U.S. patent application number 11/616788 was filed with the patent office on 2008-07-03 for low power device with contingent scheduling.
Invention is credited to Kent Leyde.
Application Number | 20080161712 11/616788 |
Document ID | / |
Family ID | 39584997 |
Filed Date | 2008-07-03 |
United States Patent
Application |
20080161712 |
Kind Code |
A1 |
Leyde; Kent |
July 3, 2008 |
Low Power Device With Contingent Scheduling
Abstract
Medical device systems and methods for operating medical device
systems conserve energy by efficiently managing computational
demands of the systems. A first analysis, having relatively lower
computational processing demand than at least a second analysis,
processes signals from a subject to determine a first estimate of a
propensity for the subject to have a neurological event. If the
first estimate meets a set of specified criteria, a second analysis
is performed to determine a second estimate of the propensity for
the subject to have a neurological event.
Inventors: |
Leyde; Kent; (Sammamish,
WA) |
Correspondence
Address: |
WOODCOCK WASHBURN LLP
CIRA CENTRE, 12TH FLOOR, 2929 ARCH STREET
PHILADELPHIA
PA
19104-2891
US
|
Family ID: |
39584997 |
Appl. No.: |
11/616788 |
Filed: |
December 27, 2006 |
Current U.S.
Class: |
600/544 |
Current CPC
Class: |
A61B 5/0006 20130101;
A61B 5/6814 20130101; A61B 5/7264 20130101; A61B 2560/0209
20130101; A61B 5/4094 20130101; A61B 5/369 20210101; G16H 40/63
20180101; A61B 5/024 20130101 |
Class at
Publication: |
600/544 |
International
Class: |
A61B 5/04 20060101
A61B005/04 |
Claims
1. A method of operating a medical device system, the method
comprising: receiving at least one signal from a physiological
sensor; processing the at least one signal by performing a first
analysis to estimate a first propensity for a subject to have a
neurological event; and performing a second analysis to estimate a
second propensity for the subject to have a neurological event when
the first estimate meets one or more specified criteria.
2. The method of claim 1 wherein performing the first analysis
requires less computational power than performing the second
analysis.
3. The method of claim 1 further comprising modifying the one or
more specified criteria based on a charge measurement for a battery
associated with the medical device system.
4. The method of claim 1 wherein receiving at least one signal
comprises measuring one or more electrical signals generated by
neural activity.
5. The method of claim 4 wherein processing the at least one signal
comprises analyzing said at least one signal by a first set of
feature extractors.
6. The method of claim 5 wherein the first set of feature
extractors comprises one or more feature extractors selected from
the group consisting of frequency based feature extractors, phase
coherence feature extractors, and band power feature
extractors.
7. The method of claim 5 wherein at least one of the feature
extractors in the first set of feature extractors is selected based
on information about the subject.
8. The method of claim 5 wherein the second analysis comprises
logic to analyze the at least one signal by a second set of feature
extractors.
9. The method of claim 8 wherein said second set of feature
extractors is characterized by relatively high specificity and
relatively high computational complexity in comparison to the first
set of feature extractors.
10. The method of claim 4 wherein the estimate of a first
propensity for the subject to have a neurological event is an
estimate of a probability for the subject to have a neurological
event within a specified time frame.
11. The method of claim 4 wherein the neurological event is a
seizure.
12. The method of claim 1 further comprising communicating to the
subject information indicative of the second estimate when the
first estimate meets the one or more specified criteria.
13. The method of claim 1 wherein performing the first analysis and
performing the second analysis is performed by a processing system
that is external to the subject.
14. The method of claim 1 wherein performing the first analysis is
performed by a first processing system and performing the second
analysis is performed by a second processing system.
16. The method of claim 1 further comprising: pre-processing the at
least one signal using a first processing system to derive data
representative of the one or more signals; communicating the
derived data representative of the at least one signal to a second
processing system, wherein the steps of processing the at least one
signal by performing a first analysis and performing a second
analysis are performed by the second processing system.
17. The method of claim 16 wherein the first processing system is
implanted in the subject and the second processing system is
external to the subject.
18. A system for estimating a propensity for a subject to have a
neurological event comprising: one or more sensors for measuring at
least one signal from the subject; first logic arranged to analyze
the at least one signal from the subject and estimate a first
propensity for the subject to have a neurological event; a
processor configured to perform the first logic; second logic
configured to be performed when the first estimate meets one or
more specified criteria and arranged to estimate a second
propensity for the subject to have a neurological event.
19. The system of claim 18 wherein the first logic is embodied in
executable instructions stored on computer accessible media and the
second logic is embodied in executable instructions stored on
computer accessible media.
20. The system of claim 18 wherein the first logic is embodied in a
programmable logic device and the second logic is embodied in a
programmable logic device.
21. The system of claim 18 wherein the first logic is embodied in a
circuit and the second logic is embodied in a circuit.
22. The system of claim 18 wherein estimating the first propensity
requires less computational power than estimating the second
propensity.
23. The system of claim 18 wherein the estimate of the first
propensity is a measure of the likelihood for the subject to have a
neurological event within a specified time period after the signals
are detected.
24. The system of claim 23 wherein the specified criteria comprise
the condition of the estimate of the first propensity indicating
that a likelihood for the subject to have a neurological event
exceeds a specified threshold.
25. The system of claim 18 wherein the one or more sensors comprise
one or more sensors for measuring brain activity signals of the
subject.
26. The system of claim 25 wherein the neurological event is an
epileptic seizure.
27. The system of claim 18 wherein the processor is external to the
subject and the processor is configured to perform the second
logic.
28. A device comprising: means for measuring at least one signal
from a subject; means for analyzing the at least one signal from a
subject to estimate a first propensity for the subject to have a
neurological event; means for determining whether the estimate of
the first propensity meets one or more specified criteria; means
for estimating a second propensity for the subject to have a
neurological event when the estimate of the first propensity meets
the one or more specified criteria.
29. A system for estimating a propensity for a subject to have an
epileptic seizure comprising: at least one sensor for measuring at
least one neurological signal from the subject; first logic
arranged to extract at least one feature from the at least one
neurological signal and to classify the extracted at least one
feature to estimate a first propensity of the subject to have an
epileptic seizure; a processor configured to perform the first
logic; second logic arranged to estimate a second propensity of the
subject to have an epileptic seizure when the estimate of the first
propensity meets at least one specified criteria; and a processor
configured to perform the second logic.
