that a seizure is occurring
where EEG data is recorded with high quality and an automated
analysis indicates no seizure may indicate that a user is
experiencing something else, e.g., a non-epileptic seizure,
psychological event. Conversely, such data feedback may indicate
that model or threshold tuning is needed or desirable.
[0048] After the EEG data has been classified at 302, e.g., as
nominal (e.g., non-seizure, pre-seizure) or abnormal (e.g.,
pre-seizure or seizure), an embodiment may provide outputs in the
form of indications. In an embodiment EEG data, such as pre-seizure
EEG data, may be classified as nominal or abnormal depending on the
feature(s) being used for classification, the context (e.g., a mode
may be selected where pre-seizure activity is ignored and
classified as nominal in favor of a lower false positive rate,
etc.), a varied threshold, etc. In an embodiment, if a seizure is
detected as a result of the classification at 302, this
classification may be used at 303 to provide an indication at 306
such as generating an alert (e.g., to the patient or a clinician),
marking the EEG segment that triggered the detection or that is
correlated with additional data, incrementing a seizure count, or
forming a report.
[0049] By way of specific example, and referring to FIG. 4A, an
embodiment may provide an indication at 306 that takes the form of
a marking of a segment of the EEG data as displayed in a trace
401a. The indication 402a may highlight the region of the EEG trace
that triggered the classification of a seizure event. This may
facilitate review by an epileptologist or another clinician. For
example, color coding on the trace or text or other graphical
indicator may be automatically supplied to facilitate
identification of important or interesting portions of the EEG
trace 401a. Additionally, or alternatively, an automated program
may provide a link or position marker to navigate to this portion
of the EEG trace 401a, e.g., automatically or in response to manual
input. This will facilitate quick review of large amounts of EEG
trace data 401a, e.g., where the patient is continuously wearing
the sensor and providing a large amount of EEG data, such as over
several days, to a remote clinician that wishes to quickly review
important events at periodic intervals.
[0050] The EEG data of two or more sensors may be displayed in
various manners. As illustrated in the example of FIG. 4B,
individual sensor EEG trace data may be displayed as well as
differential EEG data obtained via comparison to another sensor.
For example, in FIG. 4B, four sensors, such as four single channel
EEG sensors similar to sensor 201 have been used to record EEG data
at locations approximate to F7, TP9, F8 and TP10. These locations
may correspond to the forehead (front left and right) and behind
the ear (left and right) locations, respectively, utilized in an
emergent care setting, as described herein. In the example of FIG.
4B, the four channels of EEG trace data from the associated sensors
are listed from top to bottom. Differential traces, e.g., F8-TP10,
F7-F8, TP9-TP10, F7-TP10, and F8-TP9 are listed thereafter. In an
embodiment, the ordering of these EEG traces may be modified, e.g.,
based on user preference (identified through user input such as
drag and drop of the traces) or via creating more or less
differential traces or individual sensor traces. By way of specific
example, in a scenario where two sensors are used, the two sensor
traces, e.g., F7 and F8, may be displayed, and one or more
differential traces created from these sensor's readings may be
displayed as well. In an embodiment, the creation or display of the
traces may be automated, e.g., via an application such as described
in connection with FIG. 6 and FIG. 4B determining sensor
location(s) and automatically associating sensor pairs to create
differential traces of interest.
[0051] Referring back to FIG. 3, where the classification at 302
results in an abnormal classification, e.g., pre-seizure, an
embodiment may provide an indication at 305 in the form of a
forecast or prediction. In one example, a pre-seizure
classification may occur where the EEG data is abnormal, such as
EEG data in which frequency and/or amplitude changes exceed
threshold(s) obtained from a nominal EEG data trace, but not
sufficiently so to be confidently classified as a seizure event.
Similarly, a pre-seizure event may occur where the EEG data matches
a known pattern that leads up to a seizure, such as a pattern
indicating a characteristic frequency of change in the EEG data or
a characteristic amplitude change in the EEG data, or a combination
thereof, that comes before a seizure. As described in example of
FIG. 5, features of EEG data that may be useful in identifying such
EEG trace data may be obtained from human labeled training data. As
with other classifications, this determination may be aided by
reference to additional data 304, such as psychological, behavioral
or environmental data.
