U.S. patent application number 15/170560 was filed with the patent office on 2017-01-05 for method and apparatus for detecting seizures.
The applicant listed for this patent is Brain Sentinel, Inc.. Invention is credited to Jose E. Cavazos, Michael R. Girouard, Russell M. Herring, James R. Leininger.
Application Number | 20170000404 15/170560 |
Document ID | / |
Family ID | 45938745 |
Filed Date | 2017-01-05 |
United States Patent
Application |
20170000404 |
Kind Code |
A1 |
Leininger; James R. ; et
al. |
January 5, 2017 |
METHOD AND APPARATUS FOR DETECTING SEIZURES
Abstract
A method of detecting seizures may comprise receiving an EMG
signal and processing the received EMG signal to determine whether
a seizure characteristic is present in the EMG signal during a time
window. An apparatus for detecting seizures with motor
manifestations may comprise one or more EMG electrodes capable of
providing an EMG signal substantially representing seizure-related
muscle activity; and a processor configured to receive the EMG
signal, process the EMG signal to determine whether a seizure may
be occurring, and generate an alert if a seizure is determined to
be occurring based on the EMG signal.
Inventors: |
Leininger; James R.; (San
Antonio, TX) ; Herring; Russell M.; (San Antonio,
TX) ; Girouard; Michael R.; (San Antonio, TX)
; Cavazos; Jose E.; (San Antonio, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Brain Sentinel, Inc. |
San Antonio |
TX |
US |
|
|
Family ID: |
45938745 |
Appl. No.: |
15/170560 |
Filed: |
June 1, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14659317 |
Mar 16, 2015 |
9439595 |
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15170560 |
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13275309 |
Oct 17, 2011 |
8983591 |
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14659317 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 2562/0219 20130101;
A61B 5/6831 20130101; A61B 5/6844 20130101; A61B 5/0408 20130101;
A61B 5/0488 20130101; A61B 5/11 20130101; A61B 5/6833 20130101;
A61B 5/4094 20130101; A61B 5/01 20130101; A61B 5/0004 20130101;
A61B 5/04015 20130101; A61B 5/6804 20130101; A61B 5/7282 20130101;
A61B 5/0402 20130101; A61B 5/0492 20130101; A61B 2505/07
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/11 20060101 A61B005/11; A61B 5/0408 20060101
A61B005/0408; A61B 5/01 20060101 A61B005/01; A61B 5/0492 20060101
A61B005/0492; A61B 5/04 20060101 A61B005/04 |
Claims
1-78. (canceled)
79. An apparatus for detecting seizures with motor manifestations,
the apparatus including: one or more EMG electrodes capable of
providing an EMG signal substantially representing seizure-related
muscle activity; a processor configured to receive the EMG signal,
process the EMG signal to determine whether a seizure may be
occurring, and generate an alert if a seizure is determined to be
occurring based on the EMG signal; said processor being configured
to detect bursts of EMG signal, assign certainty values to
individual burst members among said detected bursts, and weight the
number of said detected bursts as a function of the certainty
values assigned to said individual burst members; wherein said
certainty values are determined based on a comparison of how said
individual burst members compare to a reference burst in terms of
one or more burst characteristics selected from the group of
characteristics including burst signal-to-noise ratio, burst width
and burst amplitude; said processor being further configured to
identify the presence of a plurality of bursts over a time window,
determine the periodicity of bursts over said time window, and
determine a periodicity contribution to seizure detection; and said
processor being further configured to use a supervisory algorithm
to determine a seizure detection value using the certainty value
weighted number of detected bursts and the periodicity
contribution, and compare said seizure detection value to a
threshold seizure detection value suitable to indicate if said
seizure is occurring.
80. The apparatus of claim 1, further including one or more of an
ECG electrode, a temperature sensor or an accelerometer.
81. The apparatus of claim 1, wherein the one or more EMG
electrodes are mounted to one or more of an arm band, adhesive
tape, or item of clothing so as to allow positioning of the one or
more EMG electrodes over a muscle.
82. The apparatus of claim 1, wherein the one or more EMG
electrodes are differential bipolar electrodes.
83. The apparatus of claim 1, including two EMG electrodes capable
of being associated with an agonist/antagonist muscle pair, wherein
one EMG electrode is associated with an agonist muscle, and the
other EMG electrode is associated with its antagonist muscle.
84. The apparatus of claim 5, wherein the agonist/antagonist muscle
pair includes the triceps brachii and biceps brachii.
85. The apparatus of claim 1, further including a transceiver for
transmitting the alert.
86. The apparatus of claim 1, further including a base station in
communication with the processor for receiving the alert.
87. The apparatus of claim 8, wherein the base station further
includes an I/O device capable of allowing manual adjustment of
alert settings and visually displaying the EMG signal or data based
thereon.
88. The apparatus of claim 1, wherein said bursts are qualified
against a minimum threshold duration and a maximum threshold
duration.
89. The apparatus of claim 1, wherein the determining of said
periodicity contribution includes calculating an average deviation
for times between bursts included among said plurality of bursts
and identifying if said average deviation is less than or greater
than a threshold average deviation.
90. The apparatus of claim 11, wherein the processor is further
configured to negatively weight said periodicity contribution
against seizure detection if said average deviation is less than a
threshold average deviation.
91. The apparatus of claim 1, wherein the processor is further
configured to eliminate bursts from said plurality of bursts if the
bursts among said plurality are too close together or too far
apart; and wherein the determining of said periodicity contribution
includes comparison of the periodicity of burst characteristics
over said time window to a minimum uniformity threshold and a
maximum uniformity threshold.
92. The apparatus of claim 1, further including one or more
leads-off detectors configured to indicate whether one or more of
the EMG electrodes is sufficiently close to a muscle to provide a
substantially accurate EMG signal representing activity of the
muscle.
93. The apparatus of claim 1, wherein the one or more EMG
electrodes and processor are packaged as a single unit mountable to
a human body.
Description
PRIORITY DATA
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/659,317, which is a continuation of U.S.
patent application Ser. No. 13/275,309 filed Oct. 17, 2011, which
issued as U.S. Pat. No. 8,983,591 on Mar. 17, 2015, and claims
priority to U.S. Provisional Application No. 61/393,747, filed Oct.
15, 2010, the disclosures of which are each wholly incorporated
herein by reference.
BACKGROUND
[0002] A seizure may be characterized as abnormal or excessive
synchronous activity in the brain. At the beginning of a seizure,
neurons in the brain may begin to fire at a particular location. As
the seizure progresses, this firing of neurons may spread across
the brain, and in some cases, many areas of the brain may become
engulfed in this activity. Seizure activity in the brain may cause
the brain to send electrical signals through the peripheral nervous
system to different muscles. For example, an electrical signal may
originate in the central nervous system and initiate the
propagation of an electrical signal through motor neurons. A motor
neuron may, for example, communicate with a muscle through
interaction with the motor end plate of a muscle fiber; thereby
initiating an action potential and depolarization of muscle cells
within a given motor unit. Depolarization typically results from
the coordinated flow of ions, e.g., sodium and potassium cations,
through channels within a muscle cell membrane. That is, changes in
states of ion channels initiate a change in the permeability of a
cell membrane, and subsequent redistribution of charged ions.
Current flow through muscle cells may initiate a corresponding flow
in the tissue above the muscle and thus an electrical signature at
the surface of the skin.
[0003] Techniques designed for studying and monitoring seizures
have typically relied upon electroencephalography (EEG), which
characterizes electrical signals using electrodes attached to the
scalp or head region of a seizure prone individual, or seizure
patient. EEG electrodes may be positioned so as to measure such
activity, that is, electrical activity originating from neuronal
tissue. Compared to EEG, electromyography (EMG) is a little-used
technique in which an electrode may be placed on or near the skin,
over a muscle, to detect an electrical current or change in
electric potential in response to redistribution of ions within
muscle fibers.
[0004] Detecting an epileptic seizure using electroencephalography
(EEG) typically requires attaching many electrodes and associated
wires to the head and using amplifiers to monitor brainwave
activity. The multiple EEG electrodes may be very cumbersome and
generally require some technical expertise to apply and monitor.
Furthermore, confirming a seizure requires observation in an
environment provided with video monitors and video recording
equipment. Unless used in a staffed clinical environment, such
equipment is frequently not intended to determine if a seizure is
in progress but rather provide a historical record of the seizure
after the incident. Such equipment is usually meant for
hospital-like environments where a video camera recording or
caregiver's observation may provide corroboration of the seizure,
and is typically used as part of a more intensive care regimen such
as a hospital stay for patients who experience multiple seizures. A
hospital stay may be required for diagnostic purposes or to
stabilize a patient until suitable medication can be administered.
Upon discharge from the hospital, a patient may be sent home with
little further monitoring. However, at any time after being sent
home the person may experience another seizure, perhaps fatal.
[0005] A patient should in some cases be monitored at home for some
length of time in case another seizure should occur. Seizures with
motor manifestations may have patterns of muscle activity that
include rhythmic contractions of some, most, or all of the muscles
of the body. A seizure could, for example, result in Sudden
Unexplained Death in Epilepsy (SUDEP). The underlying causes of
SUDEP are not well understood; however, some possible mechanisms
causing SUDEP may include tonic activation of the diaphragm muscle
so as to prevent breathing, neurogenic pulmonary edema, asystole,
and other cardiac dysrhythmia. If a sleeping person experiences a
seizure involving those conditions, then caregivers may not be
aware that the seizure is occurring, and thus be unable to render
timely aid.
[0006] While there presently exist ambulatory devices for diagnosis
of seizures, they are EEG-based and are generally not designed or
suitable for long-term home use or daily wearability. Other seizure
alerting systems may operate by detecting motion of the body,
usually the extremities. Such systems may generally operate on the
assumption that while suffering a seizure, a person will move
erratically and violently. For example, accelerometers may be used
to detect violent extremity movements. However, depending upon the
type of seizure, this assumption may or may not be true. Electrical
signals sent from the brain during the seizure are frequently
transmitted to many muscles simultaneously, which may result in
muscles fighting each other and effectively canceling out violent
movement. In other words, the muscles may work to make the person
rigid rather than cause actual violent movement. Thus, the seizure
may not be consistently detected with accelerometer-based
detectors.
[0007] Accordingly, there is a need for an epileptic seizure method
and apparatus that can be used in a non-institutional or
institutional environment without many of the cumbersome electrodes
to the head or extremities. Such an apparatus may be minimally
intrusive, minimally interfere with daily activities and be
comfortably used while sleeping. There is also a need for an
epileptic seizure method and apparatus that accurately detects a
seizure with motor manifestations and may alert one or more local
and/or remote sites of the presence of a seizure. Furthermore,
there is a need for an epileptic detection seizure method and
apparatus that may be used in a home setting and which may provide
robust seizure detection, even in the absence of violent motion,
and which may be personalizable, e.g., capable of being tailored
for an individual or specific population demographic.
SUMMARY
[0008] In some embodiments, a method of detecting seizures may
comprise receiving an EMG signal and processing the received EMG
signal to determine whether a seizure characteristic is present in
the EMG signal during a time window.
[0009] In some embodiments, an apparatus for detecting seizures
with motor manifestations may comprise one or more EMG electrodes
capable of providing an EMG signal substantially representing
seizure-related muscle activity; and a processor configured to
receive the EMG signal, process the EMG signal to determine whether
a seizure may be occurring, and generate an alert if a seizure is
determined to be occurring based on the EMG signal.
[0010] In some embodiments, apparatuses and methods comprise a
detection unit which includes EMG electrodes and a base unit in
communication and physically separated from said detection unit,
wherein the base station is configured for receiving and processing
EMG signals from the detection unit, determining from the processed
EMG signals whether a seizure may have occurred, and sending an
alert to at least one caregiver. In some embodiments, the base
station may separately process the data provided by the detection
unit for verification of the alarm condition. If the base station
agrees with the alarm, then the base station may generate an alarm
to remote devices and local sound generators. Having the base
station agree to the detection unit's alarm may introduce a voting
concept. Both devices must vote on the decision and agree to sound
the alarm. This may be used to limit false alarms.
[0011] In some embodiments, a method and apparatus for detecting a
seizure and providing a remote warning of that incident is
provided. Such a method may detect seizures using EMG electrodes.
One or more EMG electrodes may be attached to an individual's body
and one or more characteristics from the signal output of the one
or more EMG electrodes may be analyzed. EMG output may be compared
to general seizure characteristics and to one or more threshold
values. If one or more values of the output data exceed one or more
thresholds an event may be registered, e.g., logged on a register.
Analysis of events logged in registers for different
characteristics of the output data may be used to assess whether a
seizure incident is declared and whether an alarm is sent to one or
more locations.
[0012] In some embodiments, an apparatus for detecting seizures
with motor manifestations may include a detector unit and a base
unit. The detector unit may include one or more electromyography
(EMG) electrodes, and optionally one or more electrocardiography
(ECG) electrodes. The detector unit and base unit may be in
communication with each other, such as by wireless communication.
The detector unit and base unit may include electronic components
configured to execute instructions for evaluation of EMG signal
data. The base unit may be enabled for sending an alarm to one or
more remote locations. Alternatively, the base unit may be in
communication with a separate transceiver. That transceiver may be
physically distinct but within the general locale of the base unit.
That transceiver may be enabled for sending an alarm to one or more
remote locations.
[0013] In some embodiments, an alarm protocol may be initiated
based on a convolution of data in a plurality of data registers.
Individual registers may, for example, each be responsive to
detection of a different seizure variable. An alarm protocol may be
initiated if a supervisory algorithm, that supervisory algorithm
responsive to the values in the plurality of registers, determines
that an alarm protocol should be initiated.
[0014] In some embodiments, seizure detection methods as described
herein may be adaptive. For example, threshold values may be
adjusted as seizure data is collected from one or more patients. In
addition, algorithms, which may be used to determine whether a
seizure incident is declared, may be modified. Algorithms may, for
example, be modified by adjusting variable coefficients. Those
coefficients may be associated with, and weight, seizure variables.
The adjustment of such coefficients may be based on seizure data
that is collected from one or more patients, including, but not
limited to an individual patient, or other patients, such as those
of a particular demographic. The association between registered
events, the initiation of alarm protocols, and seizure related
incidents, e.g., declared events, actual seizures and inaccurately
reported incidents, may be tracked and used to update variables in
a detection method and thus improve the accuracy of a seizure
detection method or apparatus.
[0015] In some embodiments, a historical record of patient seizure
data and related incidents may be collected. A user may analyze a
historical record and modify or change one or more sub-methods or
alter the distribution of sub-methods that are included in a method
for detecting a seizure. A sub-method may, for example, be a set of
instructions which may be used to increment a counter. Sub-method,
including for example, threshold values, weighting coefficients and
other data, may be provided in a template file, may have a "factory
default" setting, and may change as the method adapts to a
particular patient.
[0016] In some embodiments, the value of a plurality of seizure
variables may be determined for a patient. Individual seizure
variables may be selected and analyzed using algorithms such that
events logged for an individual seizure variable is unlikely to
trigger an alarm; however, the convolution of events logged for the
plurality of seizure variables may raise the confidence with which
a seizure may be detected.
[0017] In some embodiments, a method and apparatus may be used, for
example, to initiate an alarm protocol, create a log of seizure
incidents to help medically or surgically manage the patient,
activate a Vagal Nerve Stimulator, or activate other stimulating
devices that may be used to abort or attenuate a seizure. In some
embodiments, a log of seizure related incidents may prompt a
physician to understand more quickly the failure of a treatment
regimen.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1 illustrates one embodiment of a seizure detection
system.
[0019] FIG. 2 illustrates one embodiment of a detection unit and
base station for a seizure detection system.
[0020] FIG. 3 illustrates one embodiment of a base station.
[0021] FIG. 4 illustrates one embodiment of a method for detecting
seizure related incidents.
[0022] FIG. 5 illustrates exemplary EMG time domain data for a
patient.
[0023] FIG. 6 illustrates exemplary EMG frequency domain data for a
patient.
[0024] FIG. 7 illustrates one embodiment of a burst detection
algorithm.
[0025] FIG. 8A and FIG. 8B illustrate exemplary model forms or
envelopes of signal bursts after filtering, rectification and peak
detection.