30. The system of claim 29 wherein the first logic is embodied in
executable instructions stored on computer accessible media and the
second logic is embodied in executable instructions stored on
computer accessible media.
31. The system of claim 29 wherein the first logic is embodied in a
programmable logic device and the second logic is embodied in a
programmable logic device.
32. The system of claim 29 wherein the first logic is embodied in a
circuit and the second logic is embodied in a circuit.
33. The system of claim 29 further comprising: an implanted device
for wirelessly transmitting data representative of said at least
one neurological signal from a subject; and a receiver for
receiving the transmitted data wherein said receiver is
operationally coupled to at least one of the first processor and
the second processor.
34. The system of claim 33 further comprising an output device for
communicating information related to the estimate of the second
propensity to the subject.
Description
FIELD
[0001] The present invention relates generally to medical device
systems.
BACKGROUND
[0002] A variety of medical device systems are used to measure
physiological signals from a subject and to process the signals and
provide indications of potential or actual problem conditions.
Computational demands of processing systems associated with the
medical devices systems produce drains on the power sources of
these device systems and can have a major impact on overall battery
life. Moreover, in many medical device systems, it is desirable to
keep the system as small and unobtrusive as possible so that the
patient can have it available at all times.
[0003] In the case of implantable systems, power source replacement
may involve surgery with its attendant costs and risks to the
subject. Moreover, power source replacement may involve replacement
of the implantable system itself because such units are typically
hermetically sealed to reduce the likelihood of infection.
SUMMARY
[0004] The invention provides medical device systems and methods
for operating medical device systems that provide energy savings by
efficiently managing computational demands of the systems. Some
such systems comprise detectors to receive input from a subject,
communications systems to deliver signals indicative of the input
to a processor, processing systems to analyze the signals and
determine one or more conditions of the subject, and a
communication system to provide indications of the one or more
conditions to the subject.
[0005] The various embodiments describe ways for a medical device
to provide at least two stages of processing for signals measured
from a subject. A first set of computer instructions, programmable
logic, or circuitry, for example, one or more feature extractors
and one or more classifiers process the signals to determine a
first estimate of a susceptibility or propensity of the subject to
have a neurological event. If the first estimate meets a set of
criteria, a second set of computer instructions, programmable logic
or circuitry, typically more computationally demanding and/or more
sensitive and possibly providing more specific results than the
first, is enabled to determine a second estimate of the propensity
for the subject to have a neurological event. If desired,
information related to the estimate may be output to the subject.
The systems and method can proceed in such a manner for any number
of iterations. For example, a third set of computer instructions,
programmable logic or circuitry may be enabled if the second
estimate meets a second set of criteria, and so forth. Information
related to the estimate may be output to the subject. The computer
instructions, programmable logic or circuitry may be enabled on a
processing system that is external to the subject, on a processing
system that is implanted in the subject, or on combinations of
external and implanted processing systems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a block diagram illustrating aspects of contingent
use of feature extractors and classifiers;
[0007] FIG. 2 is an example of a classifier output;
[0008] FIG. 3 is a flow chart of an embodiment of contingent
analysis;
[0009] FIG. 4 is an example timeline for an embodiment of an
analysis scheduling that varies temporally.
[0010] FIG. 5 is a simplified diagram of a system that embodies a
contingent scheduling system;
[0011] FIG. 6 is a block diagram of an implanted communications
unit that may be used in accordance with the systems and methods
described herein;
[0012] FIG. 7 is a block diagram of an external data device that
may be used in accordance with the systems and methods described
herein;
[0013] FIG. 8 is an example timeline for a typical therapeutic
regimen for the treatment of epilepsy; and
[0014] FIG. 9 is an example timeline for a therapeutic regimen for
the treatment of epilepsy that may be enabled by the system and
methods described herein.
DETAILED DESCRIPTION
[0015] Certain specific details are set forth in the following
description and figures to provide an understanding of various
embodiments of the invention. Certain well-known details,
associated electronics and medical devices are not set forth in the
following disclosure to avoid unnecessarily obscuring the various
embodiments of the invention. Further, those of ordinary skill in
the relevant art will understand that they can practice other
embodiments of the invention without one or more of the details
described below. Finally, while various processes are described
with reference to steps and sequences in the following disclosure,
the description is for providing a clear implementation of
particular embodiments of the invention, and the steps and
sequences of steps should not be taken as required to practice this
invention.
[0016] As described in the background, a variety of medical device
systems are used to measure physiological signals from a subject
and to process those signals. Some such medical device systems,
especially those comprising ambulatory and implantable devices,
operate on portable power sources such as batteries. Computational
processing demands, especially in the case of substantially
continuous or repeated monitoring of the subject, can cause energy
drains and may have a negative impact on battery life. The various
embodiments of the systems and methods provided herein reduce the
computational burden on such systems to extend the battery
life.
[0017] Although some of the discussion below focuses on measuring
EEG signals of subjects and subject populations for the detection
and prediction of epileptic seizures, it should be appreciated that
the invention is not limited to measuring EEG signals or to
predicting epileptic seizures. For example, the invention could
also be used in systems that measure one or more of a blood
pressure, pulse oximetry, temperature of the brain or of portions
of the subject, blood flow measurements, ECG/EKG, heart rate
signals, respiratory signals, chemical concentrations of
neurotransmitters, chemical concentrations of medications, pH in
the blood, or other physiological or biochemical parameters of a
subject.
[0018] Furthermore, aspects of the invention may be useful for
monitoring and assisting in the treatments for a variety of
conditions such as sleep apnea and other sleep disorders, migraine
headaches, depression, Alzheimer's, Parkinson's Disease, dementia,
attention deficit disorder, stroke, eating disorders, other
neurological or psychiatric disorders, cardiac disease, diabetes,
cancer, or the like.
[0019] Using epilepsy as an illustrative example, epilepsy is a
disorder of the brain characterized by neurological events in the
form of chronic, recurring seizures and affects an estimated 50
million people worldwide. Seizures are a result of uncontrolled
discharges of electrical activity in the brain. A seizure typically
manifests itself as sudden involuntary, disruptive, and often
destructive sensory, motor, and cognitive phenomena. Epilepsy is
usually treated, though not cured, with medication. Surgery may be
indicated in cases in which seizure focus is identifiable, and the
seizure focus is not located in the eloquent cortex.
[0020] A single neurological event most often does not cause
significant morbidity or mortality, but severe or recurring
neurological events can result in major medical, social, and
economic consequences. Epilepsy is more often diagnosed in children
and young adults. People with uncontrolled epilepsy are often
significantly limited in their ability to work in many industries
and may not be able to legally drive an automobile.