[0052] The indication provided at 305 may include a forecast
provided to the user, e.g., the wearer of the sensor. The
indication provided at 305 may take the form of a real time
forecast (e.g., produced within a second or two) that changes as
the EEG data or other data 304 changes. The indication provided at
305 may take other forms, e.g., an hourly, daily, weekly or other
time period forecast. Time period forecasts may be influenced by
historical data accessed at 304, e.g., an increasing or decreasing
trend in seizure frequency may assist in forming or modifying the
forecast. The forecast may take a variety of forms, for example a
score or a color displayed in a mobile application that relates to
a likelihood of seizure during a time period, e.g., imminent,
likelihood on a day, during the coming week, etc. Similarly, the
forecast may take the form of a haptic, audio or visual effect
produced by the sensor or a connected local device, remote device,
etc. The forecast may also be provided to other or additional
users, such as a clinician or another user. In the case of a
forecast provided to a clinician, the forecast may include an
indication of a related diagnosis, such as hypoxic ischemic
encephalopathy, and a related action, such as a suggested
treatment, e.g., therapeutic hypothermia, or an automated or
semi-automated action, such as requesting a consultation with an
on-call specialist.
[0053] Real-time or imminent seizure forecasting or prediction adds
additional complexity in that discrimination must be made in a time
sensitive manner, between an inter-ictal (no-seizure coming), a
pre-ictal (seizure event will happen in the next X min/hours), and
an ictal (seizure is occurring) states. Similar to seizure
detection, information features and machine learning techniques
have been widely tested and detailed in scientific literature with
respect to seizure forecasting. Seizure forecasting success is
often measured by sensitivity (was a warning correct that a seizure
was coming and were any missed) and by either a false alarm rate
per hour or by a "time-in-warning" (how often do warnings occur).
Not everyone with epilepsy can identify a psychological,
behavioral, or environmental seizure precipitating factor. Yet,
over half of all people with epilepsy report at least one seizure
precipitating factor. The number one seizure precipitating factor
is emotional stress. This is followed by behavioral factors well
known to trigger seizures, such as sleep deprivation and tiredness.
Other behavioral factors include alcohol consumption, anti-seizure
medication non-adherence, and physical exercise. Environmental
factors such as time-of-day, flickering light, and weather (e.g.,
ambient temperature, relative humidity) have been shown to increase
susceptibility to seizures.
[0054] As with seizure detection, seizure prediction may take the
form of a classification performed at 302. Likewise, additional
data such as historical, environmental or user provided data may be
used to generate or modify the classification of the EEG data as
nominal or abnormal. This data may be used to form forecasts or
predictions provided at 305.
[0055] The additional data used to classify at 302 may include but
is not limited to historical data (e.g., seizure trend data, etc.),
environmental data (e.g., weather, stimulus data such as exposure
to flickering light, etc.), and user data (e.g., behavioral data).
The user data may be provided by the user directly or indirectly.
For example, the user data may be input by the user directly, such
as entering in self-evaluation data to a mobile application.
Non-limiting examples include stress level, sleep quality rating,
sleep score according to a known scale, sleep time, etc. User or
other data may be obtained indirectly, e.g., from a linked health
app, from a medical record, from input of another user on another
device, such as a physician, or inferred from another device such
as a mobile phone or smart watch providing accelerometer data,
etc.
[0056] In an embodiment, an indication may take the form of a
report, as indicated by way of example at 306. For example, a
digital seizure diary report may be provided to the patient or
clinician. Epileptologists will have a precise, quantitative record
of a patient's seizure activity and that will let them know if a
treatment is working, enabling them to adapt the patient's
treatment more rapidly and successfully.
[0057] The improvements to a seizure detection or prediction are
heavily reliant on the volume and quality of EEG data collected.