[0026] FIGS. 9A, 9B and 9C illustrate another embodiment of a burst
and burst train detection algorithm
[0027] FIG. 10 illustrates one embodiment of a periodicity
algorithm.
[0028] FIG. 11 illustrates one embodiment of a GTC waveform
detection algorithm.
[0029] FIG. 12 illustrates a second embodiment of a GTC waveform
detection algorithm.
[0030] FIG. 13 illustrates one embodiment of a waveform regularity
detection algorithm.
[0031] FIG. 14 illustrates one embodiment of a supervisory
algorithm.
[0032] FIG. 14A illustrates another embodiment of a supervisory
algorithm.
[0033] FIG. 15 illustrates one embodiment of a method of data
collection.
[0034] FIG. 16 illustrates one embodiment of a method of updating a
template file.
[0035] FIG. 17 illustrates one embodiment of a method of adjusting
the state of a detection unit in a method of seizure
monitoring.
[0036] FIG. 18 illustrates one embodiment of an amplitude detection
algorithm.
[0037] FIG. 19 illustrates a further embodiment of a method for
detecting seizure related incidents.
[0038] FIG. 20 illustrates a still further embodiment of a method
for detecting seizure related incidents.
[0039] FIG. 21 illustrates how model data in a procedure for
analysis of data bursts may be organized.
[0040] FIG. 22 illustrates how model data for analysis of data
bursts is combined with data from a GTC accumulation register and
how data in those registers may be analyzed in a supervisory
algorithm.
[0041] FIG. 23 illustrates exemplary EMG electrical data for a
patient.
[0042] FIG. 24 illustrates exemplary EMG electrical data for a
patient while non-seizure moving.
[0043] FIG. 25 illustrates exemplary EMG electrical data for a
patient who is sleeping.
[0044] FIG. 26 illustrates exemplary EMG electrical data for a
patient at the onset of a seizure.
[0045] FIG. 27 illustrates exemplary EMG electrical data for a
patient as the seizure progresses.
[0046] FIG. 28 illustrates exemplary EMG electrical data for a
patient that has been filtered.
[0047] FIG. 29 illustrates further exemplary EMG electrical data
for a patient that has also been filtered.
[0048] FIG. 30 illustrates the same exemplary EMG electrical data
as shown in FIG. 29 and filtered using a different filter
protocol.
[0049] FIG. 31 illustrates exemplary EMG electrical data for a
patient showing short-lived data events.
[0050] FIG. 32 illustrates still further exemplary EMG electrical
data for a patient that has been filtered.
[0051] FIG. 33 illustrates exemplary EMG electrical data for a
patient showing sustained signals.
[0052] FIG. 34 illustrates another exemplary EMG electrical data
for a patient that has been filtered.
[0053] FIG. 35 illustrates another exemplary EMG electrical data
for a patient.
[0054] FIG. 36 illustrates yet another exemplary EMG electrical
data.
DETAILED DESCRIPTION
[0055] The apparatuses and methods described herein may be used to
detect seizures and timely alert caregivers of a seizure using EMG,
among other things. The apparatuses and method may be used, for
example, to initiate an alarm protocol, create a log of seizure
incidents to help medically or surgically manage the patient,
activate a Vagal Nerve Stimulator, or activate other stimulating
devices that may be used to abort or attenuate a seizure. In some
embodiments, a log of seizure related incidents may prompt a
physician to understand more quickly the failure of a treatment
regimen. The apparatuses and methods may comprise a process and
device and/or system of devices for detecting seizures with motor
manifestations including, but not limited to Tonic-Clonic,
Tonic-only, or Clonic-only seizures. A "motor manifestation" may in
some embodiments generally refer to muscle activity, whether
sustained or otherwise.
[0056] Apparatuses as described herein may be useful for monitoring
a person to determine whether the person may be having a seizure,
and for initiating an alarm. The methods described herein may be
flexible, e.g., such methods may be customized for an individual.
Moreover, such methods may be adaptive, and may improve as data is
collected, e.g., for a given patient or for a certain patient
demographic. Furthermore, apparatuses described herein may be
suited for organizing and/or prioritizing the collection of large
amounts of data, e.g., data that may be collected in a
substantially continuous manner, such as while a seizure-prone
individual is in a home setting.
[0057] In general terms, EMG electrode signals may be collected and
processed to determine seizure variables. A "seizure variable" may
in some embodiments refer to a criterion or criteria of one or more
portions of data collected from the output signal of a detector.
For a given set of data, a seizure variable may have one or more
numerical values associated with it. For example, the amplitude of
a signal may be a seizure variable that may have one or more
numerical values associated with it for a given set of data. A
value of a seizure variable may be compared to a threshold level
and may be used as an input in an algorithm for determining whether
a seizure may have occurred.
[0058] A processing method may include calculating one or more
seizure variable values and may further include comparing such
values to one or more thresholds that may characterize a seizure.
Data registers may be populated based upon such a comparison, and
used to evaluate whether to initiate an alarm protocol. The
weighting of data in different registers, and thus the importance
of different characteristics of EMG data, may be customized for an
individual patient or patient demographic, and may adapt as the
system obtains more information for a patient or patient
demographic.
[0059] A variety of suitable systems may be suitable for collecting
large amounts of EMG and other patient-related data, organizing
such data for system optimization, and for initiating an alarm in
response to a suspected seizure. FIG. 1 illustrates an exemplary
embodiment of such a system. In the embodiment of FIG. 1, a seizure
detection system 10 may include a detection unit 12, an optional
base station 14, an optional video monitor 9 and an optional alert
transceiver 16. The detection unit may comprise one or more EMG
electrodes capable of detecting electrical signals from muscles at
or near the skin surface of a patient, and delivering those
electrical EMG signals to a processor for processing. The base
station may comprise a computer capable of receiving and processing
EMG signals from the detection unit, determining from the processed
EMG signals whether a seizure may have occurred, and sending an
alert to a caregiver. An alert transceiver may be carried by, or
placed near, a caregiver to receive and relay alerts transmitted by
the base station.
[0060] In using the apparatus of FIG. 1, for example, a person 11
susceptible to epileptic seizures may be resting in bed, or may be
at some other location as daily living may include, and may have a
detection unit 12 in physical contact with or in proximity to his
or her body. The detection unit 12 may be a wireless device so that
a person may be able to get up and walk around without having to be
tethered to an immobile power source or to a bulkier base station
14. For example, the detection unit 12 may be woven into a shirt
sleeve, or may be mounted to an armband or bracelet. In other
embodiments, one or more detection units 12 may be placed or built
into a bed, a chair, an infant car seat, or other suitable
clothing, furniture, equipment and accessories used by those
susceptible to seizures. The detection unit 12 may comprise a
simple sensor, such as an electrode, that may send signals to the
base station for processing and analysis, or may comprise a "smart"
sensor having some data processing and storage capability. In some
embodiments, a simple sensor may be connected via wire or
wirelessly to a battery-operated transceiver mounted on a belt worn
by the person.
[0061] The system may monitor the patient, for example, while
resting, such as during the evening and nighttime hours. If the
detection unit 12 on the patient detects a seizure, the detection
unit 12 may communicate via wire or wirelessly, e.g., via a
communications network or wireless link, with the base station 14
and may send some signals to the base station device for more
thorough analysis. For example, the detection unit 12 may process
and use EMG signals (and optionally ECG and temperature sensor
signals) to make an initial assessment regarding the likelihood of
occurrence of a seizure, and may send those signals and its
assessment to the base station 14 for separate processing and
confirmation. If the base station 14 confirms that a seizure is
likely occurring, then the base station 14 may initiate an alarm
for transmission over the network 15 to alert a caregiver by way of
email, text, or any suitable wired or wireless messaging indicator.
In some embodiments, if one or more of the detection unit 12, the
base station 14, or a caregiver, e.g., a remotely located caregiver
monitoring signals provided from the base station, determines that
a seizure may be occurring a video monitor 9 may be triggered to
collect information.
[0062] The base station 14, which may be powered by a typical
household power supply and contain a battery for backup, may have
more processing, transmission and analysis power available for its
operation than the detection unit 12, may be able to store a
greater quantity of signal history, and evaluate a received signal
against that greater amount of data. The base station 14 may
communicate with an alert transceiver 16 located remotely from the
base station 14, such as in the bedroom of a family member, or to a
wireless device 17, 18 carried by a caregiver or located at a work
office or clinic. The base station 14 and/or transceiver 16 may
send alerts or messages to caregivers, or medical personnel via any
suitable means, such as through a network 15 to a cell phone 17,
PDA 18 or other client device. The system 10 may thus provide an
accurate log of seizures, which may allow a patient's physician to
understand more quickly the success or failure of a treatment
regimen. Of course, the base station 14 may simply comprise a
computer having installed a program capable of receiving,
processing and analyzing signals as described herein, and capable
of transmitting an alert. In other embodiments, the system 10 may
simply comprise, for example, EMG electrodes and a smartphone, such
as an iPhone, configured to receive EMG signals from the electrodes
for processing the EMG signals as described herein using an
installed program application. In further embodiments, so-called
"cloud" computing and storage may be used via network 15 for
storing and processing the EMG signals and related data. In yet
other embodiments, one or more EMG electrodes could be packaged
together as a single unit with a processor capable of processing
EMG signals as disclosed herein and sending an alert over a
network. In other words, the apparatus may comprise a single item
of manufacture that may be placed on a patient and that does not
require a base station separate transceiver.
[0063] In the embodiment of FIG. 1, the signal data may be sent to
a remote database 19 for storage. In some embodiments, signal data
may be sent from a plurality of epileptic patients to a central
database 19 and "anonymized" to provide a basis for establishing
and refining generalized "baseline" sensitivity levels and signal
characteristics of an epileptic seizure. The database 19 and base
station 14 may be remotely accessed via network 15 by a remote
computer 13 to allow updating of detector unit and/or base station
software, and data transmission. The base station 14 may generate
an audible alarm, as may a remote transceiver 16. All wireless
links may be two-way for software and data transmission and message
delivery confirmation. The base station 14 may also employ one or
all of the messaging methods listed above for seizure notification.
The base station 14 may provide an "alert cancel" button to
terminate the incident warning.
[0064] In some embodiments, a transceiver may additionally be
mounted within a unit of furniture or some other structure, e.g.,
an environmental unit or object. If a detection unit is
sufficiently close to that transceiver, such a transceiver may be
capable of sending data to a base station. Thus, the base station
may be aware that information is being received from that
transducer, and therefore the associated environmental unit. In
some embodiments, a base station may select a specific template
file, e.g., such as including threshold values and other data as
described further herein, that is dependent upon whether or not it
is receiving a signal from a certain transceiver. Thus, for
example, if the base station receives information from a detector
and from a transducer that is associated with a bed or crib it may
treat the data differently than if the data is received from a
transducer associated with another environmental unit, such as, for
example, clothing typically worn while an individual may be
exercising
[0065] The embodiment of FIG. 1 may be configured to be minimally
intrusive to use while sleeping or minimally interfere in daily
activities, may require a minimum of electrodes such as one or two,
may require no electrodes to the head, may detect a seizure with
motor manifestations, may alert one or more local and/or remote
sites of the presence of a seizure, and may be inexpensive enough
for home use.
[0066] FIG. 2 illustrates an embodiment of a detection unit 12 or
detector. The detection unit 12 may include EMG electrodes 20, and
may also include ECG electrodes 21. The detection unit 12 may
further include amplifiers with leads-off detectors 22. In some
embodiments, one or more leads-off detectors may provide signals
that indicate whether the electrodes are in physical contact with
the person's body, or otherwise too far from the person's body to
detect muscle activity, temperature, brain activity or other
patient phenomena.
[0067] The detection unit 12 may further include a temperature
sensor 23 to sense the person's temperature. Other sensors (not
shown) may be included in the detection unit, as well, such as
accelerometers. Signals from electrodes 20 and 21, temperature
sensor 23 and other sensors may be provided to a multiplexor 24.
The multiplexor 24 may be part of the detection unit 12 or may be
part of the base station 14 if the detection unit 12 is not a smart
sensor. The signals may then be communicated from the multiplexor
24 to one or more analog-to-digital converters 25. The
analog-to-digital converters may be part of the detection unit 12
or may be part of the base station 14. The signals may then be
communicated to one or more microprocessors 26 for processing and
analysis as disclosed herein. The microprocessors 26 may be part of
the detection unit 12 or may be part of the base station 14. The
detection unit 12 and/or base station 14 may further include memory
of suitable capacity. The microprocessor 26 may communicate signal
data and other information using a transceiver 27. Communication by
and among the components of the detection unit 12 and/or base
station 14 may be via wired or wireless communication.
[0068] Of course, the exemplary detection unit of FIG. 2 may be
differently configured. Many of the components of the detector of
FIG. 2 may be base station 14 rather than in the detection unit 12.
For example, the detection unit may simply comprise an EMG
electrode 20 in wireless communication with a base station 14. In
such an embodiment, A-D conversion and signal processing may occur
at the base station 14. If an ECG electrode 21 is included, then
multiplexing may also occur at the base station 14.
[0069] In another example, the detection unit 12 of FIG. 2 may
comprise a electrode portion having one or more of the EMG
electrode 20, ECG electrode 21 and temperature sensor 23, in wired
or wireless communication with a small belt-worn transceiver
portion. The transceiver portion may include a multiplexor 24, an
A-D converter 25, microprocessor 26, transceiver 27 and other
components, such as memory and I/O devices (e.g., alarm cancel
buttons and visual display).
[0070] FIG. 3 illustrates an embodiment of a base station 14 that
may include one or more microprocessors 30, a power source 31, a
backup power source 32, one or more I/O devices 33, and various
communications means, such as an Ethernet connection 34 and
transceiver 35. The base station 14 may have more processing and
storage capability than the detection unit 12, and may include a
larger electronic display for displaying EMG signal graphs for a
caregiver to review EMG signals in real-time as they are received
from the detection unit 12 or historical EMG signals from memory.
The base station 14 may process EMG signals and other data received
from the detection unit 12. If the base station 14 determines that
a seizure is likely occurring, it may send an alert to a caregiver
via transceiver 35.
[0071] Various devices in the apparatus of FIGS. 1-3 may
communicate with each other via wired or wireless communication.
The system 10 may comprise a client-server or other architecture,
and may allow communication via network 15. Of course, the system
10 may comprise more than one server and/or client. In other
embodiments, the system 10 may comprise other types of network
architecture, such as a peer-to-peer architecture, or any
combination or hybrid thereof.
[0072] FIG. 4 illustrates an exemplary a method 36 of monitoring
EMG and other signals for seizure characteristics, and initiating
an alarm response if a seizure is detected. Such a method may
involve collecting of EMG signals, calculating one or more values
of a seizure variable, and using such seizure variable data to
populate processor or memory registers. In general, one or more
seizure variables and one or more registers may be included in data
analysis. In a step 38, EMG signals and other detector output
signals may be collected. Output signals may be collected in a
substantially continuous manner or periodically. Output signals may
be processed in a step 40 to obtain seizure variable data. The data
values may be used to populate one or more detection registers, as
shown in step 42. Processing of output signals and population of
detection registers may be executed during a defined period of
time, i.e., collection time window. At the expiration of such a
collection time window, each detection register may transfer its
contents, if any, to one or more accumulation registers (as shown
in step 44), and the contents of one or more detection registers,
if any, may be cleared. After expiration of the collection time
window, and after adjustment (increase or leakage) of accumulation
registers, the cycle may repeat itself (as shown by line 46), i.e.,
detector output may be collected during a subsequent collection
window. Periodically, a supervisory algorithm may analyze the
contents of one or more accumulation registers to determine whether
a seizure is likely occurring (step 48). If the supervisory
algorithm determines that the sum of values or a weighted sum of
values in the accumulation registers exceeds a threshold then an
alarm protocol may be initiated (step 50). Alternatively, the
supervisory register may determine that the contents of
accumulation registers do not indicate that a seizure is likely and
the system may wait for a next analysis period (step 52).