[0021] The cause of epilepsy is often uncertain. Symptomatic
epilepsies arise due to some structural or metabolic abnormality of
the brain and may result from a wide variety of causes including
genetic conditions, stroke, head trauma, complications during
pregnancy or birth, infections such as bacterial or viral
encephalitis, or parasites. Idiopathic epilepsies are those for
which no other condition has been implicated as a cause and are
often genetic and generalized. In the majority of cases, the cause
of a subject's epilepsy is unknown.
[0022] One of the most disabling aspects of neurological disorders
such as epilepsy is the seeming unpredictability of neurological
events such as seizures. Mechanisms underlying the generation of
seizures are thought to operate over a period of seconds to minutes
before the clinical onset of a seizure. Typically, electrographic
manifestations of a neurological event are detectible some time
before clinical manifestations occur. Most work in the quantitative
analysis of neurological events has been aimed at detecting these
electrographic manifestations. NeuroPace, Inc. has been developing
systems to detect the electrographic onset of a neurological event
so that some action, such as direct electrical stimulation of
certain brain structures, may be taken in an attempt to preempt the
clinical onset of a neurological event. However, the detection of
the electrographic onset of a neurological event may not come far
enough in advance of the clinical onset for electrical stimulation
or other therapies, such as the administration of anticonvulsant
drugs, to be effective in preventing the clinical onset.
Additionally, seizure activity may already be causing harm to the
brain before the clinical onset of the seizure.
[0023] It is desirable to be able to predict neurological events
well before their electrographic onset. Embodiments of predictive
systems generally comprise a collection of detectors for acquiring
data from a subject and an analysis system for processing the data.
Predictive analysis systems are routinely considered to be
comprised of arrangements of feature extractors and classifiers.
Feature extractors are used to quantify or characterize certain
aspects of the measured input signals. Classifiers are then used to
combine the results obtained from the feature extractors into an
overall answer or result. Systems may be designed to detect
different types of conditions that may be reflective of neural
condition. These could include, but are not limited, to systems
designed to detect if the subject's neural condition is indicative
of an increased susceptibility or propensity for a neurological
event or systems designed to detect deviation from a normal
condition. As can be appreciated, for other neurological or
non-neurological disorders, the classification of the subject's
neural condition will be based on systems, feature extractors and
classifiers that are deemed to be relevant to the particular
disorder.
[0024] FIG. 1 depicts an example of the overall structure of a
system for estimating a propensity for the onset of a neurological
event such as, for example, an epileptic seizure. The input data
102 may comprise representations of physiological signals obtained
from monitoring a subject. Any number of signal channels may be
used. Examples of physiological signals that may be used as input
data 102 include, but are not limited to, electrical signals
generated by electrodes placed on or within the brain or nervous
system (EEG signals), temperature of the brain or of portions of
the brain, blood pressure or blood flow measurements, pulse
oximetry, ECG/EKG, blood pH, chemical concentrations of
neurotransmitters, chemical concentrations of medications,
combinations of the preceding, and the like.
[0025] The input data may be in the form of analog signal data or
digital signal data that has been converted by way of an analog to
digital converter (not shown). The signals may also be amplified,
preprocessed, and/or conditioned to filter out spurious signals or
noise. For purposes of simplicity the input data of all of the
preceding forms is referred to herein as input data 102.
[0026] The input data 102 from the selected physiological signals
is supplied to one or more feature extractors 104a, 104b, 105. A
feature extractor 104a, 104b, 105 may be, for example, a set of
computer executable instructions stored on a computer readable
medium, or a corresponding instantiated object or process that
executes on a computing device. Certain feature extractors may also
be implemented as programmable logic or as circuitry. In general, a
feature extractor 104a, 104b, 105 can process data 102 and identify
some characteristic of the data 102. Such a characteristic of the
data is referred to herein as an extracted feature.
[0027] Operation of a feature extractor 104a, 104b, 105 requires
expenditure of electrical energy to process data and identify
characteristics. The amount of electrical energy required may
depend on the complexity, quantity, and quality of the input data
and on the complexity of the processing system applied to the input
data. Feature extractors 104a, 104b, 105 that are more complex
generally require correspondingly larger amounts of electrical
energy. As described more fully below, in some embodiments, some
feature extractors 105 may be optionally applied or omitted in
various circumstances. For example, when the application of one set
of feature extractors 104a, 104b is sufficient to estimate that a
propensity for a neurological event is sufficiently low, then other
feature extractors 105 may not be applied to the input data 102. If
the set of feature extractors 104a, 104b indicates a higher
propensity for a neurological event, then additional feature
extractors 105 may be applied to the input data 102.
[0028] Each feature extractor 104a, 104b, 105 may be univariate
(operating on a single input data channel), bivariate (operating on
two data channels), or multivariate (operating on multiple data
channels). Some examples of potentially useful characteristics to
extract from signals for use in determining the subject's
propensity for a neurological event, include but are not limited
to, alpha band power (8-13 Hz), beta band power (13-18 Hz), delta
band power (0.1-4 Hz), theta band power (4-8 Hz), low beta band
power (12-15 Hz), mid-beta band power (15-18 Hz), high beta band
power (18-30 Hz), gamma band power (30-48 Hz), second, third and
fourth (and higher) statistical moments of the EEG amplitudes,
spectral edge frequency, decorrelation time, Hjorth mobility (HM),
Hjorth complexity (HC), the largest Lyapunov exponent L(max),
effective correlation dimension, local flow, entropy, loss of
recurrence LR as a measure of non-stationarity, mean phase
coherence, conditional probability, brain dynamics (synchronization
or desynchronization of neural activity, STLmax, T-index, angular
frequency, and entropy), line length calculations, area under the
curve, first, second and higher derivatives, integrals, or a
combination thereof. Of course, for other neurological conditions,
additional or alternative characteristic extractors may be used
with the systems described herein.
[0029] The extracted characteristics can be supplied to one or more
classifiers 106, 107. Like the feature extractors 104a, 104b, 105,
each classifier 106, 107 may be, for example, a set of computer
executable instructions stored on a computer readable medium or a
corresponding instantiated object or process that executes on a
computing device. Certain classifiers may also be implemented as
programmable logic or as circuitry. Operation of a classifier 106,
107 requires electrical energy. The classifiers can vary in
complexity. Classifiers 106, 107 that are more complex may require
correspondingly larger amounts of electrical energy. In some
embodiments, some classifiers may be optionally applied or omitted
in various circumstances. For example, when the application of one
or more classifiers 106 is sufficient to estimate that a propensity
for a neurological event is sufficiently low, then other
classifiers 107 may not be applied to the extracted
characteristics. If the classifiers 106 indicate a higher
propensity for a neurological event, then additional classifiers
107 may be applied to the extracted characteristics.