Currently there exists no practical EEG database. While there are
some laboratory EEG databases (e.g., MIT database), the EEG data in
these databases are too clean to use for prediction as they are not
representative of the quality of EEG data that will be collected in
the real world. Ease of the EEG data collection will improve EEG
data availability, such as via the of use of the EEG data
collection sensor 101a,101b and its minimal impact on the user's
everyday routine. Hence, a feature of an embodiment is use of a
single channel EEG data collection sensor 101a, 101b, accompanying
EEG data analysis, and seizure prediction techniques(s). While a
single channel EEG data collection may not be able to accurately
identify where in the user's brain seizure activity is occurring
all the time or initially, the simple detection of the occurrence
of seizure activity presents a valuable tool to help users manage
and treat epilepsy. As described, the location or placement of the
sensor may be refined over time, e.g., in connection with a
preliminary or subsequent full EEG montage, in connection with
analysis of the sensor's data quality, etc. Additionally,
predictive capabilities may provide users with improved quality of
life as they can conduct activities with reduced anxiety that an
unexpected seizure may occur.
[0058] One aspect of identifying and/or predicting seizures via
classification at 302 may include discriminating between the
various seizure types. For example, the absence seizure typically
occurs many times a day and the electrographic signature of such a
seizure is the same across all ages. Therefore, it may be
comparatively easier to collect extensive EEG data and improve a
seizure prediction model by machine learning to detect absence
seizures. The other type of seizures may occur less frequently,
such as once a month, so they may be difficult to predict
accurately due to the lack of EEG data on which machine learning
models can be trained. However, starting from the creation of a
generalized seizure prediction model for a common seizure type, an
embodiment may be expanded and refined by use of a model that
covers other types of seizures, particularly as long-term wear of
the sensors 101a, 101b by the users continues. This EEG data may be
stored and used with permission of the users to build a database
suitable for forming future seizure detection and prediction
models.
[0059] Turning now to FIG. 5, an example of EEG data classification
is illustrated. The example classification technique shown in FIG.
5 may be used to provide a classification as part of a larger
processing technique, for example that shown in FIG. 3.
[0060] In an embodiment that utilizes a machine learning process to
classify the EEG data, a training phase may include processes
outlined in 501-505 of FIG. 5. By way of example, as shown at 501 a
patient wears a plurality of sensors, e.g., four single channel EEG
sensors, such as sensor 201 of FIG. 2. In one example, four sensors
201 may be arranged on forehead and behind the ear positions, e.g.,
one sensor on the left forehead, one sensor on the right forehead
(F7/F8), one sensor on the left behind the ear and one sensor on
the right behind the ear (TP9/TP10). The sensors may be worn for a
period of time to collect training EEG data, e.g., a patient may
wear the sensors 201 for seven days during an Epilepsy Monitoring
Unit (EMU) stay.
[0061] In one example, the training data may include both single
channel EEG data collected using sensors 201 as well as EEG data
collected using a normal 10-20 or 10-10 multi-channel wired EEG
(wired EEG) sensor or headset as part of standard of care. That is,
both the sensors 201 and the wired EEG sensor or headset may be
worn by the same patient at the same time to obtain a set of EEG
training data.
[0062] During an EMU stay, one or more of reviewing software and an
epileptologist identifies potential seizure events in the wired EEG
data record. Patients and/or family in the room may also indicate,
e.g., push a button, that a seizure is occurring. An epileptologist
reviews the entire multi-day wired EEG, along with the reviewing
software and user provided event markers, to determine when a
seizure occurred (as is conventional). An epileptologist may also
review the EEG data to identify what type of seizure event occurred
(as is conventional). If a seizure was focal in origin, an
epileptologist may indicate which wired EEG electrodes was center
of focus. An epileptologist may also indicate EEG start/stop of
seizure, whether the electrographic seizure is visible on the wired
EEG at locations where each sensor was placed, and electrographic
obscuring artifact(s) (such as patent movement) start/stop. From
this information, when a seizure should be electrographically
visible (per conventional techniques) is known and this may be
utilized to compare with the data obtained via the four sensors
201.