[0073] As discussed below, a supervisory algorithm may comprise a
number of sub-routines that use various seizure variable values in
the accumulation and/or detection registers. As shown by way of
example in FIG. 4, methods may involve the population of individual
detection registers with a data value and addition of such a data
value to accumulation registers (steps 38, 40, 42, and 44). A
sub-method may include steps involved in the population of
individual detection registers and accumulation registers. Each
sub-method may consider one or more characteristics of the
collected data and perform process analysis on such
characteristics. Individual sub-methods may include, by way of
nonlimiting example, detection of signal bursts and detection of
GTC waveforms. Sub-methods may process data in the time domain, the
frequency domain, or, in some embodiments, process portions of data
in both the time domain and frequency domain. Before discussion of
those individual sub-methods in greater detail, it is helpful to
consider some general aspects of data collection, the detectors
used, as well as processing steps, such as data filtration that may
be involved in various sub-methods. In addition, it is instructive
to discuss exemplary EMG signal data, as shown in FIGS. 5 and 6
discussed in more detail further herein.
[0074] As indicated in step 38 of FIG. 4, in some embodiments,
detection of seizures may be accomplished exclusively by analysis
of EMG electrode data. In other embodiments, a combination of EMG
and other detectors may be used. For example, temperature sensors,
accelerometers, ECG detectors, other detectors, or any combinations
thereof, may be used. Accelerometers may, for example, be placed on
a patient's extremities to detect the type of violent movement that
may characterize a seizure. Similarly, ECG sensors may be used to
detect raised or abnormal heart rates that may characterize a
seizure. Thus, a monitoring device may detect an epileptic seizure
without the customary multitude of wired electrodes attached to the
head, as typical with EEG. Combination of EMG electrodes with other
detectors may, for example, be used with particularly difficult
patients. Patients with an excessive amount of loose skin or high
concentrations of adipose tissue, which may affect the stability of
contact between an electrode and the skin, may be particularly
difficult to monitor. In some embodiments, an electrode may be
attached to a single muscle, and in other embodiments a combination
of two or more electrodes may be used. Electrodes may, for example,
be attached to an agonist and antagonist muscle group or signals
from other combinations of different muscles may be collected.
[0075] In general, the system described herein is compatible with
any type of EMG electrode, such as, for example, surface monopolar
electrodes or bipolar differential electrodes or electrodes of any
suitable geometry. Such electrodes may, for example, by positioned
on the surface of the skin, may or may not include application of a
gel, and may, in some embodiments, be Ag/AgCl electrodes. The use
of a bipolar EMG electrode arrangement, e.g., with a reference lead
and two surface inputs, allows for the suppression of noise that is
common to those inputs. That is, a differential amplifier may be
used, and a subtraction of the signals from one input with respect
to the other may be accomplished, and any differences in signal
between the inputs amplified. In such an approach, signals that are
common to both inputs (such as external noise) may be substantially
nullified and preferential amplification of signals originating
from muscle depolarization may be achieved.
[0076] An EMG signal may be collected for a given time period,
e.g., a time domain electrode signal may be collected. Time domain
electrode data, may be converted to frequency data, i.e., spectral
content, using techniques such as Fast-Fourier Transform (FFT). In
reference to FIG. 4, the conversion of data between the time and
frequency domain may be included in a processing step 40. Other
aspects of data processing may include smoothing data, application
of one or more frequency filters, fitting data in a given region to
a particular function, and other processing operations
[0077] FIG. 5 (which comprises FIGS. 5a and 5b) provides an example
of EMG data 54 collected over a time period of about 2 seconds. The
data in FIG. 5 may exemplify data collected by placing a bipolar
differential electrode over the biceps or triceps of a patient.
FIG. 6 illustrates some of the EMG data 54 of FIG. 5 converted to
the frequency domain. The EMG data 74 in FIG. 6 may represent, for
example, a one-second epoch of the EMG data 54 converted to the
frequency domain. For an EMG electrode, visual representation of
frequency domain data may also be referred to as a spectral
graph.
[0078] Referring now to the time domain data for the graph of FIG.
5, the vertical axis or scale in FIG. 5a is signal amplitude, e.g.,
the differential signal between the pair of EMG electrode inputs,
and the horizontal axis or scale shows time (in FIG. 5, the time
window is approximately two seconds). In reference to any of the
graphs described herein the term amplitude may be used, and such
may refer to either the magnitude of signal, or absolute value of
magnitude, as may be appropriate for a given calculation. Signals
collected may, for example, be rectified, and unless otherwise
noted, detection of bursts as described herein involves rectified
signal data. As shown in FIG. 5, the amplitude (or absolute value
of the amplitude) appears to experience a sustained increase 62 at
least three times (56, 58, and 60) during the 2-second period. Such
sustained increase may be indicative of what is referred to as a
burst, or signal or data burst. As discussed in more detail below,
fluctuations in time periods between suspected bursts, such as 66
or 68, may be used to calculate a baseline. Fluctuations in a
baseline region, i.e., noise, may be related to a peak to peak
value, a root mean square (RMS) value or other metric. FIG. 5b
illustrates a portion of the EMG data 54, namely, the region of
data including burst 60 and adjacent period. In FIG. 5b, a RMS
noise value 72 and amplitude 70 are indicated. The signal-to-noise
ratio (SNR or S/N) of burst 60 is, in this example, about 4:1,
i.e., amplitude 70 is about four times larger than the noise value
72. The EMG data of FIG. 5 is discussed in further detail with
regards to a burst detection sub-method in FIG. 7.
[0079] Referring now to the exemplary data of FIG. 6, the vertical
scale represents the magnitude of a given frequency (which may be
referred to as spectral density) and the horizontal scale is signal
frequency. Note that the spectral data in FIG. 6 indicates a
curving slope with decreasing magnitude as the frequency increases,
i.e., the spectral density generally decreases as the frequency
increases. The ratio of spectral density at a lower frequency to
the spectral density at a higher frequency may be a seizure
variable that, for any given portion of electrode data, may have an
associated value. For example, for the data shown in FIG. 6 the
ratio of spectral density at a frequency of about 200 Hz (76) to
the spectral density at about 400 Hz (78) may have a value of about
1.1.
[0080] Also, as illustrated in the expanded portion of the same
data in FIG. 6b, which shows at least a portion of the
characteristic GTC waveform, a region of elevated spectral density
80, i.e., a relatively high-frequency "bump" between approximately
300-500 Hz, and particularly around 400 Hz 82 is shown. That is,
the spectral density 80 at frequency 82 in that region is elevated
above the spectral density 84, e.g., within a "slumped" region,
approximately located at a frequency 86 of about 300 Hz. The term
"slump region" or "slump" may in some embodiments refer to a
portion of spectral data generally possessing the property of
having positive curvature, i.e., a slump region refers to a local
minimum in a set of data. The term "bump region" or "bump" may in
some embodiments refer to a portion of spectral data where the data
generally possesses the property of having negative curvature,
i.e., a bump region refers to a local maximum in a set of data. To
generally possess a positive or negative curvature means that local
fluctuations in individual data points may be averaged or smoothed
out of the data. That is, neglecting local fluctuations, e.g., due
to noise, a data set may possess a property of curvature.
[0081] The ratio of spectral density at a frequency 86 to the
spectral density at a frequency 82, or slump to bump ratio, may be
used as a seizure variable. In some embodiments, the slump to bump
ratio may be used as a metric for detection of a GTC waveform.
However, more advanced data analysis techniques, e.g., looking at a
greater number of data points and/or advanced pattern recognition
algorithms, may also be used to identify a GTC waveform. In some
embodiments, a detection unit may include instructions for
calculation of a slump to bump ratio and a base unit may calculate
a slump to ratio and also corroborate the slump to bump calculation
with more advanced pattern recognition analyses. The EMG data of
FIG. 6 and the above data features are discussed in further detail
with regards to a GTC waveform detection sub-method as described,
for example, in FIGS. 11 and 12.
[0082] Referring back to FIG. 4, the collection of EMG data may be
accomplished with a detection unit and that detection unit may
execute an initial analysis and processing of data. In some
embodiments, if the detection unit determines that a seizure is
likely occurring, it may send data to a base station, where further
processing may occur. Thus, a detection unit, a base station or
both may process EMG signals, and either or both devices may
execute a seizure detection sub-method. Such a sub-method may
characterize particular features of EMG data, and may, based upon
such a characterization, direct the transfer of data between data
registers and accumulation registers. Those aspects of sub-methods,
such as described herein in reference to FIGS. 7 and 10-13, may
involve aspects of steps 38, 40, 42, 44, and 46 of method 36. A
sub-method may feed data into a supervisory algorithm.
[0083] FIG. 7 illustrates one embodiment of a sub-method 88 which
may be used for analysis of data bursts. In a step 90 of FIG. 7, a
detection unit and/or base station may select a protocol for
analysis of data bursts. The selection of an analysis protocol may,
for example, be indicated in a template file. Such a template file
may include instructions to choose a routine to smooth data, a
routine to filter data, a routine to treat the data in some other
manner or combinations of routines thereof. Such routines may be
executed by either the detection unit, base station or both. The
analysis protocol may include a peak detection program, which, for
example, after band-pass filtering and rectification may identify
and shape a data burst, as shown in the examples of FIG. 9 and FIG.
10. Any suitable peak detection technique may be used (e.g.,
continuous wavelet transform), and may in some embodiments include,
for example, data smoothing techniques (e.g., moving average
filter, Savitzky-Golay filter, Gaussian filter, Kaiser Window,
various wavelet transforms, and the like), baseline correction
processes (e.g., monotone minimum, linear interpolation, Loess
normalization, moving average of minima, and the like) and
peak-finding criteria (SNR, detection/intensity threshold, slopes
of peaks, local maximum, shape ratio, ridge lines, model-based
criterion, peak width, and the like).
[0084] A peak detector may have separate attack and decay rates.
These rates may be individually adjusted. Since there frequently
may be plenty of sustained amplitude during a real burst, fear of
the peak detected signal decaying too quickly during bursts is
generally not a problem. Therefore, the decay rate may be set to
decay rather quickly following a burst. Usually the time between
bursts is longer than the burst itself, and so there may be no
reason to speed up the decay. However, a noise spike between bursts
could artificially cause the peak detector output to jump up to a
level that would make distinguishing real seizure bursts a problem.
Therefore, the attack rate may be carefully controlled to prevent
this from occurring.
[0085] In step 91 of the method of FIG. 7, a burst detection
algorithm may be initiated. Burst analysis may be triggered, for
example, by detection of an EMG signal having an amplitude value
that meets or exceeds a burst analysis amplitude threshold. Within
the burst detection window, the EMG data may be analyzed for
elevated amplitude using, e.g., a peak detection program. Regions
of elevated amplitude may be classified as potential bursts. For
example, referring back to FIG. 5, at least three periods of
sustained elevation of amplitude (56, 58, and 60) may be identified
in the approximately 2-second epoch. Regions of elevated amplitude
within the burst detection window may be measured for amplitude,
width, and a SNR may also be determined. A portion of data, e.g.,
identified as a possible peak, may have amplitude associated with
it, e.g., peak amplitude, median, mean or other metric may be
calculated.
[0086] In step 92 of FIG. 7, EMG signal data, such as within a
certain time period (burst detection window), may be analyzed for
bursts. For example, for suspected data burst 56, amplitude 62 may
be measured. A burst may have an amplitude that is elevated over
surrounding portions of data, and that elevated amplitude may
extend for a period of time. That is, a burst may have a burst
width, such as burst width 64. To determine a burst width, a
leading edge of a burst and a trailing edge of a burst may be
determined. To detect the leading edge and trailing edge of a
burst, changes in amplitude for successive data points may be
measured, e.g., the rate of change of amplitude with time may be
calculated. Any other suitable technique, such as those described
above, may be used, as well. In some embodiments, burst width may
be categorized by calculating, for a region of time, whether a
threshold minimum amplitude is met at a given probability, e.g.,
where a majority of points show elevated amplitude above some
threshold.
[0087] Signal to noise calculations may involve, for example,
establishing a baseline by determining fluctuations in detector
signal, i.e., baseline noise, in a time period immediately prior to
data in a time suspected of containing bursts. For example, an EMG
signal may be relatively quiet in the time leading up to a seizure,
as discussed in more detail in connection with FIG. 25, below. That
quiet period may be used to establish a baseline.
[0088] A baseline may also be established by looking at
fluctuations between burst periods within the same time window
suspected of having bursts. For example, referring back to the EMG
data of FIG. 5, data fluctuations in time periods between suspected
bursts, such as the data in the time periods 66 or 68, may be used
to calculate a baseline. Fluctuations in a baseline region, i.e.,
noise, may be related to a peak to peak value, a RMS value or other
suitable baseline detection metric. In FIG. 5 an expanded region of
data, i.e., the region of data including burst 60 and adjacent
period, is shown in FIG. 5b, and a root mean square noise value 72
and amplitude 70 are approximately indicated. The S/N of burst 60
may, for example, be about four, i.e., amplitude 70 is about four
fold larger than the noise value 72.
[0089] It should be noted that the baseline established by looking
at fluctuations between burst periods may be different than the
baseline established by looking at a pre-seizure quiet time. Thus,
different peak detection algorithms may be run for each, or the
same algorithm may be ramped up or down with respect to baseline
detection depending on whether detecting quiet time or seizure
activity. For example, a baseline detector may be a peak detector
having a much longer time constant than a peak detector used for
signal envelope generation. This baseline detector may rise up to a
higher level during a tonic phase but may ramp down during a clonic
phase of activity. A negative peak detector may also be employed to
ramp a baseline detector down more quickly during relatively quiet
times so as to distinguish the bursts more readily.
[0090] In step 94, the burst detection algorithm may determine if
the EMG signal data within a burst detection window meet various
requirements or thresholds or other criteria to qualify regions of
elevated amplitude as bursts. For example, the algorithm may
determine whether one or more regions of elevated amplitude meet
requirements for amplitude, width, and time between regions of
elevated amplitude to qualify as seizure bursts. For example, a
sub-method for detecting bursts may detect amplitudes above a
certain threshold that are closer than Y seconds apart and farther
than Z seconds apart. Such requirements (or burst criteria) may be
provided in a template file. For example, referring to Table 1, the
minimum S/N criteria may be pulled from the template file and
compared to the calculated value of S/N for each suspected
burst.
[0091] Generally, a burst may be characterized by a sudden increase
in the amplitude of the EMG electrode signal from a lower amplitude
level, maintenance of that increased amplitude level for a
specified minimum amount of time, return of the amplitude level to
a lower level of electrode signal after no more than a specified
maximum time, and maintenance of the lowered amplitude level for a
specified minimum time. FIG. 8A and FIG. 8B illustrate exemplary
model forms or envelopes of signal bursts after filtering,
rectification and peak detection. Generally, the lower amplitude
signal level may not go to zero. The lower amplitude above zero is
signal noise. The ratio of the burst amplitude level to the noise
level is the SNR. For example, if the signal level of the burst is
1 volt, and the noise is 0.35 volts, then the SNR would be 1/0.35,
or 2.86. In the example of FIG. 8, the peak amplitude 120 of EMG
signal data may be compared to criterion associated with peak
amplitude. If the amplitude 120 is greater than a minimum amplitude
criterion 120a, and less than a maximum amplitude criterion 120b,
then the ratio of peak amplitude to the level of noise 102 may be
determined and compared to a burst amplitude criterion, e.g., a SNR
threshold. If the peak amplitude meets the SNR threshold, then the
EMG signal data may qualify as a burst (or the start of a burst)
with respect to amplitude. A maximum burst amplitude requirement
may be helpful in eliminating from consideration elevated amplitude
EMG data caused from external noise sources that may introduce
amplitude well above the amplitudes capable of being produced by
the human body.
[0092] FIG. 8A also shows the region of elevated amplitude as
having a width 114 is shown. The width 114 may be compared to a
minimum burst width (dashed line 116) and a maximum burst width
(dashed line 118). As may be seen in FIG. 8B, the width 114 falls
between the minimum and maximum burst width thresholds, and thus
qualifies the region of elevated amplitude as a burst with respect
to width. A maximum burst width requirement may be helpful in
eliminating from consideration elevated amplitude EMG data that is
from voluntary muscle activity, a noise source or is caused by
electrode connectivity problems. That could help eliminate falsely
identifying real or apparent high-amplitude muscle activity as a
seizure.