[0030] The classifiers 106, 107 analyze one or more of the
extracted characteristics and possibly other subject dependent
parameters to provide a result 108 that may characterize, for
example, a subject's neural condition. Some examples of classifiers
include k-nearest neighbor ("KNN"), neural networks, and support
vector machines ("SVM"). Each classifier 106, 107 may provide a
variety of output results, such as a logical result or a weighted
result. The classifiers 106, 107 may be customized for the
individual subject and may be adapted to use only a subset of the
characteristics that are most useful for the specific subject. For
example, the classifier may detect pre-onset characteristics of a
neurological event. Additionally, over time, the classifiers 106,
107 may be further adapted to the subject, based, for example, in
part on the result of previous analyses and may reselect extracted
characteristics that are used for the specific subject.
[0031] As it relates to epilepsy, for example, one implementation
of a classification of neural conditions defined by the classifiers
106, 107 may include (1) an inter-ictal condition (sometimes
referred to as a "normal" condition), (2) a pre-ictal condition
(sometimes referred to as an "abnormal" or "pre-seizure"
condition), (3) an ictal condition (sometimes referred to as a
"seizure" condition), and (4) a post-ictal condition (sometimes
referred to as a "post-seizure" condition). In another embodiment,
it may be desirable to have the classifier classify the subject as
being in one of two conditions--a pre-ictal condition or
inter-ictal condition--which could correspond, respectively, to
either an elevated or high propensity for a future seizure or a low
propensity for a future seizure.
[0032] As noted above, instead of providing a logical answer, it
may be desirable for a classifier 106, 107 to provide a weighted
answer so as to further delineate within the pre-ictal condition to
further allow the system to provide a more specific output
communication for the subject. For example, instead of a simple
logical answer (e.g., pre-ictal or inter-ictal) it may be desirable
to provide a weighted output or other output that quantifies the
subject's propensity, probability, likelihood and/or risk of a
future neurological event using some predetermined scale (e.g.,
scale of 1-10, with a "1" meaning "normal" and a "10" meaning a
neurological event is imminent). For example, if it is determined
that the subject has an increased propensity for a neurological
event (e.g., subject has entered the pre-ictal condition), but the
neurological event is likely to occur on a long time horizon, the
output signal could be weighted to be reflective of the long time
horizon, e.g., an output of "5". However, if the output indicates
that the subject is pre-ictal and it is predicted that the
neurological event is imminent within the next 10 minutes, the
output could be weighted to be reflective of the shorter time
horizon to the neurological event, e.g., an output of "9." On the
other hand, if the subject is normal, the system may provide an
output of "1".
[0033] Other implementations involve classifier 106 outputs
expressing the inter-ictal and pre-ictal conditions as a continuum,
with a scalar or vector of parameters describing the actual
condition and its variations. FIG. 2 depicts an example of a
graphical display of the output of one embodiment of a classifier
over a period of time. The output of the classifier at any point in
time is a vector of two estimated probabilities: an estimated
probability 152 that the input data is indicative of an inter-ictal
condition and an estimated probability 154 that the input data is
indicative of a pre-ictal condition. The sum of the two
probabilities 152, 154, at any given time is one. The estimated
probabilities 152, 154 are plotted over a period of time beginning
approximately 360 minutes before the onset of a neurological event
at time zero, indicated by the vertical axis 150. In the example
graph, the estimated probability 152 that the data is indicative of
an inter-ictal state was larger than the estimated probability 154
that the data was indicative of a pre-ictal state, until
approximately 80 minutes before the onset 150 of the neurological
event. The estimated probability 152 then dropped to very small
values beginning approximately 50 minutes before the onset 150. The
estimated probability 154 that the data is indicative of a
pre-ictal state remained low until approximately 80 minutes before
the onset 150 of the neurological event at which time it began to
trend upward rapidly.
[0034] As described above, the computational demands of the
processing provided by feature extractors 104a, 104b, 105 and
classification provided by classifiers 106, 107 can be extensive.
In the case of ambulatory systems supplied by portable power
sources, such as implanted batteries, supplying the energy required
to meet the computational demands can severely limit power source
life. In some applications, physiological signals may be measured
and analyzed continuously or often over long periods of time and
the need to conserve energy may be particularly acute.
[0035] FIG. 3 depicts a simplified block diagram of a method of
operating medical devices for analyzing signals that provides
energy savings by enabling different parts of an overall algorithm
to process signals only as needed in order to reduce energy
consumption and optimize system performance. One or more
physiological signals from a subject are measured 110. In one
embodiment, sixteen channels of physiological signals are measure.
More or fewer channels may be measured according to the particular
kinds of analysis being employed. The measured signals may be
pre-processed, such as, for example, by amplification, filtering,
and/or conversion from analog to digital, to generate input data
112 for the analysis. Input data 112 may also comprise other
subject dependent parameters (such as subject inputs and/or subject
history data) that may be indicative and/or predictive of a
subject's propensity for a neurological event.
[0036] Typically, the physiological signal(s) from the subject are
measured during a sliding observation window or epoch.
Characteristics of the sliding window may be adapted based on
previous measurements and analysis. In particular, the sliding
windows may operate continuously, periodically during specified
intervals, or during an adaptively modified schedule (for example,
to customize it to the specific subject's cycles). In some
embodiments, adaptations to the sliding window can be made
automatically by the system. In some embodiments, adaptations to
the window may be made by a clinician. For example, if it is known
that the subject is prone to have a neurological event in the
morning, a clinician may program the system to continuously monitor
the subject during the morning hours, while only periodically
monitoring the subject during the remainder of the day. As another
example, it may be less desirable to monitor a subject and provide
an output to a subject when the subject is asleep. In such cases,
the system may be programmed to discontinue monitoring or change
the monitoring and communication protocol with the subject during a
specified "sleep time" or whenever a subject inputs into the system
that the subject is asleep or when the system determines that the
subject is asleep. This could include intermittent monitoring,
monitoring with a varying duty cycle, decreasing of the sampling
frequency, or other energy saving or data minimization strategy
during a time period in which the risk for a neurological event is
low. Additionally, the system could enter into a low risk mode for
a time period following each medication dose.
