U.S. patent application number 12/880138 was filed with the patent office on 2010-12-30 for seizure sensing and detection using an implantable device.
This patent application is currently assigned to NEUROPACE, INC.. Invention is credited to Stephen T. Archer, Craig M. Baysinger, Barbara Gibb, Suresh Gurunathan, Bruce Kirkpatrick, Benjamin D. Pless, Thomas K. Tcheng.
Application Number | 20100331717 12/880138 |
Document ID | / |
Family ID | 25405615 |
Filed Date | 2010-12-30 |
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United States Patent
Application |
20100331717 |
Kind Code |
A1 |
Pless; Benjamin D. ; et
al. |
December 30, 2010 |
Seizure Sensing and Detection Using an Implantable Device
Abstract
A system and method for detecting and predicting neurological
events with an implantable device uses a relatively low-power
central processing unit in connection with signal processing
circuitry to identify features (including half waves) and calculate
window-based characteristics (including line lengths and areas
under the curve of the waveform) in an electrographic signal
received from a patient's brain. The features and window-based
characteristics are combinable in various ways according to the
invention to detect and predict neurological events in real time,
enabling responsive action by the implantable device.
Inventors: |
Pless; Benjamin D.;
(Atherton, CA) ; Archer; Stephen T.; (Sunnyvale,
CA) ; Baysinger; Craig M.; (Livermore, CA) ;
Gibb; Barbara; (Foster City, CA) ; Gurunathan;
Suresh; (Palo Alto, CA) ; Kirkpatrick; Bruce;
(Mountain View, CA) ; Tcheng; Thomas K.; (Pleasant
Hill, CA) |
Correspondence
Address: |
NEUROPACE, INC.
1375 SHOREBIRD WAY
MOUNTAIN VIEW
CA
94043
US
|
Assignee: |
NEUROPACE, INC.
Mountain View
CA
|
Family ID: |
25405615 |
Appl. No.: |
12/880138 |
Filed: |
September 12, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10973091 |
Oct 25, 2004 |
7813793 |
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12880138 |
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09896092 |
Jun 28, 2001 |
6810285 |
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10973091 |
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Current U.S.
Class: |
600/544 |
Current CPC
Class: |
A61B 5/4094 20130101;
A61B 5/7246 20130101; A61N 1/36064 20130101; A61B 5/291 20210101;
A61B 5/316 20210101; A61B 5/7264 20130101; A61B 5/7275 20130101;
A61B 5/369 20210101; A61B 5/7203 20130101; A61B 5/7242 20130101;
A61B 5/6868 20130101 |
Class at
Publication: |
600/544 |
International
Class: |
A61B 5/0476 20060101
A61B005/0476 |
Claims
1. A method for detecting a neurological event by analyzing an
electrical signal from a patient's brain with an implantable
device, the method comprising the steps of: receiving a first
electrical signal and a second electrical signal from a plurality
of electrodes; processing the electrical signals with a detection
subsystem to obtain a first detection channel output and a second
detection channel output; combining the first detection channel
output and the second detection channel output to obtain an event
detector output; and causing the implantable device to perform an
action in accordance with the event detector output.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This is a divisional of U.S. Ser. No. 10/973,091, filed Oct.
25, 2004, which is a continuation of U.S. patent application Ser.
No. 09/896,092, filed on Jun. 28, 2001 now U.S. Pat. No. 6,810,285.
U.S. Ser. No. 10/973,091 and U.S. Ser. No. 09/896,092 are hereby
incorporated by reference in the entirety.
FIELD OF THE INVENTION
[0002] The invention relates to systems and methods for detecting
and predicting neurological dysfunction characterized by abnormal
electrographic patterns, and more particularly to a system and
method for detecting and predicting epileptic seizures and their
onsets by analyzing electroencephalogram and electrocorticogram
signals with an implantable device.