[0093] FIG. 8B shows examples of two successive bursts (104 and
106) separated by a time period 108. In FIG. 8B the time between
bursts 108 may, for example be compared to criterion values
associated with a minimum period between successive bursts (dashed
line 110) and a maximum period between successive bursts (dashed
line 112). If a sufficient quantity of bursts succeed each other
within the minimum and maximum time periods, then successive bursts
may qualify as a burst train indicative of a seizure. However, not
all burst trains indicate a seizure, and a periodicity algorithm
(discussed in more detail below) may be used to further evaluate
the likelihood that a seizure is occurring. For example, extremely
regular bursts may not indicate a seizure. Sporadic bursts may not
indicate a seizure, either, or if spaced sufficiently far apart,
represent minimal threat of imminent harm from seizure.
[0094] After reaching the end of the burst detection window, the
burst detection algorithm may wait for a delay period before
analyzing data in a subsequent burst detection window. By adding a
delay, the burst detection algorithm may ensure that new data is
analyzed. If analysis of a burst window, or analysis of one or more
successive burst detection windows reveals no bursts or
near-bursts, then the burst detection sub-method may pause, as seen
at step 95, until the burst analysis amplitude threshold triggers
activation of the sub-method.
[0095] The burst amplitude, width and periodicity values may be
stored in registers for use by a supervisory algorithm to determine
the likelihood of a seizure occurring. If the supervisory algorithm
determines that a seizure is occurring, then it may declare an
alarm, and cause the base station 14 to send an alert to a
caregiver.
[0096] Criterion values may, for example, be included in a template
file. More specifically, Table 1 lists exemplary criteria that that
may be included in a template file which may be used in a
sub-method for evaluation of data bursts. Each criterion may be a
variable that may be changed to adjust the sensitivity of the
seizure detection method. Of course, not all of the criteria need
be used. For example, maximum burst amplitude may be considered
optional if unduly limiting for a particular patient. Likewise,
additional criteria may be used. For example, if signal amplitude
is sufficiently high to trigger the burst detection sub-method, but
does not quite meet the minimum burst amplitude even though it
meets burst width criteria, then its variance from the minimum
burst amplitude may be negatively weighted by a certainty value
criterion. A certainty value criterion may be, for example, a
percentage value. If the measured amplitude is 95% of the minimum
burst amplitude, then the certainty value may be set accordingly.
If successive bursts have sufficient periodicity to qualify as a
burst train, the negatively-weighted burst may be included in the
train to further test periodicity. If a certain number of
negatively-weighted bursts appear in the data, then a supervisory
algorithm may lower the minimum burst amplitude thresholds to
increase the sensitivity of the burst detection method for the
particular patient being monitored. Similar weighting may be done
with respect to signal values that do not quite meet the other
burst criteria. Certainty values may be used by the burst detection
method, other sub-methods described herein, and the supervisory
algorithm.
TABLE-US-00001 TABLE 1 Template data for a burst detection
sub-method Variable Value/unit Type Burst analysis minimum
amplitude threshold XX amplitude Criterion for initiation of burst
detection algorithm Burst detection window XX seconds Routine
selection Delay between adjacent burst detection windows XX seconds
Routine selection Minimum burst width XX seconds Criterion for
burst count Maximum burst width XX seconds Criterion for burst
count Burst envelope peak detector attack rate XX Routine selection
Burst envelope peak detector decay rate XX Routine selection
Minimum burst amplitude XX amplitude Criterion for burst count
Maximum burst amplitude XX amplitude Criterion for burst count
Minimum S/N XX Criterion for burst count Minimum period between
successive bursts XX seconds Criterion for burst count Maximum
period between successive bursts XX seconds Criterion for burst
count Decay rate XX Data feature/weighting coefficient Decay rate
(S/N) modifier XX Data feature/weighting coefficient Selection of
filter protocol (if applied) XX Routine selection Selection of
smoothing protocol (if applied) XX Routine selection Calculation
method XX Routine selection Baseline calculation method XX Routine
selection Coefficient (combination with supervisory XX Weighting
coefficient algorithm)
For clarity, the "XX" is simply a value placeholder, and should not
be construed to connote magnitude or precision in any way.
[0097] Referring back to FIG. 7, in a step 96, one or more
detection registers may be loaded with burst values for a detection
window. For example, a burst count register may be used to contain
a value corresponding to the number of detected bursts within the
burst detection window. For example, if the two-second time period
of FIG. 5 was a burst detection window, then the EMG data within
that window may be analyzed for bursts. In FIG. 5, for example, the
EMG signal data shows three bursts. Thus, a value of 3 may be
stored in the burst count register. Other registers may be used to
store other burst values, such as amplitude, periodicity, width,
certainty values, and so forth.
[0098] Following each burst detection cycle, e.g., analysis of a
burst detection window, the detection register may, in some
embodiments, add its contents to one or more burst accumulation
registers (step 98). Before analyzing the data in subsequent burst
detection windows, the detection registers may be cleared to allow
storage of burst data for the subsequent burst detection windows.
The detection registers may then begin storing burst values during
another cycle, or, in some embodiments, begin counting bursts after
a certain delay period.
[0099] In some embodiments, the EMG signal data may be written to a
circular buffer in RAM in the device hardware. One advantage of
such a strategy may be that less RAM is used because the processed
data may store only a pattern of the data, such as peak detected
values, and not a point by point data file of full signal data.
That is, a voltage (or other electrical parameter that reflects
amplitude of the detection unit) at each corresponding point in
time need not be stored. For example, in some embodiments, only the
data necessary to derive a model form such as indicated in FIG. 8
and FIG. 9 may be stored. It should be appreciated in those figures
that noise in regions between detected bursts is depicted to be
maintained at a constant level. Thus, only a calculated value of
the noise, e.g., such as RMS amplitude (102), may be stored and not
all of the individual fluctuations in the baseline data. Thus, the
data file in RAM may be significantly compressed. In some
embodiments, as opposed to storing a compression of the data in a
time window, all raw data from a given window may be stored in a
circular buffer in RAM. It should thus be appreciated that an
algorithm may look at any given preceding time window at any point
in the algorithm. Such may be used, for example, to consider how
any given value of EMG data has changed between one or more time
windows.
[0100] In some embodiments, each burst may be weighted with a value
that is not only related to detection of a burst but also related
to the certainty of burst detection. Certainty values may, for
example, be related to the normalized amplitude or the ratio of the
normalized amplitude to detector noise. For example, a signal burst
may be characterized by transition from approximately 100% of the
normalized amplitude to approximately 35% of the normalized
amplitude. The certainty value may be approximately 65, which
number may be loaded into a register whose maximum value could be
approximately 100.
[0101] As denoted in step 97, one or more of the detection
registers may add their contents to one or more accumulation
registers. For example, a burst count detection register may add
its value to a burst count accumulation register.
[0102] In step 98, the accumulation registers may, in addition to
accepting a data value from the detection register, adjust the
value of any previous data which may be held. For example, in some
embodiments, the burst count accumulation register may hold a value
that is related to the quantity of bursts collected in a preceding
number of burst detection cycles. That is, each time the burst
count detection register adds contents from one cycle, the burst
count accumulation register may remove a data value that was added
during some preceding cycle. Thus, the burst count accumulation
register may, in some embodiments, act as a moving sum based on the
sum of counts from a number of preceding burst detection windows.
In such an embodiment, the computer may store in memory, e.g., in
any number of additional registers, the appropriate data value to
add or subtract from the burst count accumulation register. In
other embodiments, at the completion of a cycle, the burst count
detection register may add any contents, e.g., value of collected
bursts, to the burst count accumulation register and then remove a
certain value, i.e., it may leak at a certain rate. A leakage rate,
or decay rate as shown in Table 1, may be included in a template
file and may be adjusted to customize the burst detection
sub-method to a particular patient or patient demographic. In some
embodiments, the leakage rate may be a value that is modified based
upon another criterion. For example, the burst count accumulation
register may if one or more successive burst detection windows do
not contain any bursts.
[0103] In other embodiments, the rate of decay of the burst count
accumulation register may depend upon the S/N of bursts counted in
one or more given time window. In further embodiments, the burst
count accumulation register may be modified based on how the S/N of
bursts is changing. That is, the average S/N of detected bursts may
be tracked, e.g., the average S/N value of bursts in given time
windows may, at least for some period of time, be stored in memory,
such as in a circular RAM buffer. If the S/N of bursts changes
between time windows, such a change may be analyzed, and used to
modify the decay rate of the burst count accumulation register. In
general, if the S/N of bursts is increasing the decay rate of the
burst count accumulation register will drop by some factor and if
the S/N of bursts is decreasing the decay rate of the burst count
accumulation register will increase by some factor. In addition,
during step 98 the contents of the burst count accumulation
register, may decay in a manner that is dependent upon various
negative weighting factors. For example, if no bursts are detected
in a cycle, such may be an indication that a seizure is not
occurring, and the rate of decay of the burst count accumulation
register may be adjusted. Again, to analyze data in preceding time
windows, either point by point data or a model shape may be stored
in a circular buffer of RAM in the system hardware. Referring back
to FIG. 4, the value stored in the burst count accumulation
register is an example of one value that may be examined with a
supervisory algorithm.
[0104] In step 99, the burst detection algorithm may wait for a
time period equal to the burst detection window delay value before
analyzing EMG signal data in subsequent burst detection windows.
The burst detection registers may be cleared in step 100 before
analyzing EMG data in the next burst detection window. In some
embodiments, the burst detection algorithm may continue to run
until it finds one or more burst detection windows that do not
contain any bursts or near-bursts, or until the supervisory
algorithm triggers an alarm.
[0105] In general, the presence of qualified bursts, and a large
value being stored in the burst count accumulation register, may
increase the probability that a seizure event is declared. It is
also an aspect of methods described herein, negative weighting
factors may be used, for example, with respect to signal
characteristics that diminish the likelihood that a seizure is
occurring. For example, as discussed above, different negative
weighting factors, such as the absence of bursts in a preceding
time window, or a decreasing S/N may influence the leakage rate of
an accumulation register.
[0106] FIGS. 9A, 9B and 9C illustrate another embodiment of a burst
and burst train detection algorithm. The flowcharts of FIGS. 9A-9C
show logic flow, not actual routines. In an actual routine, they
would be called by the supervisory algorithm or be scheduled as
one-time passes by timer interrupt, not infinite loops. There are
two main routines, the burst detection algorithm (FIGS. 9A and 9B),
and the burst train detection algorithm (FIG. 9C). The burst
detection algorithm looks for a burst that meets the requirements
of amplitude (both min and max) and minimum width. If the minimum
spacing between detected bursts is too small, the burst train
detection algorithm will catch it. A burst train detection
algorithm may rely on a periodicity algorithm, as discussed
below.
[0107] In FIG. 10 an additional exemplary algorithm 113 (the
periodicity algorithm) is described that may, in some cases, act to
suppress the initiation of a seizure alarm. The periodicity
algorithm accomplishes this task by looking at the circular buffer
over a time frame and examining the regularity of time periods
between the detected bursts. A periodicity algorithm may scan
different the data values from various time windows that the burst
detection algorithm wrote into a circular buffer, and examine the
periodicity of signal characteristics, including those that may not
be indicative of a seizure.
[0108] In some embodiments, variables in the periodicity algorithm
may be: [0109] Periodicity Time Window (in seconds) [0110] Minimum
Average (or Standard) Deviation Allowed (percentage)
[0111] The periodicity time window variable is the period of time
over which the periodicity algorithm scans data. For example, the
periodicity time window may be sufficient to include some number of
burst detection windows from the burst detection algorithm. The
Deviation Allowed variable is the minimum value of how far from a
single frequency the bursts may be distributed to qualify as a
seizure. If the bursts huddle too closely around a specific
frequency, for example 1 Hz, then that burst train may not indicate
a real seizure. In some embodiments, values for the periodicity
algorithm may be empirically selected for default. This variable
could be altered based upon patient history, experience, patient
modeling and learning, and/or human feedback. In some embodiments,
a patient may, for example, partake in different activities, such
as, for example brushing teeth, exercising, walking or other
activities to collect data that may be used to establish defaults
for the periodicity algorithm.
[0112] In step 115 of the exemplary method of FIG. 10, the average
duration of the period between bursts within the periodicity time
window may be calculated. In step 117, each actual duration of the
value of each such time period may be subtracted from the average
time value, and the absolute values of the differences used to
calculate, in step 119, the average deviation of the periods, and
convert the average deviation to a percentage.
[0113] In step 121 the average deviation percentage may be compared
to threshold values. Such threshold values may be taught to the
system in operation and may be customized for the particular
environment that an individual may commonly occupy.
[0114] For example, if in a periodicity time window (measuring in
seconds), nine bursts were detected at the following times: [0115]
12, 13, 13.75, 14.35, 15, 15.8, 16.2, 16.5, 17.4 there would be 8
time periods between bursts. So, over a periodicity time window
including the foregoing epoch of 5.4 seconds, there were nine
bursts with eight periods between bursts. The average period may be
calculated as 5.4/8=0.675 seconds per burst. The time periods
between bursts are as follows:
[0115] 13-12=1
13.75-13=0.75
14.35-13.75=0.6
15-14.35=0.65
15.8-15=0.8
16.2-15.8=0.4
16.5-16.2=0.3
17.4-16.5=0.9
In this example, a simplified method allows the time around which a
burst is centered to serve as a time stamp for that burst. In other
words, each time the burst algorithm qualifies a burst, a time
stamp may be written into a circular buffer for use by the
periodicity algorithm. In other embodiments, real burst width may
be used to calculate the actual length of the time periods between
bursts. For example, if the burst occurring at 12 seconds lasted
for 0.02 seconds, then the time period between the burst starting
at 12 and the burst starting at 13 would be 0.98 seconds. The
absolute value of the deviations from the average may be calculated
as follows:
1-0.675=0.325
0.75-0.675=0.075
0.675-0.6=0.075
0.675-0.65=0.025
0.8-0.675=0.125
0.675-0.4=0.275
0.675-0.3=0.375
0.9-0.675=0.225
Averaging the absolute values may be accomplished as follows:
Sum of all deviations:
0.325+0.075+0.075+0.025+0.125+0.275+0.375+0.225=1.5
TABLE-US-00002 Average deviation: 1.5/8 = 0.1875
The percentage deviation of this average is: 0 0.1875/0.675=27.8%.
That is a significant deviation from the average and is unlikely to
be artificial. If the Minimum Average Deviation Allowed variable is
set, for example, to 15%, then the periodicity algorithm would
declare that confidence is high that this is a seizure and would
not vote against declaring that a seizure alarm. The result may be
placed in a register for use by the supervisory algorithm.
[0116] In another simplified example, the burst train could look
like this (in seconds):
17, 17.5, 18.02, 18.51, 19.04, 19.56, 20.1, 20.6, 21.13
[0117] So, over a periodicity time window including the foregoing
epoch of 4.13 seconds, there were nine bursts with eight periods
between bursts. The average period may be calculated as
4.13/8=0.51625 seconds per burst. The individual times between
bursts are as follows:
17.5-17=0.5
18.02-17.5=0.52
18.51-18.02=0.49
19.04-18.51=0.53
19.56-19.04=0.52
20.1-19.56=0.45
20.6-20.1=0.5
21.13-20.6=0.53
The absolute value of the deviations from the average are as
follows:
0.51625-0.5=0.01625
0.52-0.51625=0.00375
0.51625-0.49=0.02625
0.53-0.51625=0.01375
0.52-0.51625=0.00375
0.51625-0.45=0.06625
0.51625-0.5=0.01625
0.53-0.51625=0.01375
The sum of all deviations may be calculated as follows:
0.01625+0.00375+0.02625+0.01375+0.00375+0.06625+0.01625+0.01375=1.6
TABLE-US-00003 The average deviation is therefore: 1.6/8 = 0.02
The percentage deviation of this average is thus: 0
0.02/0.51625=3.87%. This example thus shows a very regular pattern.
If the Minimum Average Deviation Allowed variable was set to 15%,
then the algorithm would declare that confidence is very low that a
true seizure is occurring and would vote against declaring a
seizure alarm. The result may be placed in a register for use by
the supervisory algorithm.