[0037] Input data 112 is subjected to a first stage analysis 114.
The first stage analysis 114 may be performed by logic embodied in,
for example, computer-executable instructions, such as program
modules, executed by one or more computers or other devices.
Generally, program modules include routines, programs, objects,
components, data structures, and the like that perform particular
tasks or implement particular abstract data types. Typically the
functionality of the program modules may be combined or distributed
as desired in various embodiments. The first stage analysis 114 may
comprise the application of one or more feature extractors and a
classifier such as described above. Typically, the first stage
analysis 114 will comprise the application of a subset of available
feature extractors applied to the input data to identify some
characteristics of the signals. Preferably, the first stage
analysis will be relatively low in computational demands and will
have a relatively high sensitivity, but not necessarily a high
specificity. In general terms, the sensitivity of the analysis is
related to the probability that analysis indicates the presence of
a condition given that the condition actually exists. In general
terms, the specificity of the analysis is related to the
probability that the analysis indicates the absence of a condition
given that the condition is actually absent. The detection of
particular frequencies in EEG signals is one example of feature
extractor having a relatively low computational demand. The
output(s) from the first set of feature extractors may be combined
using a first classifier.
[0038] Based on the first stage analysis 114, a first estimate of a
susceptibility or propensity for the subject to have a neurological
event is determined 116. The first estimate may take the form of a
qualitative characterization or may be represented quantitatively
or by a combination of qualitative and quantitative
characterizations. A qualitative characterization may, for example,
relate to the presence of pre-onset characteristics for a
neurological event. A quantitative characterization may be a single
number, such as, for example, by a probability of a neurological
event occurring in a predetermined time period following the
measurement of the signals or an estimated time horizon during
which an estimated propensity for the subject to have a
neurological event is below a predetermined threshold, or a
collection of values that characterize the analysis.
[0039] The first estimate 116 is then examined 118 to determine
whether it meets one or more specified criteria. The specified
criteria may be universal or may be adapted to a particular
subject. By way of examples, the criteria may include the presence
or absence of certain features in the signals or the exceedance of
a threshold probability. The criteria may be modified over time.
The criteria may be adapted in response to various conditions of
the subject such as, for example, the subject's state of
wakefulness or current activity level. The criteria may also
adapted in response to current conditions of the medical device
such as, for example, the current charge state of a battery.
[0040] If the criteria are not met, a second stage of analysis 120
is not performed and the system may return to a monitoring
condition 126. In this instance, the computational and energy costs
of the second stage analysis 120 are not incurred. For additional
energy savings, for example if it is determined that a seizure is
very unlikely, the system may also reduce the sampling rate or
cease monitoring and turn off or go to sleep for some specified
amount of time. Such embodiments will depend on the predictive
value of collecting continuous monitoring data. For example, if it
can be determined that the value of such data is low, turning off
may be a viable option for some amount of time.
[0041] If the criteria in step 118 are met, for example if the
estimate derived from the first analysis indicates an increased
susceptibility or propensity for the monitored condition to exist
or occur (for example, prediction of the pre-ictal condition), then
the algorithm may transition from the base mode to a second or
advanced mode wherein a second stage analysis 120 is performed to
determine a second estimate of a propensity for the subject to have
a neurological event 122. The second stage analysis 120 may be
performed by logic embodied in, for example, computer-executable
instructions, such as program modules, executed by one or more
computers or other devices.
[0042] Depending on the particular embodiment, the set of feature
extractors employed in the second stage analysis 120 may be used in
conjunction with the set of feature extractors employed in the
first stage analysis 114 or as an alternative to the first set of
feature extractors. The set of feature extractors employed in the
second stage analysis 120 will typically afford a higher level of
computational complexity and/or may have a higher specificity
and/or sensitivity than the set of feature extractors employed in
the first stage analysis 114. In one embodiment, the second stage
analysis 120 may perform more refined versions of the analyses
performed by the first stage analysis 114. In another embodiment,
the second stage analysis 120 may perform different kinds of
analyses. In yet other embodiments, the feature extractors in the
first stage analysis and second stage analysis may have
multi-resolution predictions and may provide for divergent spatial
predictions. For example, the first stage analysis may include
feature extractors that more accurately predict over a long time
horizon, while the second stage analysis may more accurately
predict over a short time horizon.
[0043] The output from the second set of feature extractors may be
combined in the classifier used in the first stage analysis 114 or
a second classifier. The result from either classifier may, in one
embodiment, have both an improved sensitivity and specificity,
relative to the sensitivity and specificity of the classification
based on only the first set of feature extractors. The second
estimate 122 is preferably more refined than the first estimate 116
and may take the form of a qualitative or a quantitative
characterization or a combination thereof. It will be appreciated,
however, that battery life is saved regardless of whether more or
less computation is required to produce the second estimate 122
than the first estimate 116.
[0044] Once the subject's susceptibility or propensity for seizure
is estimated by the predictive algorithm, a signal that is
indicative of the propensity for the future seizure may optionally
be communicated to the subject 124. In some embodiments, the
predictive algorithm provides an output that indicates when the
subject has an elevated propensity for seizure. In such
embodiments, the communication output to the subject may simply be
a warning or a recommendation to the subject that was programmed
into the system by the clinician. In other embodiments, the
predictive algorithm may output a graded propensity assessment, a
quantitative assessment of the subject's condition, a time horizon
until the predicted seizure will occur, or some combination
thereof. In such embodiments, the communication output to the
subject may provide a recommendation or instruction that is a
function of the risk assessment, probability, or time horizon.
[0045] It will be recognized by those skilled in the art that the
method described herein can readily be extended to encompass more
than two stages of analysis. In one embodiment, the result of the
first stage analysis may determine which of a plurality of second
stage algorithms is selected to run. In another embodiment, the
result of a second stage analysis may be used to decide whether a
third stage of analysis is run, which result may trigger a fourth
stage analysis, and so on.
[0046] In alternative embodiments of the energy saving methods
disclosed herein, the predictive algorithms are run less often when
previous results indicate that it is unlikely that a neurological
event is imminent. For example, if the result of a previous
execution of the algorithm indicates that it is relatively more
likely that a neurological event will occur within a given time
interval, the predictive algorithm may be scheduled to execute more
frequently. Such variable scheduling techniques may be usefully
combined with the other scheduling techniques discussed in detail
herein.