BACKGROUND OF THE INVENTION
[0003] Epilepsy, a neurological disorder characterized by the
occurrence of seizures (specifically episodic impairment or loss of
consciousness, abnormal motor phenomena, psychic or sensory
disturbances, or the perturbation of the autonomic nervous system),
is debilitating to a great number of people. It is believed that as
many as two to four million Americans may suffer from various forms
of epilepsy. Research has found that its prevalence may be even
greater worldwide, particularly in less economically developed
nations, suggesting that the worldwide figure for epilepsy
sufferers may be in excess of one hundred million.
[0004] Because epilepsy is characterized by seizures, its sufferers
are frequently limited in the kinds of activities they may
participate in. Epilepsy can prevent people from driving, working,
or otherwise participating in much of what society has to offer.
Some epilepsy sufferers have serious seizures so frequently that
they are effectively incapacitated.
[0005] Furthermore, epilepsy is often progressive and can be
associated with degenerative disorders and conditions. Over time,
epileptic seizures often become more frequent and more serious, and
in particularly severe cases, are likely to lead to deterioration
of other brain functions (including cognitive function) as well as
physical impairments.
[0006] The current state of the art in treating neurological
disorders, particularly epilepsy, typically involves drug therapy
and surgery. The first approach is usually drug therapy.
[0007] A number of drugs are approved and available for treating
epilepsy, such as sodium valproate, phenobarbital/primidone,
ethosuximide, gabapentin, phenytoin, and carbamazepine, as well as
a number of others. Unfortunately, those drugs typically have
serious side effects, especially toxicity, and it is extremely
important in most cases to maintain a precise therapeutic serum
level to avoid breakthrough seizures (if the dosage is too low) or
toxic effects (if the dosage is too high). The need for patient
discipline is high, especially when a patient's drug regimen causes
unpleasant side effects the patient may wish to avoid.
[0008] Moreover, while many patients respond well to drug therapy
alone, a significant number (at least 20-30%) do not. For those
patients, surgery is presently the best-established and most viable
alternative course of treatment.
[0009] Currently practiced surgical approaches include radical
surgical resection such as hemispherectomy, corticectomy, lobectomy
and partial lobectomy, and less-radical lesionectomy, transection,
and stereotactic ablation. Besides being less than fully
successful, these surgical approaches generally have a high risk of
complications, and can often result in damage to eloquent (i.e.,
functionally important) brain regions and the consequent long-term
impairment of various cognitive and other neurological functions.
Furthermore, for a variety of reasons, such surgical treatments are
contraindicated in a substantial number of patients. And
unfortunately, even after radical brain surgery, many epilepsy
patients are still not seizure-free.
[0010] Electrical stimulation is an emerging therapy for treating
epilepsy. However, currently approved and available electrical
stimulation devices apply continuous electrical stimulation to
neural tissue surrounding or near implanted electrodes, and do not
perform any detection--they are not responsive to relevant
neurological conditions.
[0011] The NeuroCybernetic Prosthesis (NCP) from Cyberonics, for
example, applies continuous electrical stimulation to the patient's
vagus nerve. This approach has been found to reduce seizures by
about 50% in about 50% of patients. Unfortunately, a much greater
reduction in the incidence of seizures is needed to provide
clinical benefit. The Activa device from Medtronic is a pectorally
implanted continuous deep brain stimulator intended primarily to
treat Parkinson's disease. In operation, it supplies a continuous
electrical pulse stream to a selected deep brain structure where an
electrode has been implanted.
[0012] Continuous stimulation of deep brain structures for the
treatment of epilepsy has not met with consistent success. To be
effective in terminating seizures, it is believed that one
effective site where stimulation should be performed is near the
focus of the epileptogenic region. The focus is often in the
neocortex, where continuous stimulation may cause significant
neurological deficit with clinical symptoms including loss of
speech, sensory disorders, or involuntary motion. Accordingly,
research has been directed toward automatic responsive epilepsy
treatment based on a detection of imminent seizure.