[0118] Of course, standard deviation calculations may be
substituted for average deviation calculations for a more
statistically accurate result.
[0119] The supervisory algorithm may use the results of the values
provided by the periodicity algorithm. That is, in steps 123 or 125
the algorithm may add either a positive or negative value to the
supervisory algorithm. The particular value added may depend upon
comparison to thresholds in step 121. The value added to the
supervisory algorithm may, in some embodiments, depend not only on
the particular decision, at step 121, but also on the certainty in
which the decision was qualified. In addition, the value added to
the supervisory algorithm may depend on other features measured.
For example, characteristic patterns in an environment may not only
have a certain periodicity they may also have certain amplitude.
For example, an algorithm may learn that a certain period is
typically identified with a certain signal amplitude and when those
characteristics are viewed together, an additive or super-additive
value may modulate the supervisory algorithm.
[0120] In a real seizure, the bursts can look like they are spaced
evenly. However, these are generated by the body and may be only
rarely evenly spaced. Real seizures are generally characterized by
some variance in the spacing between bursts. Other sources of
signals, that is, sources that are not derived from seizure muscle
activity, may be picked up by the EMG electrodes. For example,
mechanical vibration of the room or bed could result in a rhythmic
vibration of the arm or other muscle to which the electrodes are
attached. This could cause signals which may be picked up from the
electrodes and may have an elevated amplitude. However, these
signals may be very regular in frequency. Likewise, regular
voluntary body movements, such as from brushing teeth, may produce
bursts that look like a seizure. Whatever the source of
interference at the electrodes that may look like bursts, the
periodicity algorithm evaluates the periodicity of pseudo-bursts as
being too regular and therefore not indicative of a seizure.
[0121] FIG. 11 illustrates one embodiment of another sub-method
that may also contribute a value that may be that may be examined
with a supervisory register. In FIG. 11, one embodiment of a GTC
waveform detection algorithm 130 is illustrated. FIG. 12
illustrates another embodiment of a GTC waveform detection
algorithm 146. As previously described, in some embodiments, the
detection unit and the base station may analyze data in the same or
different ways. The embodiment of FIG. 10 may, for example, be
useful as an initial screen of data, i.e., it may be used to
determine whether a data set is sent to a base station. The
embodiment of FIG. 12 may, for example, involve the comparison of a
spectral shape to a large number of files stored in memory and may
be executed by a base station.
[0122] In a step 132, as shown in FIG. 11, a detection unit and/or
base station may select an analysis protocol. The selection of an
analysis protocol may, for example, be indicated in a template
file. Such a template file may include instructions to choose a
routine to smooth data, a routine for data filtration, a routine to
treat the data in some other manner, or combinations of routines
thereof. Such routines may be executed at various steps in
sub-method 130. In a step 134, data may be collected and FFT
methods may be used to convert data between the time and frequency
domains. In collection of EMG data, suitable sample rates may be
used as appropriate, for example, to avoid aliasing of the
frequency domain data. In a step 136, the frequency value
associated with a local minimum value and a local maximum value of
the power density may be determined. To accomplish such, the data
may typically be smoothed and a parabolic function fit to the data
in a frequency region suspected of being a local maximum. In
attempting to find local extreme values, the sub-method may find
that the EMG data does not meet criteria to be classified as a GTC
waveform. For example, the sub-method may find that in a given
region expected to show a local maximum or local minimum value, the
data does not exhibit such behavior.
[0123] The sub-method may, if local maximum and local minimum
values are found, calculate the area under the power
density/frequency curve for a region associated with the determined
local extreme values (step 138). For example, the program may
calculate the area under a region of 10 Hz centered on the
determined local maximum and also calculate the area under a region
of 10 Hz centered on the determined local minimum. The ratio of
these areas may be calculated, i.e., a slump to bump ratio may be
calculated, in a step 140, and compared to a threshold ratio, e.g.,
minimum and maximum threshold for acceptable slump to bump ratios.
If the slump to bump ratio is within the threshold bounds a value
may be added to a GTC detection register in a step 142. The value
added to the GTC detection register, may, in some embodiments, be
related to the certainty in which the slump to bump ratio was
detected. In a next step 144, the value of the GTC detection
register may be added to a GTC accumulation register. That is at
the completion of a cycle, i.e., after each GTC collection window,
the GTC detection register may add any contents, e.g., a value
reflecting a detected slump to bump ratio, to the GTC accumulation
register. In some embodiments, the GTC collection window may be the
same as the burst detection window, i.e., the GTC waveform
detection algorithm may analyze the same data that the burst
detection algorithm analyzes. The GTC accumulation register may
then be changed by a certain value, e.g., it may leak at a certain
rate.
[0124] Referring to FIG. 12, in a step 148, another embodiment of
the waveform detection algorithm may, for example, create an image
in memory representing the spectral content of the EMG signal over
a certain period of time. For example, one or more detectors may
collect data over a certain time window and then that data may be
converted to the frequency domain for spectral analysis. In a step
150, the waveform detection algorithm may evaluate the image, e.g.,
spectral data, and look for a characteristic GTC waveform. Any
number of spectral regions, such as a high frequency region of the
spectrum may be analyzed. In a step 152, a GTC accumulation
register may be populated in a manner that depends on how the
spectral data compares to a stored GTC waveshape template.
[0125] FIG. 13 illustrates one embodiment of a waveform regularity
detection algorithm 154. Like a periodicity algorithm, a waveform
regularity detection algorithm may be used to determine if bursts
are too regular in waveform to originate from seizure activity. In
a step 156, the amplitude and burst width of EMG signal data during
a time period may be determined. This may be accomplished in much
the same way as described in for the burst detection algorithm. In
a step 158 a waveform may be calculated, e.g., data from a
sub-period of time around a burst may be converted to the frequency
domain and a waveform calculated. The waveform may be calculated
and compared to waveforms that were collected for other bursts in
the time period. In some embodiments, if those waveforms are too
uniform, e.g., identical or very similar in at least some
characteristics, then a regularity accumulation register may be
incremented. Differences between waveforms may be calculated in a
manner similar to that of a periodicity algorithm, e.g., by
determining an average waveform, calculating the average deviation
of each waveform, and determining the percentage difference of the
average deviation from the average waveform. If that percentage
difference falls below a regularity threshold requirement (another
variable), then a regularity detection register may be populated.
In succeeding detection cycles, the regularity detection register
may add its contents to a regularity accumulation register. In some
embodiments, the waveform may look for uniformity within a given
time period by converting data collected over that time period to
the frequency domain and detecting a spike in amplitude over a very
narrow frequency range. In a step 150, if the waveform regularity
decreases then the regularity accumulation register may decay. As
previously noted, some seizure variables may either enhance or
weigh against the declaration of an alarm. In some embodiments, the
value a regularity accumulation register may serve to suppress the
declaration of an alarm. Referring back to FIG. 4, the values
stored in either GTC accumulation register of sub-methods 130 or
146, or the value stored in the regularity accumulation register,
such as described in sub-method 160, may be a value that may be
used by a supervisory algorithm.
[0126] The value stored in all or some of the above referenced
accumulation detection registers, e.g., such as described in
relation to FIGS. 7, 11-13, and 18, or input from other algorithms,
e.g., as discussed in FIG. 10, may be periodically evaluated, such
as in a step 48 of FIG. 4, which describes the use of a supervisory
algorithm. The supervisory algorithm may be the overall seizure
detection program running in the processor of a device in the
seizure detection system 10, such as the detection unit 12 or base
unit 14. Among other things, the supervisory algorithm may
determine whether a seizure is in process. The supervisory
algorithm may accomplish this by evaluating the conclusions of the
other sub-methods or algorithms that analyze EMG signal data, and
perhaps other data such as temperature or heart rate, as well. A
supervisory algorithm may convolute data in one or more registers
that correspond to seizure variables. For example, as discussed
above, a sub-method may, e.g., identify a specific characteristic
of data, calculate a certainty value, and increment a register
value. A supervisory algorithm may then take the register values
and multiply each value by a coefficient (e.g., from zero to one)
to give more weight to certain seizure variables, and then may add
all of the resultant products together. If the sum of the products
exceeds a threshold value, then a seizure may be declared as
detected, and an alert sent accordingly. For example, an example
would be TOTAL=a(register 1)+b(register 2)+ . . . z(register 26).
If TOTAL ever goes over the detection threshold, then a seizure
detection may be declared.
[0127] FIG. 14 illustrates one embodiment of a supervisory
algorithm 162. In a step 164, the supervisory algorithm may
periodically evaluate one or more of the detection and accumulation
registers. That is, the supervisory algorithm may determine the
value stored in such registers. In a step 166, the supervisory
algorithm may multiply, or convolute in some other manner, the
value in each register by an appropriate weighting coefficient.
Such weighting coefficients may, for example, be associated with a
template file. For example, table 1 indicates a coefficient that
may be used to adjust the value of the burst count accumulation
register. A sum of the values in accessed detection and
accumulation registers may be added together in a step 168. In a
step 170, the sum determined in step 168 may be compared to an
overall threshold. If the sum is larger than the threshold, then a
seizure alarm protocol may be initiated (step 172). In some
embodiments, a supervisory algorithm may evaluate the output of a
portion of the registers. For example, one or more registers may be
evaluated, convoluted with coefficients, compared to a threshold,
and if appropriate, an alarm protocol may be initiated. In some
embodiments, the coefficient by which one seizure variable is
modified may depend upon the value of another seizure variable. For
example, the system may learn that when two seizure variables are
simultaneously elevated, or related in some other way, that the
system may detect a seizure with higher confidence.
[0128] FIG. 14A illustrates another embodiment of a supervisory
algorithm. A supervisory algorithm may analyze the processed EMG
data with respect to a different seizure characteristics. A
supervisory algorithm may integrate or average over time its
results and continually update its conclusions. This may serve to
remove short glitches or spikes in the data that could lead to a
false positive. In the embodiment of FIG. 14A, the supervisory
algorithm uses register values from some of the foregoing
sub-algorithms as follows: [0129] Burst Train Detect flag and
Certainty value [0130] Periodicity good or bad and Certainty value
[0131] GTC waveform Detect and Certainty value In this embodiment,
each sub-algorithm could produce a flag indicating a detection, or,
in the case of the periodicity, a flag that votes against
detection. Each may have a coefficient or multiplier variable (A,
B, C, D) that establishes each sub-algorithm's importance or weight
in the overall determination of seizure declaration. As discussed
above, certainty values may range from 0 to 100%, with 100 being
the highest certainty. The supervisory algorithm uses the Certainty
Value to gauge confidence in the results of the Burst Detection
algorithm.
[0132] Generally, a Certainty value may be used by one algorithm to
transmit to another algorithm how certain the first algorithm was
in its judgment. For a burst detection algorithm, for example, one
metric may be the average SNR during the burst normalized to a max
value of 50. Another metric may be how closely the burst looks like
an ideal burst, e.g., through waveform regularity analysis. A burst
that is barely greater in width than the minimum may not rate as
high as one 5 times wider than the minimum. Also, a burst that is
too close to the maximum may given a lower certainty value. For
example, as suggested herein, a reference burst width could
originally come from empirical data from many test patients
experiencing actual seizures, and be a factory default. Later, as
data from the patient is gathered, a more representative ideal
width could be established for that patient. The rating of a burst
width could be normalized to a max value of 50 and added to the SNR
value for a maximum of 100. Other metrics could be factored in as
well and each could be weighted differently. One example of a
method of weighting would be to normalize each to a different
value:
TABLE-US-00004 SNR 40% Width 35% Amplitude 25%
A similar process for establishing certainty values could be
implemented for each sub-algorithm.
[0133] An equation that the supervisory algorithm could use to
quantify the decision process is:
Seizure_etection=A*(Burst_Train_Flag*Certainty)+B*(Periodicity_good_flag-
*Certainty_good)-C*(Peridodicity_bad_flag*Certainty_bad)+D(GTC_flag*Certai-
nty_value)
If the sum is greater than a Seizure Detection Threshold variable
value, then the supervisory algorithm declares a seizure. Other
seizure variables may be used, such as Seizure Length could be used
to specify how long (time in seconds) the seizure must be in
process before an alarm is generated. If the sum is less than a
Seizure Detection Threshold variable value, then the supervisory
algorithm may be inactive for a period of time before re-scanning
sub-method registers.
[0134] It can be seen from the above equations that if the
periodicity is good, it adds to the summation with one weight. If
the periodicity is bad, it subtracts from the summation with
another weight. This allows the periodicity algorithm to strongly
vote against a seizure detection if it determines that the EMG
signals include obvious interference such as harmonics from the
power mains, fluorescent lights, etc. Other inputs such as
temperature or heart rate could be added with their own
coefficients and certainty values. Sometimes heart rate can be
detected with EMG electrodes and thus would require no more
electrodes. However, dedicated electrodes for heart rate and
temperature may provide better signals with respect to those
phenomena.
[0135] An aspect of systems and methods described herein is that
they may be readily customized and adapted as more data regarding
general seizure characteristics for a patient, or patient
demographic, is collected. Such methods may use algorithms that may
have a set of routines, coefficients, or other values that may be
included in a modifiable template file. It may, in some
embodiments, also be useful that a detection system, e.g., a system
that is designed to quickly detect seizures, has an accurate log of
the data and also a log of the condition of a patient. That is, for
example, a detection system that has accurately logged the event it
is intended to detect and the detection data itself (and correlated
those events in time), may, as described below, be optimized.
[0136] To appreciate the concept of a template file and adaptive
aspects of systems described herein, reference may now be made to
FIGS. 15 and 16. FIG. 15 shows at a high level, a method 174 of
data collection. Such a method may be used to optimize the
detection of seizures. In method 174 an initial template file may
be generated or selected for an individual (step 176). Once a
template is generated or selected it may be added to computer
memory of a detection unit and/or a base station. An example of
some data that may be included in a template file was shown in
Table 1.
[0137] A number of approaches may be used for establishing an
initial template file. In some embodiments, a patient may be
monitored for a period of time in a hospital or other controlled
setting and data, such as data derived from EMG electrode outputs,
may be collected and correlated with the presence or absence of
seizures, i.e., general seizure characteristics for an individual
may be established. From that data, an operator or software may
generate an initial template file or select an appropriate file
from a list of pre-generated templates. In some embodiments, an
initial template file may be obtained using historical data from a
general patient demographic. For example, a patient may be defined
by various characteristics including, for example, any combination
of age, gender, ethnicity, weight, level of body fat, fat content
in the arms, fat content in the legs, fitness level, or the patient
may be defined by other characteristics. The patient's medical
history including, for example, history of having seizures, current
medications, or other factors may also be considered. Once a
template file is generated or selected it may be included in
computer memory within a detection unit and base unit and an
individual may use the detection unit in a home-setting.
[0138] In step 178 a patient while in a home-setting may collect
and process EMG output or other detector output, such as using a
detection unit. It should be noted, as indicated in FIG. 1, that a
detection unit may be in communication with a base unit,
transceiver and also with a data storage unit. Thus, any portion of
data may be collected, processed and also sent to a data archive.
In FIG. 15, the storage of detector data is illustrated in step
180. Any portion of the data, e.g., raw data or processed data may
be stored. In some embodiments, data may be converted to a model
form that allows one to access the data and determine how that data
would have behaved if analyzed in another algorithm. For example,
the noise value in periods between shaped data bursts may be stored
as value and may not include a point by point data file that
includes all fluctuations in the baseline. Bursts may themselves be
shaped and this pattern may be stored. In some embodiments, data
may be added to a storage archive and more than one different
template applied to that data. That is, the data may be analyzed
with any number of template files and the results of that analysis
stored for future review. In that light, the results of running
different pre-generated templates may be stored and not raw data or
other processed data. Of course, the results of running those
pre-generated templates may be evaluated and it may be determined,
e.g., after comparison of those results with data reflecting the
physical states of patients, that one template, i.e., a template
that was not used to monitor a patient, would have in fact detected
the patient's seizures in a preferred manner.