[0047] FIG. 4 depicts an illustrative example of one such
alternative embodiment. Each bar represents a result of the
predictive system as measuring and analysis cycles were run at
various times, t.sub.1, t.sub.2, etc. At time t.sub.1, a relatively
low propensity of a neurological event was estimated and the next
running of the analysis was scheduled for time t.sub.2. A similarly
low propensity was estimated at time t.sub.2, and so the system was
scheduled to be run again at time t.sub.3, where the time interval
t.sub.3-t.sub.2 is the same as the time interval t.sub.2-t.sub.1.
At time t.sub.4, an elevated propensity was estimated and so the
analysis system was scheduled to execute more frequently, with a
shorter interval between succeeding executions. The estimated
propensity did not change again until the system was executed at
time t.sub.8, at which time the estimated propensity was once again
lower and so the system was scheduled to run on the baseline
schedule as it had been at t.sub.1. At t.sub.11, a highly elevated
propensity was estimated, and so the system was scheduled to run on
a much more frequent schedule which continued until a lowered
propensity was estimated at time t.sub.16.
[0048] The length and frequency of measurement and analysis cycles
may be tailored to the prediction horizon. As an example, if the
predictive system indicates that it is unlikely for a neurological
event to occur in the next hour, the system could be scheduled to
run more often than once per hour, but not so often as several
hundred times per hour. Preferred scheduling frequencies would be
between about 2 and about 100 times the reciprocal of the system's
neurological event prediction horizon. By varying the frequency of
measuring and analysis cycles according to the estimated
propensity, energy savings would be realized.
[0049] In an alternative embodiment, different prediction
subsystems may be run depending on the results of prior
calculations. For example, if the current propensity for a
neurological event is remote, a corresponding exceptionally low
operating power analysis subsystem would be scheduled. If the low
operating power analysis subsystem indicates an elevated propensity
for a neurological event, a second, more computationally demanding
and more specific subsystem would be scheduled. If the output of
the second subsystem indicates a propensity above a certain
threshold, a third subsystem may be scheduled, and so forth. In yet
another alternative embodiment, the approaches of the two preceding
embodiments may be combined. The selection of subsystems and their
rates of execution may depend on the results of prior analyses.
[0050] The systems described herein may be embodied as software,
hardware, firmware, or combinations thereof. In some instances, it
may be desirable to have first or lower stage systems operating
only in hardware in order to minimize energy requirements. The
systems described above may be embodied in a device external to the
subject, an implanted device, or distributed between an implanted
device and an external device.
[0051] Because the methods of the present invention are able to
consume less energy than conventional algorithms and will thereby
prolong the life of the power sources, the methods of the present
invention will facilitate the long-term implementation of the
algorithms in a portable and/or implantable device system. FIG. 5
illustrates a system in which the algorithms of the present
invention may be embodied. The system 200 is used to monitor a
subject 202 for purposes of detecting and predicting neurological
events. The system 200 of the embodiment provides for substantially
continuous sampling of brain wave electrical signals such as in
electroencephalograms or electrocorticograms, referred to
collectively as EEGs.
[0052] The system 200 comprises one or more sensors 204 configured
to measure signals from the subject 202. The sensors 204 may be
located anywhere on the subject. In the exemplary embodiment, the
sensors 204 are configured to sample electrical activity from the
subject's brain, such as EEG signals. The sensors 204 may be
attached to the surface of the subject's body (e.g., scalp
electrodes), attached to the head (e.g., subcutaneous electrodes,
bone screw electrodes, and the like), or, preferably, may be
implanted intracranially in the subject 202. In one embodiment, one
or more of the sensors 204 will be implanted adjacent a previously
identified epileptic focus, a portion of the brain where such a
focus is believed to be located, or adjacent a portion of a seizure
network.
[0053] Any number of sensors 204 may be employed, but the sensors
204 will preferably include between 1 sensor and 16 sensors. The
sensors may take a variety of forms. In one embodiment, the sensors
comprise grid electrodes, strip electrodes and/or depth electrodes
which may be permanently implanted through burr holes in the head.
Exact positioning of the sensors will usually depend on the desired
type of measurement. In addition to measuring brain activity, other
sensors (not shown) may be employed to measure other physiological
signals from the subject 202.
[0054] In an embodiment, the sensors 204 will be configured to
substantially continuously sample the brain activity of the groups
of neurons in the immediate vicinity of the sensors 204. The
sensors 204 are electrically joined via cables 206 to an implanted
communication unit 208. In one embodiment, the cables 206 and
communication unit 208 will be implanted in the subject 202. For
example, the communication unit 208 may be implanted in a
subclavicular cavity of the subject 202. In alternative
embodiments, the cables 206 and communication unit 208 may be
attached to the subject 202 externally.
[0055] In one embodiment, the communication unit 208 is configured
to facilitate the sampling of signals from the sensors 204.
Sampling of brain activity is typically carried out at a rate above
about 200 Hz, and preferably between about 200 Hz and about 1000
Hz, and most preferably at about 400 Hz. The sampling rates could
be higher or lower, depending on the specific conditions being
monitored, the subject 202, and other factors. Each sample of the
subject's brain activity is typically encoded using between about 8
bits per sample and about 32 bits per sample, and preferably about
16 bits per sample.
[0056] In alternative embodiments, the communication unit 208 may
be configured to measure the signals on a non-continuous basis. In
such embodiments, signals may be measured periodically or
aperiodically.
[0057] An external data device 210 is preferably carried external
to the body of the subject 202. The external data device 210
receives and stores signals, including measured signals and
possibly other physiological signals, from the communication unit
208. External data device 210 could also receive and store
extracted features, classifier outputs, patient inputs, and the
like. Communication between the external data device 210 and the
communication unit 208 may be carried out through wireless
communication. The wireless communication link between the external
data device 210 and the communication unit 208 may provide a
one-way or two-way communication link for transmitting data. In
alternative embodiments, it may be desirable to have a direct
communications link from the external data device 210 to the
communication unit 208, such as, for example, via an interface
device positioned below the subject's skin. The interface (not
shown) may take the form of a magnetically attached transducer that
would enable power to be continuously delivered to the
communication unit 208 and would provide for relatively higher
rates of data transmission. Error detection and correction methods
may be used to help insure the integrity of transmitted data. If
desired, the wireless data signals can be encrypted prior to
transmission to the external data device 210.