[0063] Indicated at 502, a pre-processing of raw EEG data is
performed. By way of example, noise removal or filtering may be
applied, such as removal of 50/60-Hz line noise, low-pass filtering
to remove electromyographic (EMG) muscle activity, normalization of
standardization to account for inter-patient and inter-sensor
differences in the data amplitude. Other or additional signal
processing conducted at 501 may include electro-ocular artifact
rejection (to remove the impact of eye movement), e.g., from
certain sensor placements such as F7/F8 placed sensors.
[0064] Pre-processed data is segmented into short-duration segments
(for example between 0.5-10 seconds) at 503. In an embodiment, each
segment is labeled, e.g., as seizure or non-seizure, based on its
origin from a time previously noted as during a seizure in the
patient wearing phase of 501 and if the seizure was visible at the
sensor 201 location(s).
[0065] Feature Extraction is performed at 504. In an embodiment,
one or more of the following features are extracted from the
segmented data: time domain (min, max, mean, median, range,
variance, standard deviation, skew, kurtosis); frequency domain
(Fast-Fourier Transforms, EEG specific bands (s, delta, theta,
alpha, beta, gamma)); time-frequency domain (wavelets); complexity
domain (sample/spectral entropy, non-linear energy operator, Hjorth
parameters, fractal dimensions); transforms: (principal component
analysis, linear discriminant analysis); and historical (past
segment values, which may be weighted).
[0066] Model tuning is performed at 505. Because the EEG sensor
data is highly-imbalanced (e.g., 100:1 ratio of non-seizure
segments to seizure), a subset of the EEG data may be used (e.g.,
3:1 for the EEG data/model). A machine learning model, for example
a random forest as shown in FIG. 5 or a support vector machine, an
artificial neural network (shallow or deep), etc., is trained and
tuned on the training data at 505. For example, tuning may include
hyper-parameter tuning, feature relevance determination,
cross-validation (e.g., using leave-one-out (LOO) methods),
etc.
[0067] Metrics such as receiver operating curve (ROC) area under
the curve (AUC), specificity, sensitivity, positive predictive
value, false positive rate, or any combination of foregoing, may be
used to determine the best model. In certain cases, e.g., where
seizure detection is paramount and false positives are tolerable,
e.g., in a seizure diary context, a particular model may be chosen
over use in another scenario, e.g., where false positives are to be
minimized, such as automated medication recommendations or
providing suspected diagnoses. In an embodiment, the model employed
may be exchanged or modified, e.g., by adjusting a parameter such
as a probability threshold, to suit the use context. By way of
example, an embodiment may adjust the model employed by offering
the end user data input interfaces, such as displaying selectable
elements that indicate the use context, which after selection loads
a predetermined model or set of parameter(s) for the context
indicated. For example, contexts such as seizure diary, real time
alerting, emergent care, etc., may be indicated via selections,
which loads a model or adjusts a model's parameter(s), e.g.,
probability threshold(s) that can be modified to adjust
sensitivity, to match the context indicated. More experienced users
may interface with the model parameters more directly.
[0068] Following the tuning at 505, the tuned model(s) may be saved
for use in detection. For example, in steps 506-510, a model such
as a previously tuned model is accessed and run on patient data,
e.g., collected using one or more sensors 201 in a treatment
scenario. As described herein, this may include a process of
segment detection, as outlined in 506-510. By way of example, at
506 a patient, e.g., that has been previously diagnosed with a
seizure disorder, wears one or more sensors 201, e.g., using a
placement guided by the patient's epileptologist as to be the most
likely to pick up seizure events. No wired EEG data would be
recorded and the patient wears the sensor(s) 201, e.g., during
everyday activities. In one example, the patient wears the
sensor(s) 201 up to 24-hours a day and may do so for several
days.
[0069] EEG data collected by the sensors 201 may be streamed into a
remote device such as a cloud-platform, as per the example of FIG.