[0139] Adapting an algorithm to better detect seizures in an
individual patient or patient demographic may depend not only on
the organization of detector data but also upon corroborating
information, e.g., for any given portion of detector data, the
physical condition of the patient. That is, it may, in some
embodiments, be useful to document, along with EMG or other
detector data, a record of what actually occurred at certain points
in a data stream. Such information may, for example, be identified
by a caregiver, as indicated in step 182. A caregiver may also
provide such information to a data storage facility, which may
store the information (step 184). Alternatively, one caregiver may
provide such information to an operator who may execute an
optimization procedure. Information provided to data storage may
include, for example, whether a suspected seizure was verified to
be a seizure, whether a suspected seizure was in fact something
different, the location of the patient when an incident occurred,
severity of the seizure, time of the incident, any medical care
that may have been issued and other information as well. At least
some of this information may also be provided by the patient or
individual.
[0140] In addition, in some embodiments, a patient may also provide
information related to general seizure characteristics. For
example, a patient may receive an alert from the detector unit that
a seizure is in progress (step 186). An individual, if alert, and
aware that they are in fact not experiencing a seizure, may be
given the option of sending a message to a caregiver and/or to a
data storage unit that a false positive was alerted by the system.
In some embodiments, an individual may communicate the presence of
a false detection by simultaneously pressing two buttons on an
attached device, e.g., the detection unit or another unit. Of
course, the requirement that an individual simultaneously press two
buttons may minimize the risk that an inadvertent signal is sent.
Any other suitable approach to minimize inadvertent messages may
also be used. A message sent in this manner, e.g., sent to a
storage facility from a patient (step 188), may include a time
stamp to correlate a false positive event with the data which
initiated the false positive event. Such information may be stored
in a data storage facility (step 190)
[0141] An individual may, in some embodiments, also be given the
option to provide additional information, e.g., other information
that may be associated with any false positive event, or seizure
incident. Such supporting information may include an activity they
were engaged in or the physical location they were at when they
received notification that a seizure is in progress. Also, a
detector unit may, as previously described, be an input/output
device, and thus, a seizure alert may be sent to a detector unit,
or other unit carried or worn by a patient, from a base unit. That
is, if the base unit controls initiation of an alarm, the base
station may inform the detector unit (which is physically near the
patient) that a seizure has been detected. In some embodiments, a
device including means for reporting information, such as a false
positive event, to a caregiver or data storage facility may be worn
around the wrist or on the belt of a patient. An operator may
access data in a data storage facility and organize the information
192.
[0142] A method 194 of optimizing seizure detection, and updating a
template file, is shown in FIG. 16. In step 196 an operator may add
any new data, e.g., data collected in a home-setting for a patient,
to any previously stored data for that patient, i.e., an operator
may update a data file. Alternatively, an operator may add newly
collected data for a patient to a body of data that is associated
with a patient demographic. The system may in step 198, for
example, use the initial template file (or a currently used
template file for that patient), and characterize detection metrics
for the system as applied to the individual's updated data file.
Metrics of the system may include listing seizure events that were
correctly identified, seizure events that were missed, false
positives, and in some embodiments, a determination of the severity
of an event that was considered to be a seizure. Also, for any
given reported event, e.g., a seizure incident or false positive
detection, the operator may, in some embodiments, be provided with
a listing of the data in different registers at the time of the
event. Such information may, for example, be recalculated (during
optimization) from original signal data or from stored values. In a
step 200, the operator may execute a computer program to select
fields of information, e.g., weighting coefficients, thresholds,
criteria, and selected processing routines, from the initial
template file (or currently used template) and vary those fields.
The operator may also manually select and adjust one or more
fields. The system may characterize detection metrics (step 202)
while varying template fields and select new settings (step 204)
for an updated template file. Of course, the updated template file
may be downloaded to either or both of the detection unit and base
station.
[0143] One aspect of methods and apparatuses described herein is
that they are, in various embodiments, able to organize information
between a detection unit and base station or between those units
and a data archive. In addition, some embodiments may be used to
organize the collection of portions of data that are most
relevant.
[0144] In some embodiments, the rate at which data may be collected
may depend upon whether or not an electrode is in a given state,
such as an active state, resting state, or engaged in a polling
operation. For example, FIG. 17 illustrates one embodiment of a
method 206 of detecting seizures in which the rate of data
collection depends upon the state of an electrode. Method 206 may,
for example, be used to toggle a detection unit and/or base station
between a "sleep" mode, i.e., characterized by operations within
dashed line 208, and a mode of substantially continuous operation,
such as active state 214. As shown in FIG. 17, a detector and/or
base unit may be configured to exist in the resting state 200 for a
portion of time while in a "sleep mode." While in the resting state
210 a detector or base unit may be silent, e.g., it may not be
monitoring or collecting data from a patient. The resting state may
include instructions to periodically exit the resting state 210
and, for example, collect detector data for a period of time. That
is, a detector may enter a polling operation step 212 where data is
collected. The duration of an individual polling operation may be
sufficient to collect data as needed to make a decision regarding
the state of an electrode. That is, for example, based on data
collected during polling step 212 a detector may revert back to the
resting state 210 or may enter another state, such as active state
214.
[0145] Any of various routines may be used to collect data for
toggling between a resting and active state. An amplitude detection
algorithm may, for example, be used to switch an electrode between
a resting and active state. FIG. 18 illustrates one embodiment of
an amplitude detection algorithm 216. An EMG signal amplitude may
be, for example, a peak value, a mean value, a median value, an
integrated value, or other value that may be measured at a given
time point or over a selected time interval. EMG signal amplitude
may be normalized or calibrated for a patient's baseline activity.
As shown in FIG. 18, in a step 218 one or more electrodes in a
resting state may "wake up" and measure the EMG signal amplitude.
For example, as illustrated in step 220, if the amplitude is above
a threshold level, then the one or more electrodes may continue to
measure the EMG signal amplitude and if the threshold level is not
obtained, the one or more electrodes may return to a resting state.
By having a period of time in which a detection unit is in "sleep"
mode, a system may conserve battery life, minimize the amount of
data that is stored in memory, minimize the amount of data that is
transferred over a network, or serve other functions. In some
embodiments, a decision to enter an active state, and monitor a
patient in a more continuous manner, may be made based on factors
in addition to amplitude detection.
[0146] Additional embodiments that may be used to allocate data
collection among devices are shown in FIGS. 19 and 20. In the
embodiment of FIG. 19, an EMG electrode in a detection unit detects
an EMG signal, determines the spectral content of the signal, and
may compare the spectral content to a model GTC waveform stored in
the detection unit's memory. If the spectral content is
substantially similar to the GTC waveform, then the detector unit
may send approximately ten seconds-worth of EMG signal to the base
station. Preferably, the sent EMG signal includes the signal that
formed the basis of the comparison. The base station may
independently determine the spectral content of the received
signal, and compare the spectral content to the GTC waveform stored
at the base station. If the spectral content is substantially
similar to the GTC waveform, then the base station may send an
alert to a remote station or caregiver. Thus, in one embodiment,
for an alert to be sent, both the detection unit and base station
must each determine that the spectral content of the EMG signal is
substantially similar to the GTC waveform.
[0147] In the embodiment of FIG. 20, an EMG electrode in a
detection unit detects an EMG signal, determines the spectral
content of the signal, and compares the spectral content to the GTC
waveform stored in the detection unit. If the spectral content is
substantially similar to the GTC waveform, then the detector unit
may send approximately ten seconds-worth of EMG signal to the base
station. Preferably, the sent EMG signal includes the signal that
formed the basis of the comparison. The base station may
independently determine the spectral content of the received
signal, and compare the spectral content to the GTC waveform stored
at the base station. The base station may also analyze the received
signal for burst activity, as described above, such as regular
periodicity, to determine if burst thresholds are met. If the
spectral content is substantially similar to the GTC waveform, and
the base station recognizes burst activity that meets the burst
thresholds, then the base station may send an alert to a remote
station or caregiver.
[0148] Similarly, processing of EMG signal data for various seizure
variable values may be accomplished at the detection unit, at the
base station, or both, depending on processor existence and
capability, and storage capacity.
[0149] Some additional processing techniques that may be used in
the above algorithms or in other sub-methods are described below.
For example, in some embodiments, a register may be populated in a
manner such the level, or value of the contents, of the register is
related to the time that a seizure variable may be above threshold,
related to the magnitude of a certain characteristic of data, e.g.,
seizure variable, or both. For example, a register may be loaded
with a set numerical value every X seconds that a certain
characteristic is maintained above a threshold. Thus, if a given
number of time periods, e.g., nX seconds, are maintained with the
characteristic above threshold, the method may advocate a seizure
detection. If the characteristic drops below threshold, the
register may be reset or decremented in some manner. In such an
embodiment, an alarm may be triggered based on the number of time
periods that a certain characteristic is above threshold. A
register (e.g., a first register) may also be loaded with a
numerical value every X seconds that a certain characteristic is
above a threshold, and that numerical value may be proportional to
the magnitude of signal or number of events detected over the
provided time period. At the completion of every X seconds, a
second register may be populated in a manner that depends upon the
first register, e.g., whether it is maintained above a certain
level. In such an embodiment, an alarm may be triggered, for
example, if the second register is populated for a certain number
of consecutive time periods. The first register may, in some
embodiments decrement at a certain rate. For example, the first
register may be loaded every X seconds in a manner proportional to
the magnitude or number of registered events and also decremented
each X second period. Thus, the first register may either increase
in value or decrease in value as dependent upon how it is
incremented or decremented. In some embodiments, an alarm may be
triggered if either the second register exceeds a certain
threshold, if the first register exceeds threshold, or if either or
both exceeds a certain threshold. If a characteristic evaluated is
of a type where an integration calculation is needed, then the
method may increment the register a specific amount every X
seconds. If the register is set to decay more slowly than the rate
of increment, then the register value will increase over time. A
slower rate of increase may allow the method to slowly build up to
a higher confidence level of seizure detection.
[0150] In some embodiments, an EMG electrode in a detection unit
may detect an EMG signal, determine the spectral content of the
signal, and compare the spectral content to the GTC waveform stored
in the detection unit. If the spectral content is substantially
similar to the GTC waveform, then the detector unit may send an
alert to the base station, a remote station, and or caregiver. The
detector unit may send the alert without requiring corroborative
analysis by the base station. In yet other embodiments, the
detector unit may further analyze the EMG signal for seizure burst
activity, as described above, such as regular periodicity, to
determine if burst thresholds are met. If the spectral content is
substantially similar to the GTC waveform, and the detector unit
recognizes burst activity that meets the burst thresholds, then the
detector unit may send an alert to a base station, a remote station
and/or caregiver.
[0151] In some embodiments, the seizure detection system may be
provided with a generalized GTC waveform and calibrated for a
patient's baseline activity, e.g., sleeping, daytime activity, etc.
When waveform activity increases, the seizure detection system may
compare the signals collected by the detection unit to the
generalized GTC waveform. The seizure detection system may begin to
characterize the signals and look for elevated signal amplitudes.
The seizure detection system may process the signals to generate
spectral content by well understood methods such as Fast-Fourier
Transform (FFT). The seizure detection system may apply filtering
to more clearly reveal higher-frequency "bursts." The seizure
detection system may determine if the processed signal fits the
generalized seizure characteristics by measuring one or more of the
factors of amplitude, count, time length of train, and periodicity
of bursts and comparing those factors against stored patterns and
thresholds. If the thresholds are exceeded, then an alarm may be
sent, e.g., to the base station together with data. The base
station may separately process the data for verification of the
alarm condition. If the base station agrees with the alarm, then
the base station may generate an alarm to remote devices and local
sound generators. An alarm may comprise an audible signal, or a
text message, or email, or trigger vibration in a PDA, or other
suitable attention-getting mechanisms. In some embodiments, having
the base station agree to the detection unit's alarm introduces a
voting mechanism for reducing false alarms. Both devices must vote
on the decision and agree to sound the alarm. This may be used to
limit false alarms. Of course, a processor in a patient-mounted
unit may process the EMG signals based on burst detection, and may
separately process the EMG signals based on GTC waveform, and may
send an alert if both processes indicate that an alarm protocol
should be initiated. Thus, voting may occur within a device, as
well.
[0152] In some embodiments, during or after a seizure event, a
human operator may review and adjust thresholds based upon the
severity of the seizure or possibly the non-detection of an actual
seizure because of high thresholds. Many people have seizures and
do not realize that they had a seizure, e.g., the short-lived
seizures discussed above. Having this data to review may help
medically manage the person with seizures. Also, a human operator
may evaluate the data and conclude that a seizure did not occur,
and either cancel the alarm or instruct the seizure detection
system that the detected waveform did not indicate a seizure.
Likewise, a human operator may instruct the seizure detection
system that an undetected seizure had occurred by, e.g., specifying
the time during which the seizure occurred. For example, the graphs
in the figures discussed above may comprise a rolling "window" of
EMG activity, and the human operator may "rewind" the recorded
signal and indicate to the seizure detection system the time window
in which the seizure occurred. In some embodiments, the base unit
may include a visual display that allows display of EMG signals in
time and spectral domain to allow a caregiver to view historical
seizure data. In some embodiments, the base station may visually
depict the signal and provide a graphic user interface (GUI) that
allows human operators to accomplish the "window" selection and
define other operating thresholds and conditions. For example, the
system 10 of FIG. 1 may include a video camera that records the
patient while sleeping to allow a caregiver to review the EMG
signal in coordination with video footage to assess a patient's
condition corresponding that EMG signal. Thus, video data may be
stored along with EMG signal data, and reviewed, for example, on
the base station GUI along with the EMG signal graphs. In other
words, the base station could allow a caregiver to view EMG signal
graphs and corresponding video data side-by-side. The seizure
detection system may thus have additional data points against which
to evaluate future seizure events for that particular patient. The
seizure detection system may employs adaptively intelligent
software to "learn" the patient's seizure patterns, and over time
effectively customize the generalized GTC waveform to better detect
seizures in that patient.
[0153] An apparatus for detecting seizures is preferably
man-portable, and may include a detection unit that may be attached
to the body, such as by use of an elastic arm band. The detection
unit may be battery powered, and may wirelessly communicate with
the base station. The detection unit may include sufficient data
storage, processing and transmission capability to receive, buffer,
process and transmit signals. The detection unit may process the
signals and conduct a simplified comparison, e.g., using two
factors of amplitude and frequency, with the generalized seizure
detection requirements stored in the detection unit. When the
detection unit determines that a seizure is occurring, it can
download both its analysis and the raw signal data to a bedside
base station for more complex processing. The base station may have
much more power, larger storage capability and greater processing
speed and power, and be better able overall to process the
information. It could have a larger database of patterns to compare
against. As the seizure detection system "learns" the patient's
patterns, the base station may modify the generalized seizure
detection requirements to more closely model the patient's pattern.
The base station may update the detection device periodically with
the modified generalized seizure detection requirements. Likewise,
the base station may transmit raw and processed signal data to a
remote computer for further analysis and aggregation with signal
data from other units in use. For example, multiple base stations
may transmit data for multiple patients to a remote computer. Each
base station may not receive the other base station's data, but the
remote computer may serve as a common repository for data.
Aggregation of the data may allow further data points upon which to
further refine the generalized seizure detection requirements,
thresholds and statistical information that may be supplied to base
stations and detection units as a factory default.
[0154] As previously noted, in some embodiments, in addition to
using EMG, electrocardiography (ECG) may be used to corroborate (or
contradict) the occurrence of a seizure. This option could be used
with particularly difficult patients. Patients with an excessive
amount of loose skin or high concentrations of adipose tissue may
be particularly difficult to monitor. For example, a factor
associated with reliable EMG measurements, is the stability of the
contact between the electrodes and skin. For some patients this may
be difficult to achieve in a reliable manner ECG data may be
included in a method for determining a likelihood of whether a
seizure related incident is taking place (or has taken place) and
ECG data may be used to determine whether a seizure should be
declared, e.g., an alarm initiated. Moreover, skin and fat are
inherently a type of frequency filter.