[0058] FIG. 6 depicts a block diagram of one embodiment of a
communication unit 208 that may be used with the systems and
methods described herein. Energy for the system is supplied by a
rechargeable power supply 224. The rechargeable power supply may be
a battery, or the like. The rechargeable power supply 224 may also
be in communication with a transmit/receive subsystem 226 so as to
receive power from outside the body by inductive coupling,
radiofrequency (RF) coupling, and the like. Power supply 224 will
generally be used to provide power to the other components of the
implantable device. Signals 212 from the sensors 204 are received
by the communication unit 208. The signals may be initially
conditioned by an amplifier 214, a filter 216, and an
analog-to-digital converter 218. A memory module 220 may be
provided for storage of some of the sampled signals prior to
transmission via a transmit/receive subsystem 226 and antenna 228
to the external data device 210. For example, the memory module 220
may be used as a buffer to temporarily store the conditioned
signals from the sensors 204 if there are problems with
transmitting data to the external data device 210, such as may
occur if the external data device 210 experiences power problems or
is out of range of the communications system. The external data
device 210 can be configured to communicate a warning signal to the
subject in the case of data transmission problems to inform the
subject and allow him or her to correct the problem.
[0059] The communication unit 208 may optionally comprise circuitry
of a digital or analog or combined digital/analog nature and/or a
microprocessor, referred to herein collectively as "microprocessor"
222, for processing the signals prior to transmission to the
external data device 210. The microprocessor 222 may execute at
least portions of the analysis as described herein. For example, in
some configurations, the microprocessor 222 may run one or more
feature extractors 104a, 104b, 105 (FIG. 1) that extract
characteristics of the measured signal that are relevant to the
purpose of monitoring. Thus, if the system is being used for
diagnosing or monitoring epileptic subjects, the extracted
characteristics (either alone or in combination with other
characteristics) may be indicative or predictive of a neurological
event. Once the characteristic(s) are extracted, the microprocessor
222 may transmit the extracted characteristic(s) to the external
data device 210 and/or store the extracted characteristic(s) in
memory 220. Because the transmission of the extracted
characteristics is likely to include less data than the measured
signal itself, such a configuration will likely reduce the
bandwidth requirements for the communication link between the
communication unit 208 and the external data device 210.
[0060] In some configurations, the microprocessor 222 in the
communication unit 208 may run one or more classifiers 106, 107
(FIG. 1) as described above with respect to FIG. 1. The result 108
(FIG. 1) of the classification may be communicated to the external
data device 210.
[0061] While the external data device 210 may include any
combination of conventional components, FIG. 8 provides a schematic
diagram of some of the components that may be included. Signals
from the communication unit 208 are received at an antenna 230 and
conveyed to a transmit/receive subsystem 232. The signals received
may include, for example, a raw measured signal, a processed
measured signal, extracted characteristics from the measured
signal, a result from analysis software that ran on the implanted
microprocessor 222, or any combination thereof.
[0062] The received data may thereafter be stored in memory 234,
such as a hard drive, RAM, EEPROM, removable flash memory, or the
like and/or processed by a microprocessor, application specific
integrated circuit (ASIC) or other dedicated circuitry of a digital
or analog or combined digital/analog nature, referred to herein
collectively as a "microprocessor" 236. Microprocessor 236 may be
configured to request that the communication unit 208 perform
various checks (e.g., sensor impedance checks) or calibrations
prior to signal recording and/or at specified times to ensure the
proper functioning of the system.
[0063] Data may be transmitted from memory 234 to microprocessor
236 where the data may optionally undergo additional processing.
For example, if the transmitted data is encrypted, it may be
decrypted. The microprocessor 236 may also comprise one or more
filters that filter out low-frequency or high-frequency artifacts
(e.g., muscle movement artifacts, eye-blink artifacts, chewing, and
the like) so as to prevent contamination of the measured
signals.
[0064] External data device 210 will typically include a user
interface 240 for displaying outputs to the subject and for
receiving inputs from the subject. The user interface will
typically comprise outputs such as auditory devices (e.g.,
speakers) visual devices (e.g., LCD display, LEDs), tactile devices
(e.g., vibratory mechanisms), or the like, and inputs, such as a
plurality of buttons, a touch screen, and/or a scroll wheel.
[0065] The user interface may be adapted to allow the subject to
indicate and record certain events. For example, the subject may
indicate that medication has been taken, the dosage, the type of
medication, meal intake, sleep, drowsiness, occurrence of an aura,
occurrence of a neurological event, or the like. Such inputs may be
used in conjunction with the measured data to improve the
analysis.
[0066] The LCD display may be used to output a variety of different
communications to the subject including, status of the device
(e.g., memory capacity remaining), battery state of one or more
components of system, whether or not the external data device 210
is within communication range of the communication unit 208, a
warning (e.g., a neurological event warning), a prediction (e.g., a
neurological event prediction), a recommendation (e.g., "take
medicine"), or the like. It may be desirable to provide an audio
output or vibratory output to the subject in addition to or as an
alternative to the visual display on the LCD.
[0067] External data device 210 may also include a power source 242
or other conventional power supply that is in communication with at
least one other component of external data device 210. The power
source 242 may be rechargeable. If the power source 242 is
rechargeable, the power source may optionally have an interface for
communication with a charger 244. While not shown in FIG. 8,
external data device 210 will typically comprise a clock circuit
(e.g., oscillator and frequency synthesizer) to provide the time
base for synchronizing the external data device 210 and the
communication unit 208.
[0068] Referring again to FIG. 5, in a preferred embodiment, most
or all of the processing of the signals received by the
communication unit 208 is done in an external data device 210 that
is external to the subject's body. In such embodiments, the
communication unit 208 would receive the signals from subject and
may or may not pre-process the signals and transmit some or all of
the measured signals transcutaneously to an external data device
210, where the prediction of the neurological event and possible
therapy determination is made. Advantageously, such embodiments
reduce the amount of computational processing power that needs to
be implanted in the subject, thus potentially reducing energy
consumption and increasing battery life. Furthermore, by having the
processing external to the subject, the judgment or decision making
components of the system may be more easily reprogrammed or custom
tailored to the subject without having to reprogram the
communication unit 208.