2. Raw sensor EEG data pre-processing is performed at 507, EEG
segments are identified at 508, and feature(s) extracted at 509,
which may be similar to the processes performed at 502, 503, and
504, respectively.
[0070] At 510, the unlabeled, segmented, feature set is run through
the trained model, the outputs of which may be utilized in a whole
seizure detection process, as for example outlined at 511-514.
[0071] In the example of FIG. 5, the outputs of the model run at
510 may include segment probabilities 511 for seizure events per
segment or set of segments. In one embodiment, the output of the
model run at 510 is a 0-1 likelihood that the segment occurs during
a seizure event. In some embodiments, the specific type of seizure
may or may not be determined, e.g., if a machine learning model
tuned for a specific seizure type is employed, such as via user
selection of such a model. In an embodiment, a general seizure
detection model and identification process, e.g., as outlined in
510-514, is akin to a series of models with probabilities for each
seizure type, combined or considered together to make a
seizure/non-seizure determination.
[0072] At 512 segment stitching is performed. For example, segment
probabilities are combined to create a start/stop time for a
(whole) seizure event, i.e., consisting of multiple segments. This
may be accomplished in many ways. By way of example, the segments
may be stitched or combined together via individual segment
thresholding (such as comparing frequency and amplitude EEG data
changes to threshold(s) for each segment), a multi-segment
thresholding and windowing process (combining or considering
together multiple segments probabilities, e.g., in comparison to
one or more thresholds), or integration windowing (e.g., weighted,
leaky, etc.). The windows are typically as short as a few seconds
(e.g., absence seizures) or up to minutes in duration (e.g., for
focal seizures).
[0073] Annotations are generated, e.g., for a patient medical
record or EEG trace display, for the start/stop time of the
determined seizure events at 513. This annotation list data may be
utilized in a variety of ways, e.g., as an indication per FIG. 3.
For example, an embodiment may present an annotation list (list of
segments of EEG data that are associated with a seizure or metadata
for identifying such EEG data) directly to the patient through an
application run on a local device, or directly to the clinician via
a local or remote device (e.g., if the clinician is remote). The
annotation list data may also be stored with the raw EEG trace data
in a cloud-platform for clinician review.
[0074] Indicated at 514 is an epileptologist review stage. In an
embodiment, clinicians may review the annotations and/or the raw
EEG data and make clinical decisions. The process of epileptologist
or simply user review at 514 may be influenced by the context. For
example, in an embodiment, a low-threshold may be set (as described
herein) that leads to many false positives. In such an embodiment,
a whole seizure/no-seizure determination may or may not even be
made. Rather, the annotations produced at 513 may be provided to a
clinician, e.g., for more rapid review of potentially interesting
EEG data, and seizure/non-seizure decisions may be made by the
clinician based on this review. In other contexts, the review may
be conducted by another, e.g., an at home user making a seizure
diary. In this context, additional or different data may be
displayed, e.g., time or location context data indicating when the
potentially interesting EEG data was recorded to facilitate user
review.
[0075] Referring to FIG. 6, to facilitate use of one or more EEG
sensors such as sensor 201 by non-expert users, an embodiment
provides an application, such as a mobile application for use on a
mobile device, that guides the user in placing the sensors and
recording EEG data. By way of example, as illustrated in FIG. 6, an
embodiment permits the user to easily capture sensor or patient
data using the mobile device. In the example of FIG. 6, at 601 the
application obtains sensor data, for example via capturing an image
of a bar code, QR code, or other coded data, such as for example
provided with each sensor. This permits an embodiment to
automatically identify the sensor. Similarly, patient data may also
be automatically or semi-automatically captured by the application.
Of course, as will other data inputs described herein, the data may
be entered manually. An example screen or application view of a
user capturing sensor data from a bar code is provided in FIG. 7A.
As illustrated in FIG. 7A, the application may capture an image of
the bar code and automatically populate the display screen with the
captured sensor information (e.g., an identification formed from
the bar code or other captured data). The application may further
indicate to the user how many sensors are to be used for the
application or context. In the example of FIG. 7A, four sensor
locations are indicated, two of which have been successfully
identified. This assists the user in determining how many sensors
are to be used in the scenario, e.g., four sensors for an emergent
care scenario.