[0155] Heart rate may, for example, elevate during a seizure, e.g.,
a patient may become tachycardic. As discussed further herein, if
the EMG processing portion of the seizure detection apparatus
determines that a seizure may be in progress and the heart rate
does not go up, then the confidence of the detection may be
reduced. For example, epileptic patients that use a beta blocker
drug may not experience a rise in heart rate. In such situations, a
method incorporating heart rate as a factor may be provided with a
coefficient to lower the weight given to that factor. Thus, the
disclosed detection method and apparatus may be adjusted or readily
customized according to patient-specific considerations, such as
use of a particular drug regimen. In some embodiments, ECG may be
used to detect other cardiac dysrhythmia, such as bradycardia or
asystole following a seizure, and to send an alarm if such a
condition is detected. Data from a temperature sensor situated as
to detect patient temperature may also be used to corroborate
occurrence of a seizure or to initiate an alarm.
[0156] Generally, the devices of a seizure detection system may be
of any suitable type and configuration to accomplish one or more of
the methods and goals disclosed herein. For example, a server may
comprise one or more computers or programs that respond to commands
or requests from one or more other computers or programs, or
clients. The client devices, may comprise one or more computers or
programs that issue commands or requests for service provided by
one or more other computers or programs, or servers. The various
devices in FIG. 1, e.g., 12, 13, 14, 16, 17, 18 and/or 19, may be
servers or clients depending on their function and configuration.
Servers and/or clients may variously be or reside on, for example,
mainframe computers, desktop computers, PDAs, smartphones (such as
Apple's iPhone.TM., Motorola's Atrix.TM. 4G, and Research In
Motion's Blackberry.TM. devices), tablets, netbooks, portable
computers, portable media players with network communication
capabilities (such as Microsoft's Zune HD.TM. and Apple's iPod
Touch.TM. devices), cameras with network communication
capabilities, wearable computers, and the like.
[0157] A computer may be any device capable of accepting input,
processing the input according to a program, and producing output.
A computer may comprise, for example, a processor, memory and
network connection capability. Computers may be of a variety of
classes, such as supercomputers, mainframes, workstations,
microcomputers, PDAs and smartphones, according to the computer's
size, speed, cost and abilities. Computers may be stationary or
portable, and may be programmed for a variety of functions, such as
cellular telephony, media recordation and playback, data transfer,
web browsing, data processing, data query, process automation,
video conferencing, artificial intelligence, and much more.
[0158] A program may comprise any sequence of instructions, such as
an algorithm, whether in a form that can be executed by a computer
(object code), in a form that can be read by humans (source code),
or otherwise. A program may comprise or call one or more data
structures and variables. A program may be embodied in hardware or
software, or a combination thereof. A program may be created using
any suitable programming language, such as C, C++, Java, Perl, PHP,
Ruby, SQL, and others. Computer software may comprise one or more
programs and related data. Examples of computer software include
system software (such as operating system software, device drivers
and utilities), middleware (such as web servers, data access
software and enterprise messaging software), application software
(such as databases, video games and media players), firmware (such
as device specific software installed on calculators, keyboards and
mobile phones), and programming tools (such as debuggers, compilers
and text editors).
[0159] Memory may comprise any computer-readable medium in which
information can be temporarily or permanently stored and retrieved.
Examples of memory include various types of RAM and ROM, such as
SRAM, DRAM, Z-RAM, flash, optical disks, magnetic tape, punch
cards, EEPROM. Memory may be virtualized, and may be provided in,
or across one or more devices and/or geographic locations, such as
RAID technology.
[0160] An I/O device may comprise any hardware that can be used to
provide information to and/or receive information from a computer.
Exemplary I/O devices include disk drives, keyboards, video display
screens, mouse pointers, printers, card readers, scanners (such as
barcode, fingerprint, iris, QR code, and other types of scanners),
RFID devices, tape drives, touch screens, cameras, movement
sensors, network cards, storage devices, microphones, audio
speakers, styli and transducers, and associated interfaces and
drivers.
[0161] A network may comprise a cellular network, the Internet,
intranet, local area network (LAN), wide area network (WAN),
Metropolitan Area Network (MAN), other types of area networks,
cable television network, satellite network, telephone network,
public networks, private networks, wired or wireless networks,
virtual, switched, routed, fully connected, and any combination and
subnetwork thereof. The network may use a variety of network
devices, such as routers, bridges, switches, hubs, repeaters,
converters, receivers, proxies, firewalls, translators and the
like. Network connections may be wired or wireless, and may use
multiplexers, network interface cards, modems, IDSN terminal
adapters, line drivers, and the like. The network may comprise any
suitable topology, such as point-to-point, bus, star, tree, mesh,
ring and any combination or hybrid thereof.
[0162] Wireless technology may take many forms such as
person-to-person wireless, person-to-stationary receiving device,
person-to-a-remote alerting device using one or more of the
available wireless technology such as ISM band devices, WiFi,
Bluetooth, cell phone SMS, cellular (CDMA2000, WCDMA, etc.), WiMAX,
WLAN, and the like.
[0163] Communication in and among computers, I/O devices and
network devices may be accomplished using a variety of protocols.
Protocols may include, for example, signaling, error detection and
correction, data formatting and address mapping. For example,
protocols may be provided according to the seven-layer Open Systems
Interconnection model (OSI model), or the TCP/IP model.
[0164] Although the foregoing specific details describe certain
embodiments of this invention, persons reasonably skilled in the
art will recognize that various changes may be made in the details
of this invention without departing from the spirit and scope of
the invention as defined in the appended claims and considering the
doctrine of equivalents. Therefore, it should be understood that
this invention is not to be limited to the specific details shown
and described herein.
[0165] Additional information related to the methods and apparatus
herein described may be understood in connection with the examples
provided below.
EXAMPLES
Example 1
[0166] In one example, a patient who may be susceptible to having
seizures may be monitored. The patient may, for example, be
monitored during a period immediately following a hospitalization,
or at some other time where they are at risk for SUDEP. It may be
useful to set up the monitoring protocol for the patient, based at
least in part, upon data obtained for the patient while the patient
is monitored for seizures in a controlled setting. For example,
during hospitalization the patient may be monitored and data may be
collected for determining general seizure characteristics. The
patient may, for example, be monitored with EMG over a period of
several days, or some other interval, as necessary to collect data
associated with a statistically significant number of seizures.
During the period of hospitalization, the patient EMG data may be
collected by placing bipolar differential electrodes on or near one
or more pairs of muscles, e.g., agonist and antagonist muscle
pairs. EMG data may, for example, be collected from a first group
of muscles, e.g., the biceps and triceps, and a second group of
muscles, e.g., the hamstrings and quadriceps. EMG data from time
periods with known seizures and also intervals with non-seizure
periods may be collected, archived and an operator may analyze the
data.
[0167] An operator may analyze the data and characterize how the
patient data relates to a seizure variable, including, for example,
seizure variables characteristic of a burst. An operator may, for
example, measure the amplitude, width, and determine the signal to
noise (S/N) ratio for portions of data that are elevated, i.e.,
periods that may be characterized as data bursts. Signal to noise
calculations may involve, establishing a baseline by determining
fluctuations in detector signal, i.e., baseline noise, in a time
period immediately prior to data in a time suspected of containing
bursts. Various filters may be applied to the data, e.g., digitized
data may be subjected to a 3rd order Butterworth filter from 300 Hz
to 500 Hz or filtered in another manner. Using data that is
filtered, the operator may, for example, repeat measurement of
amplitude, width, and signal to noise (S/N) ratio for data at times
that appears to contain data bursts. The operator may then select
threshold values associated with burst measurements. Alternatively,
an operator may opt to use threshold values typical for all
patients or patients of a certain demographic.
[0168] Similarly, an operator may, for example, determine the
frequency position of local minimum values and local maximum values
of power density for the spectral data. For example, data from a
certain time window, such as five seconds, may be collected and
converted to spectral data (in the frequency domain). The operator
may determine local maximum and minimum values and specify a range
of frequencies on either side of the local maximum value and local
minimum value and an algorithm may calculate the area under the
power density/frequency curves. The ratio of these areas may be
used as the value of a seizure variable, e.g., a slump to bump
ratio. A threshold value for the slump to bump ratio may be
specified by the operator or selected from a template file for all
patients, or patients of a certain demographic.
[0169] An operator may import archived data, i.e., data from
periods collected in which a seizure was present and other
non-seizure periods, into a computer program using the selected
threshold values and instructions for executing an algorithm. The
algorithm may, for a given time window, e.g., 5 seconds, calculate
values of burst related seizure variables. For example, for any
time period, software may detect possible bursts, and may also
measure amplitude, width and S/N. If bursts meet the criteria
established, e.g., are within the set thresholds, the computer may
populate a value in a burst detection register. To clarify the flow
of data in the algorithm, model data from Example 1 may be
referenced to FIG. 21. FIG. 21 shows how model data in a procedure
(270) for analysis of data bursts may be organized, and how data
may be transferred between a detection register in computer memory
and an accumulation register, also in computer memory. In a first
interval of time (271), data may be analyzed, and for example, it
may be determined that three events meet threshold requirements for
characterization as bursts. In a step (272) data may be transferred
to a detection register. The detection register (273) in FIG. 21 is
represented by dashed line (273), and the flow of information
within the detection register (273) is represented by blocks (274,
275, 277, and 278), which represent the detection register (273) in
different states. As data is transferred in step (272), the
detection register in a state (274), i.e., storing a data value of
zero, may become populated with a value of three, as shown in state
(275). In a step (279), the data value stored in the detect
register (273) may be transferred to an accumulation register
(280). In FIG. 21 the burst train accumulation register (280) is
represented by dashed line (280), and the flow of information
within the burst train accumulation register (280) is represented
by blocks (281, 282, 284, and 285), which represent the
accumulation register in different states. In step (279), the
accumulation register in a state (281), i.e., a store storing a
data value of zero, may become populated with a value of three, as
reflected in state (282). Referring back to the detection register
(273), upon transferring contents to the accumulation register
(280), in step (276), the detection register (273) may clear its
contents, as reflected in state (277). As reflected in step (286),
in a second interval of time (286), another interval of data may be
analyzed, and for example, it may be determined that five events
meet threshold requirements and are characterized as bursts. In
step (287), the detect register (273), now in state (277) may
receive data associated with the measured burst value from step
(287), i.e., a value of five. The detect register (273) may now
hold a data value of five, as shown by state (278). Prior to
transfer of data from the detect register, i.e., in state (278) to
the burst train accumulation register (280), the burst train
accumulation register (280), may be subjected to an adjust
accumulation register step (283). That is, in step (283) the burst
train accumulation register may be adjusted in value. For example,
as illustrated in Example 1, the accumulation register is shown to
"leak" a value of one during the adjust accumulation register step
(283). Thus, if step 283 denotes a leakage value of one and if a
greater number of bursts are detected in successive time intervals,
e.g., steps (271) and (286), then the accumulation register will
increase in value. For example, as shown in Example 1, in step
(288), the detect register (273) transfers its contents to the
burst train accumulation register (280), while the burst train
accumulation register is in state (284), and a value content of
five is transferred to the burst train accumulation register (280).
The accumulation register would then hold a data value of seven, as
shown for state (285).
[0170] In addition to the steps above, an algorithm may also
involve other registers, e.g., a GTC accumulation register. For
example, as described in relation to FIG. 22, a GTC accumulation
register (290) may be populated. Thus, it should be appreciated
that at any point in time, the burst train accumulation register
(280) and the GTC accumulation register (290) may hold a value. A
supervisory algorithm (162), may be used to analyze the data in
those registers (285) and (290). To clarify the flow of data in
Example 1, reference is now made to FIG. 22, as well as the general
description of supervisory algorithms in FIG. 14.
[0171] As shown in FIG. 22, and using illustrative data for this
Example 1, the GTC accumulation register (290) is shown to have a
value of five. The burst train accumulation detection register
(280) is shown to be in a state (285), and as noted previously,
holds a value of seven. In a step 291 of the supervisory algorithm,
the values of the registers are multiplied by a coefficient. That
coefficient may be pulled from a template file and used as a
weighting factor for associated seizure variables. That is, as
shown in Example 1, a GTC weighting coefficient (298) may be 1.5
and a burst coefficient (299) may have a value of 1.0. The,
weighted value of the two seizure variables following
multiplication with their associated coefficients may then be 7.5
(292) and 7 (293). In a step (294) those values may be added
together, and as shown in FIG. 22, a sum value, e.g., 14.5, may
become associated with a supervisory register (295). In a step
(296) the value of held in the supervisory register (295) may be
compared to a threshold value. For example, a threshold value for
reporting a seizure may be 14, and thus, an alarm protocol would be
triggered
[0172] In Example 1, the data that is input into the algorithm is
historical data from a patient's time in the hospital. Thus, the
operator may in step (297) compare the results determined by the
algorithm to the actual state of the patient at the time that the
data was collected. That is, an operator may compare the result
that would have been initiated with the actual course that was
appropriate. An operator may thus compare, for all of the data that
is available, how accurately the algorithm detects actual seizures
and whether the algorithm would have detected any false positives,
e.g., decisions to declare an alarm when the proper course of
action was to not report a seizure incident.
[0173] The computer program may allow the operator to manually
adjust coefficients, including for example threshold values for
burst or GTC waveform detection (such as slump to bump), GTC
coefficient (298), burst coefficient (299), or combinations
thereof. The program may be set to automatically adjust any
combination of the aforementioned coefficients in an optimization
routine, wherein the computer may modify the coefficients and look
for an ideal combination that provides both accurately detects
seizures and also minimizes false positive detections.
[0174] The patient in Example 1 may be sent home and monitored with
a configuration of EMG electrodes that closely resembles the
configuration of EMG electrodes used to optimize the detection
algorithm. As the patient is monitored, data may be collected and
the presence of any detected seizures, missed seizures (if
present), and false positives may be reported. The system may
periodically analyze the available archived data, including any
archived data derived while the patient is at home, and re-optimize
a combination of coefficients. Thus, the system may adapt to better
monitor a given patient over time.
Example 2
[0175] In this Example 2, a patient may be set up to be monitored
in a home setting using a pair of EMG electrodes on the biceps and
triceps. The patient may be set up to be monitored based on a
template file for patients that share a demographic with the
patient. In Example 2, the patient may be an obese male and an
initial set of coefficients and thresholds may be used to monitor
the patient based on a set of coefficients and thresholds optimized
for the entire set of data from all obese males for which data is
available. As distinguished, from Example 1, the patient in this
example may be monitored without previous evaluation in a hospital
setting. That is, the patient may be monitored with weighting
coefficients derived entirely by importing values associated with
other patients, e.g., patients that share characteristics with the
patient. The patient in Example 2 may be monitored for several
weeks and the system may record electrode data. For the model data
in Example 2, the system may accurately detect five seizure events
but miss one seizure event. The system may then be optimized with
archived data from the patient. That is, data from the patient may
be used to adjust coefficients to improve the accuracy of detecting
all events.
Example 3
[0176] In FIG. 23, the top trace labeled "EMG1-raw" shows EMG
electrical activity using a bipolar EMG electrode arrangement. The
trace labeled "EMG2-raw" is from a similar bipolar electrode
arrangement (differential electrode) on the triceps of the same
arm. The vertical scale in the FIG. 23 graphs, EMG1-raw and
EMG2-raw, is signal amplitude, e.g., the differential signal
between either the pair of EMG electrode inputs on the biceps or
the differential signal between the pair of EMG electrode inputs on
the triceps, and the horizontal scale shows time (in FIG. 23, the
time window is approximately 4 h28'55'' to approximately 4
h29'00''). FIG. 23 shows the collection of 5 seconds worth of
patient data. In some embodiments, data may be collected over some
other time period. Attachment of EMG electrodes on opposing muscle
groups, e.g., such as the biceps and triceps, may be beneficial for
several reasons. For example, as further discussed below, an
electrode configuration that involves opposing muscles may be
useful in the interpretation of data wherein a patient is involved
in certain activities, e.g., non-seizure motion, and
differentiation of data collected while the patient is engaged in
such activity from electrode data collected while the patient is
experiencing a seizure.