[0069] In alternative embodiments, the predictive systems disclosed
herein and treatment systems responsive to the predictive systems
may be embodied in a device that is implanted in the subject's
body, external to the subject's body, or a combination thereof. For
example, in one embodiment the predictive system may be stored in
and processed by the communication unit 208 that is implanted in
the subject's body. A treatment analysis system, in contrast, may
be processed in a processor that is embodied in an external data
device 210 external to the subject's body. In such embodiments, the
subject's propensity for neurological event characterization (or
whatever output is generated by the predictive system that is
predictive of the onset of the neurological event) is transmitted
to the external subject communication assembly, and the external
processor performs any remaining processing to generate and display
the output from the predictive system and communicate this to the
subject. Such embodiments have the benefit of sharing processing
power, while reducing the communications demands on the
communication unit 208. Furthermore, because the treatment system
is external to the subject, updating or reprogramming the treatment
system may be carried out more easily.
[0070] In other embodiments, the signals 212 may be processed in a
variety of ways in the communication unit 208 before transmitting
data to the external data device 210 so as to reduce the total
amount of data to be transmitted, thereby reducing the power
demands of the transmit/receive subsystem 226. Examples include:
digitally compressing the signals before transmitting them;
selecting only a subset of the measured signals for transmission;
selecting a limited segment of time and transmitting signals only
from that time segment; extracting salient characteristics of the
signals, transmitting data representative of those characteristics
rather than the signals themselves, and transmitting only the
result of classification. Further processing and analysis of the
transmitted data may take place in the external data device
210.
[0071] In yet other embodiments, it may be possible to perform some
of the prediction in the communication unit 208 and some of the
prediction in the external data device 210. For example, one or
more characteristics from the one or more signals may be extracted
with feature extractors in the communication unit 208. Some or all
of the extracted characteristics may be transmitted to the external
data device 210 where the characteristics may be classified to
predict the onset of a neurological event. If desired, external
data device 210 may be customizable to the individual subject.
Consequently, the classifier may be adapted to allow for
transmission or receipt of only the characteristics from the
communication unit 208 that are predictive for that individual
subject. Advantageously, by performing feature extraction in the
communication unit 208 and classification in an external device at
least two benefits may be realized. First, the amount of wireless
data transmitted from the communication unit 208 to the external
data device 210 is reduced (versus transmitting pre-processed
data). Second, classification, which embodies the decision or
judgment component, may be easily reprogrammed or custom tailored
to the subject without having to reprogram the communication unit
208.
[0072] In yet another embodiment, feature extraction may be
performed external to the body. Pre-processed signals (e.g.,
filtered, amplified, converted to digital) may be transcutaneously
transmitted from communication unit 208 to the external data device
210 where one or more characteristics are extracted from the one or
more signals with feature extractors. Some or all of the extracted
characteristics may be transcutaneously transmitted back into the
communication unit 208, where a second stage of processing may be
performed on the characteristics, such as classifying of the
characteristics (and other signals) to characterize the subject's
propensity for the onset of a future neurological event. If
desired, to improve bandwidth, the classifier may be adapted to
allow for transmission or receipt of only the characteristics from
the subject communication assembly that are predictive for that
individual subject. Advantageously, because feature extractors may
be computationally expensive and energy hungry, it may be desirable
to have the feature extractors external to the body, where it is
easier to provide more processing and larger power sources.
[0073] For additional energy savings, the systems of the present
invention may also embody some of the energy saving concepts
described in commonly owned, copending patent application Ser. No.
______, entitled "Low Powered Device with Variable Scheduling,"
filed concurrently herewith (BNC Docket No. 6.00US), the complete
disclosure of which is incorporated herein by reference.
[0074] More complete descriptions of systems that may embody the
concepts of the present invention are described in commonly owned,
copending U.S. patent applications Ser. Nos. 11/321,897,
11/321,898, 11/322,150, all filed on Dec. 28, 2005, the complete
disclosures of which are incorporated herein by reference.
[0075] The inventive aspects described herein may be applicable to
commercial monitoring systems. For example, the systems herein may
be applied to the NeuroPace.RTM. RNS system. Such commercial
systems extract half-wave amplitude and duration, sum of absolute
differences as an approximation of signal curve length (which is in
turn a simplification of waveform fractal dimension), and a
modified sum of absolute amplitudes as an approximation of signal
energy. Instead of running all of the feature extractors
continuously all of the time, subsequent measurement and analysis
cycles may be scheduled based on analysis of previous measurement
cycles. In some embodiments, the measuring and analysis cycles are
run less often when the previous measurements indicates that it is
relatively unlikely that a seizure is imminent. On the other hand,
if previous measurements indicate that a seizure is relatively
likely to be proximate, the measurement and analysis cycles are run
more frequently, up to, possibly, some predetermined maximum rate.
Such a feature extractor configuration will preserve computation
power, reduce battery usage, and prolong the time between battery
changes.
[0076] The ability to provide long-term low-power ambulatory
measuring of physiological signals and prediction of neurological
events can facilitate improved treatment regimens for certain
neurological conditions. FIG. 8 depicts the typical course of
treatment for a subject with epilepsy. Because the occurrence of
neurological events 300 over time has been unpredictable, present
medical therapy relies on continuous prophylactic administration of
anti-epileptic drugs ("AEDs"). Constant doses 302 of one or more
AEDs are administered to a subject at regular time intervals with
the objective of maintaining relatively stable levels of the AEDs
within the subject. Maximum doses of the AEDs are limited by the
side effects of their chronic administration.
[0077] Reliable long-term essentially continuously operating
neurological event prediction systems would facilitate epilepsy
treatment. Therapeutic actions, such as, for example, brain
stimulation, peripheral nerve stimulation (e.g., vagus nerve
stimulation), cranial nerve stimulation (e.g., trigeminal nerve
stimulation ("TNS")), or targeted administration of AEDs, could be
directed by output from a neurological event prediction system. One
such course of treatment is depicted in FIG. 9. Relatively lower
constant doses 304 of one or more AEDs may be administered to a
subject at regular time intervals in addition to or as an
alternative to the prophylactic administration of AEDs.
Supplementary medication doses 306 are administered just prior to
an imminent neurological event 308. By targeting the supplementary
doses 306 at the appropriate times, neurological events may be more
effectively controlled and potentially eliminated 308, while
reducing side effects attendant with the chronic administration of
higher levels of the AEDs.
[0078] While the present disclosure has been described in
connection with various embodiments, illustrated in the various
figures, it is understood that similar aspects may be used or
modifications and additions may be made to the described aspects of
the disclosed embodiments for performing the same function of the
present disclosure without deviating therefrom. Other equivalent
mechanisms to the described aspects are also contemplated by the
teachings herein. Therefore, the present disclosure should not be
limited to any single aspect, but rather construed in breadth and
scope in accordance with the appended claims.
* * * * *