[0076] In an embodiment, an application may further display
instructional steps to the user. For example, the application may
display instructions for turning on or activating the sensor,
pairing a sensor, which may be an automated or semi-automated
routine accomplished with a user input such as a button press,
confirming that a sensor is connected to the mobile device,
confirming that the mobile device is connected with a remote device
(e.g., cloud platform), preparing a sensor to be adhered to a
patient, determining the appropriate location(s) for sensor
placement on the patient, re-positioning the sensors in the
application, recording data, and storing data (locally or
remotely). Further, the application may include additional or
alternative display capabilities, e.g., the ability to have a live
video call with an expert, clinician, etc., the latter of which may
assist in live or real-time troubleshooting or diagnosis
contexts.
[0077] Once the user has activated and connected the sensor(s) to
the mobile device running the application, this is confirmed by the
application as illustrated at 602. For example, the application may
display the sensor(s) in a location illustration, as indicated in
FIG. 7B. This assists the user in determining if the sensor(s) are
properly located on the patient for the given context and this is
accurately reflected in the application view. In the example of
FIG. 7B, the sensors are to be located in forehead and behind the
ear positions, as illustrated. If a given sensor ID in the
illustration provided by the application does not match the actual
location of the physical sensor on the patient (e.g., visible on
the bar code or otherwise identifiable on the physical sensor), a
user may select (e.g., touch in the case of a mobile touch screen)
the subject sensor icon to reassign its location in the
illustration. By way of example, if sensor 502 of FIG. 7B is
illustrated by the application as a right-front located sensor, but
in reality, it was placed on the left-front by the user, the user
may simply relocate it by interfacing with the application. This
may take a variety of ways. In the example of FIG. 7B, a user may
touch a selectable icon, one of which is indicated at 702 for
sensor 502, to bring up a menu for swapping its position with
another sensor in the array, as indicated at 701. Similarly,
another mechanism such as drag and drop of the icon 702 may be used
to reposition the sensor(s) in the application. This ensures that
the EEG data collected by the actual sensors is known to the
application, e.g., for creating differential EEG data via
subtraction from another sensor location.
[0078] Referring again to FIG. 6, an embodiment may confirm that
the sensor locations are appropriate at 603, following which, if no
decision to relocate the sensor(s) is forthcoming, as indicated at
604, a recording session may begin, as shown at 605. At a
predetermined time or based on another factor such as user
selection or interface, the recording session may be concluded, as
determined at 606. Thereafter, the EEG data of the session may be
stored locally, remotely, or both, as indicated at 607. As
described herein, during the recording at 605, other activities may
be performed by an embodiment. For example, the EEG data of the
sensor(s) may be analyzed locally or remotely, e.g., by a cloud
platform, a remote clinician, etc.
[0079] It will be readily understood that certain embodiments can
be implemented using any of a wide variety of devices or
combinations of devices. Referring to FIG. 8, an example system on
chip (SoC) included in a computer 800 is illustrated, which may be
used in implementing one or more embodiments. The SoC or similar
circuitry outlined in FIG. 8 may be implemented in a variety of
devices in addition to the computer 800, for example similar
circuitry may be included in a sensor 870 or another device or
platform 870a. In addition, circuitry other than a SoC, an example
of which is provided in FIG. 8, may be utilized in one or more
embodiments. The SoC of FIG. 8 includes functional blocks, as
illustrated, integrated onto a single semiconductor chip to meet
specific application requirements.
[0080] The central processing unit (CPU) 810, which may include one
or more graphics processing units (GPUs) and/or micro-processing
units (MPUs), includes an arithmetic logic unit (ALU) that performs
arithmetic and logic operations, instruction decoder that decodes
instructions and provides information to a timing and control unit,
as well as registers for temporary data storage. The CPU 810 may
comprise a single integrated circuit comprising several units, the
design and arrangement of which vary according to the architecture
chosen.