[0177] Still referring to FIG. 23, the bottom left graph (labeled
"EMG1 Spectral Analysis") is a representation of the frequencies of
data collected from the EMG electrode over the biceps (spectral
content). The bottom right graph (labeled "EMG2 Spectral Analysis")
is a representation of the frequencies of the triceps EMG
electrode. Data collected over a given time period, i.e., time
domain electrode data, may be converted to frequency data, i.e.,
spectral content, using techniques such as Fast-Fourier Transform
(FFT). For the spectral data, the horizontal scale is signal
frequency, and the vertical scale is the signal amplitude, which
for the spectral data described herein may be referred to as the
spectral density. Note that the spectral data in FIG. 23 indicates
a curving slope with decreasing amplitude as the frequency
increases, i.e., the spectral density generally decreases as the
frequency increases. The ratio of spectral density at low
frequencies to the spectral density at higher frequencies is a
seizure variable that, for any given set of electrode data, may
have an associated value. For example, for the data shown in FIG.
23 the ratio of spectral density at a frequency of about 200 Hz
(298) to the spectral density at about 400 Hz (300) may have a
value of about 5.0. The ratio of spectral densities at those
frequencies, or at other frequencies, may be a seizure variable and
the value of that seizure variable, such as derived from data in
FIG. 23, may be generally characteristic of non-seizure muscle
activity, such as moving in bed or moving arms. In some cases, such
as in FIG. 6, where the ratio at 200 Hz to 400 Hz is lower, such a
ratio may be indicative of seizure activity.
Example 4
[0178] FIG. 24 provides a spectral graph of EMG signals at a
different window of time than those of FIG. 23, namely, from
approximately 4 h39'30'' to approximately 4 h39'35'' when the
patient is again non-seizure moving. The spectral graph shows a
high spectral density across a wide group of frequencies in the
frequency band. Some normal voluntary muscle movement is a
coordinated contraction of agonist and antagonistic muscles in a
cooperative way to achieve a particular motion. In contrast to FIG.
23, and to illustrate the coordination of different muscle groups,
in FIG. 24, the data in "EMG1-raw" and the data in "EMG2-raw" are
from different electrodes associated with an agonist and antagonist
muscle group, i.e., data from those muscles are superimposed upon
each other. In some embodiments, the coordination of signals
between electrodes on agonist and antagonist muscles may be used as
a negative weighting factor for detection of a seizure. Often
during seizures this coordination is lost. Instead, the muscles
tend to lock up with muscles fighting each other. A good example of
a scenario wherein coordination of agonist and antagonist muscles
is lost may be seen in the tonic phase of a motor seizure when the
biceps and triceps muscles are both stimulated. These muscles will
fight each other with very high amplitude signals but the arms may
not move much at all. That is, data traces from different
electrodes where a phase relationship is maintained for some period
of time may be evidence that an individual is not experiencing a
seizure.
Example 5
[0179] The data shown in FIGS. 25-27 are collectively indicative of
how electrode data may change as patients transition from a
non-seizure state to the experiencing of an actual seizure. FIG. 25
shows a relatively quiet time (from time approximately 7 h20'40''
to approximately 7 h20'45'') of EMG signals obtained during sleep
just prior to a seizure. The spectral graph shows only relatively
low frequency activity. The amplitude of electrode data at the far
right of the time domain graph (later times), e.g., the amplitude
at a point (304), is increased over data illustrated at earlier
times, e.g., the amplitude at a point (302). That is, the amplitude
of electrode data is increasing as the seizure approaches. In some
embodiments, achieving a signal amplitude may trigger a change in
state for an EMG electrode or initiate transfer of data between a
detector and base unit and/or data storage unit. Changing states
for detectors from sleep to active is discussed above. Achieving an
amplitude at a point (304), or achieving such an amplitude with a
certain frequency for data points over a certain period, e.g., such
as a one second interval (306), may be used as a criteria that
initiates the transfer of data between a detection unit and base
unit and/or data archive.
[0180] FIG. 26 shows the EMG signals recorded during sleep at the
onset of a seizure (showing time approximately 7 h21'00'' to
approximately 7 h21'05''). The two lower spectral graphs ("EMG 1
Spectral Analysis" and "EMG2 Spectral Analysis") show a minor
"bump" (308) (with poor signal to noise) in the spectral display at
the higher frequencies, between approximately 350-450 Hz, and a
minor "slump" (310) in the spectral display at lower frequencies,
between about 250-350 Hz. In brief, the data in FIG. 4 shows the
beginning structure of a "GTC waveform," which is shown in FIG. 5
more clearly. However, at first, during a seizure, electrode data
derived from muscles, e.g., muscles whose activity is in a process
of building up, during a seizure may show the "GTC waveform" only
poorly (if at all), and while the spectral density is greater at
higher frequencies than typically seen for non-seizure data, such
data, at the start of a seizure, may seem random or show only minor
variations in spectral density across high frequency regions. Some
electrical signals associated with normal voluntary muscle
activity, recorded with macro-electrodes are almost entirely below
300 Hz. However, electrical frequencies recorded with
macro-electrodes frequently extend above 300 Hz in a sustained
manner during a seizure with motor manifestations. In some
embodiments, the duration of time in which a threshold spectral
density is achieved, e.g., at some high frequency, may be a seizure
variable.
[0181] FIG. 27 shows the evolution of the EMG signals as the
seizure progresses (showing time approximately 7 h21'20'' to
approximately 7 h21'25''). As may be seen in the bottom right
spectral graph, which corresponds to the triceps electrode, the
characteristic GTC waveform shows a region of elevated spectral
density, i.e., a relatively high-frequency "bump" between
approximately 300-500 Hz, and particularly around 400 Hz. That is,
the spectral density at a point (312) in that region is elevated
above the spectral density (314), e.g., within a "slumped" region,
approximately located within a range of about 250 Hz to 350 Hz. The
ratio of spectral density at the point (312) to the spectral
density at the point (314), or slump to bump ratio, may be used as
a seizure variable. In comparison of the spectral graph in FIGS. 26
and 27 it should be noted that as the patient begins to transition
into a seizure that the GTC waveform changes. For example, a
measureable slump to bump ratio becomes present in FIG. 27. As the
ratio becomes measureable, a GTC detection register may become
populated with an increasing value. If the GTC detection register
becomes populated with a value that is greater than the leakage
rate of the GTC accumulation register the value in the GTC
accumulation register may increase over successive time
periods.
[0182] In some embodiments, the slump to bump ratio may be used as
a metric for detection of a GTC waveform. However, more advanced
data analysis techniques, e.g., looking at a greater number of data
points and/or advanced pattern recognition algorithms, may also be
used to identify a GTC waveform. For example, in some embodiments a
detection unit may include instructions for calculation of a slump
to bump ratio and a base unit may calculate a slump to ratio and
also corroborate the slump to bump calculation with more advanced
pattern recognition analyses.
[0183] For this patient, the EMG data bursts have significant
noise, i.e., large statistical fluctuations, at time points between
them. Other patients may have less noise, resulting in GTC
waveforms that are more clearly visible, and slump to bump ratios
with greater signal to noise. A variety of analysis techniques may
be used to improve the signal to noise for detection of a GTC
waveform and/or slump to bump ratio. For example, in some
embodiments, spectral data over a certain frequency range may be
integrated, e.g., the area of the spectral curve within a frequency
range of a "bump region" may be calculated. Also, the area of the
curve within a frequency range of a "slump region" may be
calculated. The specific ranges for slump to bump used for
integration may be optimized for a given patient. That is,
historical electrode data may be accessed from a data repository,
different ranges for the slump region and/or the bump region may be
selected, and different values for the slump to bump calculated for
each selected ranges. Some slump to bump ratios, e.g., selected
with some ranges, may show better S/N ratios and/or better
correlation with the presence of a seizure than a slump to bump
calculated with other ranges. That is, general seizure
characteristics for the slump to bump ratio using frequency data in
one range may prove to be more useful, i.e., show better
correlation with the presence of a seizure, than a slump to bump
ratio using another frequency range. Thus, a slump to bump seizure
variable may be optimized for a given patient and may be updated
periodically as historical data is collected for the patient.
[0184] In some embodiments, data in a predetermined frequency
range, e.g., a range for a patient that typically shows a slump,
may be smoothed and the local minimum in the data established. The
area under a curve approximately centered on the local minimum may
be calculated. Similarly, the algorithm may analyze data in another
predetermined frequency range, e.g., a range for a patient that
typically shows a bump. Data in that range may be smoothed, a local
maximum established, and the area under the curve approximately
centered on the local maximum may be calculated. The area under the
local minimum, area under the local maximum, and ratio of those
integrals may be used as seizure variables. In some embodiments, a
detector unit may perform a calculation of the slump to bump ratio
for a given portion of electrode data and a base station may
perform more advanced pattern recognition techniques on the
electrode data.
Example 6
[0185] In Example 6, and associated FIGS. 28-31, some aspects of
data filtering are described. FIG. 28 illustrates additional EMG
data for the same patient also during a seizure. In this
embodiment, the EMG 2 signal at time approximately 7 h22'50'' to
approximately 7 h22'55'' has been filtered with a 3rd order
Butterworth filter from 300 Hz to 500 Hz. When filtering is applied
to the EMG 2 signal, the time domain data shows a series of bursts,
i.e., regions of elevated EMG signal amplitude separated by lower
amplitude signals, with high signal to noise. For example, at least
four different burst regions (316, 318, 320, and 322) may be
detected in the data of FIG. 28. The bursts shown in FIG. 28 may be
categorized based upon the number of bursts, e.g., such as four,
within a time window, the period between adjacent bursts (324, 326,
and 328) and the time duration of a burst (330). Such burst
features may be seizure variables. Referring now to the spectral
graphs in FIG. 28, application of a high frequency filter in this
embodiment, clearly illustrates the presence of high intensity
frequency data. FIG. 28 also shows sharp, brief frequency "spikes"
in the bottom two graphs. Those spikes may generally correspond to
noise from overhead lighting at household frequency of 60 Hz, and
may generally appear at 60 Hz harmonics. Such interferences may be
recognized and an algorithm may include instructions to disregard
such data signatures. Also, the EMG1 signal (biceps) shows
sustained contraction (tonic activity), and the EMG2 signal
(triceps) shows periodic contraction (clonic activity). Thus, and
in contrast to the data illustrated in FIG. 27, such agonist and
antagonist muscle groups do not necessarily have a correlated phase
between them.
[0186] The lower right graph of FIG. 29 in particular shows even
more dramatically how filtering from 350 Hz to 450 Hz, in the EMG 2
signal, can reveal bursts (332, 334, and 336) and high frequency
information (338) out of the electrode signal (showing time
approximately 7 h22'10'' to approximately 7 h22'15''). The
selection of a given filter may in some embodiments be adjusted for
a given patient.
[0187] FIG. 30 shows the exact same frame as FIG. 29, except the
EMG 2 signal is unfiltered. It is evident from the spectral display
that the lower frequencies have a higher amplitude as compared to
the data in FIG. 29. Furthermore, bursts associated with the time
domain data clearly have much lower signal to noise ratios. Based
on the data in FIG. 29 and FIG. 30, it should be appreciated that
electrode data may be filtered in any of various ways. The value of
a given seizure variable may be determined from data collected
using a filter that improves the signal to noise of the calculated
value. For example, burst width and burst count may be collected
from an electrode that uses a filter, such as a 3rd order
Butterworth filter from 300 Hz to 500 Hz (FIG. 28) or a filter from
350 Hz to 450 Hz (FIG. 29). Other seizure variables, such as the
slump to bump ratio of a GTC waveform may be collected without use
of a filter or with another filter, such as one that passes a lower
range of frequencies, as shown in FIG. 30. As shown in FIG. 30 a
slump region (340) and a bump region (342) may be detected.
[0188] FIG. 31 provides another good example of increasing the
discrimination of seizure bursts for the EMG 2 signal with respect
to the noise (time approximately 7 h25'22'' to approximately 7
h25'27''), e.g., increasing the signal to noise ratio of the
spectral data by filtering the raw data. For example,
representative burst (344) shows a high signal to noise ratio. Note
the relative irregularity of the bursts (344, 346, and 348), as
shown in the time domain data, which may be a factor that tends to
indicate a seizure. That is, the periods between adjacent bursts,
such as burst interval (350) and burst interval (352), have
different values. In FIG. 31 the EMG 1 data, which has not been
filtered, shows a characteristic GTC waveform, with a detectable
slump (354) and bump (356).
Example 7
[0189] In Example 7, and associated FIGS. 32-34, some aspects of
data that may, for example, include features that may apply
negative weighting to detection algorithm are discussed. FIG. 32
may indicate a short-lived seizure preceding the foregoing seizure
(time approximately 5 h17'41'' to approximately 5 h17'46'').
Several bursts (358, 360, and 362) appear to have occurred, and are
evident in both the EMG 1 and EMG 2 signals. Those bursts may be of
relatively low concern due to their short duration. Some patients
experience many of these short seizures. Comparison, of such short
bursts with archived data, e.g., historical data for such patients,
may be used to modify, e.g., a minimum burst detection width
criteria. Thus, the algorithm may adapt to selectively neglect some
data features, i.e., short and inconsequential bursts, and the
algorithm may become better adapted to avoid initiation of
unnecessary alarms.
[0190] FIG. 33 provides an example of high amplitude signals even
after the EMG 2 signal has been filtered (time approximately 5
h15'46 to approximately 5 h15'51''). As the upper two waveforms
show ("EMG1-raw" and "EMG2-raw"), the signals are highly uniform, a
characteristic that may be detected and may be used to assess that
the data may not indicate a seizure. The bursts are also very close
together (the burst period is too small). Such a characteristic may
also be detected and used to qualify the data and weigh against a
determination that a seizure may be occurring. In some embodiments,
either the signal uniformity or time period between regions of
elevated amplitude may be used to disqualify data events or may be
used to apply a negative weight to a seizure variable, e.g.,
amplitude bursts. Data that is highly uniform or has too short a
period between data events may indicate an interfering signal, such
as from a nearby electrical device. In real seizures, huge spikes
at several discrete frequencies are rare or nonexistent. Again,
historical data may be collected for a patient and analyzed.
Coefficients may be adjusted to adapt the algorithm and avoid
initiation of unnecessary alarms.
[0191] FIG. 34 (time approximately 4 h39'36'' to approximately 4
h39'40'') provides another example of sustained signals that may
not trigger an alarm because they are too uniform and/or have too
short a period between repeating data events. Such characteristics
may be attributed to external noise and are typically not
associated with a seizure.
Example 8
[0192] In example 8, and associated FIGS. 35 and 36, data from
another patient who exhibits data bursts is shown. Here, as well, a
differential bipolar electrode with two inputs was placed over the
person's biceps (graph not shown), and also over the persons
triceps (upper graph labeled "EMG2-raw"). The vertical scale shows
the amplitude of the signal. The middle graph (labeled "EMG2
filtered 350-450) shows the signal of the upper graph filtered to
show 350-450 Hz frequencies. Note how well defined the bursts are,
e.g., representative bursts (364) and (366), and how well the
350-450 Hz filtering works to reveal the characteristic GTC
waveform, as seen in the middle graph and in the lower right graph
(labeled "EMG2 Spectral Analysis"). The period of the bursts is
fairly regular but not the same from burst to burst. In that light,
it should be appreciated that while some seizures show fairly
regular periodicity, real seizures are subject to fluctuations that
are greater than some sources of noise, e.g., from man-made sources
or from voluntary muscle activity. The balance between near perfect
regularity for an artificial source of noise and the periodicity of
burst trains may be balanced for an individual patient, such as by
varying coefficients and threshold variables in a periodicity
algorithm.
[0193] FIG. 36 continues the waveform of this patient, and shows
how well ordered, but not completely uniform, a series of bursts
(368, 370, and 372) may be. This pattern may be typical for some
patients and may provide a very characteristic pattern that may be
assigned very high weight in an algorithm.
[0194] Although the disclosed method and apparatus and their
advantages have been described in detail, it should be understood
that various changes, substitutions and alterations can be made
herein without departing from the invention as defined by the
appended claims. Moreover, the scope of the present application is
not intended to be limited to the particular embodiments of the
process, machine, manufacture, composition, or matter, means,
methods and steps described in the specification. As one will
readily appreciate from the disclosure, processes, machines,
manufacture, compositions of matter, means, methods, or steps,
presently existing or later to be developed that perform
substantially the same function or achieve substantially the same
result as the corresponding embodiments described herein may be
utilized. Accordingly, the appended claims are intended to include
within their scope such processes, machines, manufacture,
compositions of matter, means, methods or steps.
* * * * *