[0081] Computer 800 also includes a memory controller 840, e.g.,
comprising a direct memory access (DMA) controller to transfer data
between memory 850 and hardware peripherals. Memory controller 840
includes a memory management unit (MMU) that functions to handle
cache control, memory protection, and virtual memory. Computer 800
may include controllers for communication using various
communication protocols (e.g., I.sup.2C, USB, etc.).
[0082] Memory 850 may include a variety of memory types, volatile
and nonvolatile, e.g., read only memory (ROM), random access memory
(RAM), electrically erasable programmable read only memory
(EEPROM), Flash memory, and cache memory. Memory 850 may include
embedded programs and downloaded software, e.g., EEG processing
software, etc. By way of example, and not limitation, memory 850
may also include an operating system, application programs, other
program modules, and program data.
[0083] A system bus permits communication between various
components of the computer 800. I/O interfaces 830 and radio
frequency (RF) devices 820, e.g., WIFI and telecommunication
radios, BLE devices, etc., are included to permit computer 800 to
send and receive data to sensor(s) 870 or remote devices 870a using
wired or wireless mechanisms. The computer 800 may operate in a
networked or distributed environment using logical connections to
one or more other remote computers or databases. The logical
connections may include a network, such as a personal area network
(PAN), a local area network (LAN) or a wide area network (WAN) but
may also include other networks/buses. For example, computer 800
may communicate data with and between a sensor 870 and remote
devices 870a over the Internet.
[0084] The computer 800 may therefore execute program instructions
configured to store and analyze EEG data, and perform other
functionality of the embodiments, as described herein. A user can
interface with (for example, enter commands and information) the
computer 800 through input devices, which may be connected to I/O
interfaces 830. A display or other type of device may also be
connected or coupled to the computer 800 via an interface selected
from I/O interfaces 830.
[0085] It should be noted that the various functions described
herein may be implemented using executable instructions stored in a
memory, e.g., memory 850, that are transmitted to and executed by a
processor, e.g., CPU 810. Computer 800 includes one or more storage
devices that persistently store programs and other data. A storage
device, as used herein, is a non-transitory storage medium. Some
additional examples of a non-transitory storage device or medium
include, but are not limited to, storage integral to computer 800,
such as a hard disk or a solid-state drive, and removable storage,
such as an optical disc or a memory stick.
[0086] Program code stored in a memory or storage device may be
transmitted using any appropriate transmission medium, including
but not limited to wireless, wireline, optical fiber, cable, RF, or
any suitable combination of the foregoing.
[0087] Program code for carrying out operations may be written in
any combination of one or more programming languages. The program
code may execute entirely on a single device, partly on a single
device, as a stand-alone software package, partly on single device
and partly on another device, or entirely on another device. In
some cases, the devices may be connected through any type of
connection or network, including a LAN, a WAN, a short-range
wireless mechanism such as a PAN, a near-field communication
mechanism, or the connection may be made through other devices (for
example, through the Internet using an Internet Service Provider),
using wireless connections or through a hard wire connection, such
as over a USB connection.
[0088] Example embodiments are described herein with reference to
the figures, which illustrate example methods, devices and program
products according to various example embodiments. It will be
understood that the actions and functionality may be implemented at
least in part by program instructions. These program instructions
may be provided to a processor of a device to produce a special
purpose machine, such that the instructions, which execute via a
processor of the device implement the functions/acts specified.
[0089] It is worth noting that while specific elements are used in
the figures, and a particular ordering of elements has been
illustrated, these are non-limiting examples. In certain contexts,
two or more elements may be combined, an element may be split into
two or more elements, or certain elements may be re-ordered or
re-organized or omitted as appropriate, as the explicit illustrated
examples are used only for descriptive purposes and are not to be
construed as limiting.
[0090] Although illustrative example embodiments have been
described herein with reference to the accompanying figures, it is
to be understood that this description is not limiting, and that
various other changes and modifications may be affected therein by
one skilled in the art without departing from the scope or spirit
of the disclosure.
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