[0013] A typical epilepsy patient experiences episodic attacks or
seizures, which are generally electrographically defined as periods
of abnormal neurological activity. As is traditional in the art,
such periods shall be referred to herein as "ictal".
[0014] Most prior work on the detection and responsive treatment of
seizures via electrical stimulation has focused on analysis of
electroencephalogram (EEG) and electrocorticogram (ECoG) waveforms.
In general, EEG signals represent aggregate neuronal activity
potentials detectable via electrodes applied to a patient's scalp.
ECoG signals, deep-brain counterparts to EEG signals, are
detectable via electrodes implanted on or under the dura mater, and
usually within the patient's brain. Unless the context clearly and
expressly indicates otherwise, the term "EEG" shall be used
generically herein to refer to both EEG and ECoG signals.
[0015] Much of the work on detection has focused on the use of
time-domain analysis of EEG signals. See, e.g., J. Gotman,
Automatic seizure detection: improvements and evaluation,
Electroencephalogr. Clin. Neurophysiol. 1990; 76(4): 317-24. In a
typical time-domain detection system, EEG signals are received by
one or more implanted electrodes and then processed by a control
module, which then is capable of performing an action
(intervention, warning, recording, etc.) when an abnormal event is
detected.
[0016] It is generally preferable to be able to detect and treat a
seizure at or near its beginning, or even before it begins. The
beginning of a seizure is referred to herein as an "onset."
However, it is important to note that there are two general
varieties of seizure onsets. A "clinical onset" represents the
beginning of a seizure as manifested through observable clinical
symptoms, such as involuntary muscle movements or
neurophysiological effects such as lack of responsiveness. An
"electrographic onset" refers to the beginning of detectable
electrographic activity indicative of a seizure. An electrographic
onset will frequently occur before the corresponding clinical
onset, enabling intervention before the patient suffers symptoms,
but that is not always the case. In addition, there are changes in
the EEG that occur seconds or even minutes before the
electrographic onset that can be identified and used to facilitate
intervention before electrographic or clinical onsets occur. This
capability would be considered seizure prediction, in contrast to
the detection of a seizure or its onset.
[0017] In the Gotman system, EEG waveforms are filtered and
decomposed into "features" representing characteristics of interest
in the waveforms. One such feature is characterized by the regular
occurrence (i.e., density) of half-waves exceeding a threshold
amplitude occurring in a specified frequency band between
approximately 3 Hz and 20 Hz, especially in comparison to
background (non-ictal) activity. When such half-waves are detected,
it is believed that seizure activity is occurring. For related
approaches, see also H. Qu and J. Gotman, A seizure warning system
for long term epilepsy monitoring, Neurology 1995; 45: 2250-4; and
H. Qu and J. Gotman, A Patient-Specific Algorithm for the Detection
of Seizure Onset in Long-Term EEG Monitoring: Possible Use as a
Warning Device, IEEE Trans. Biomed. Eng. 1997; 44(2): 115-22.
[0018] The Gotman articles address half wave characteristics in
general, and introduce a variety of measurement criteria, including
a ratio of current epoch amplitude to background; average current
epoch EEG frequency; average background EEG frequency; coefficient
of variation of wave duration; ratio of current epoch amplitude to
following time period; average wave amplitude; average wave
duration; dominant frequency (peak frequency of the dominant peak);
and average power in a main energy zone. These criteria are
variously mapped into an n-dimensional space, and whether a seizure
is detected depends on the vector distance between the parameters
of a measured segment of EEG and a seizure template in that
space.
[0019] It should be noted that the schemes set forth in the above
articles are not tailored for use in an implantable device, and
hence typically require more computational ability than would be
available in such a device.
[0020] U.S. Pat. No. 6,018,682 to Rise describes an implantable
seizure warning system that implements a form of the Gotman system.
However, the system described therein uses only a single detection
modality, namely a count of sharp spike and wave patterns within a
timer period. This is accomplished with relatively complex
processing, including averaging over time and quantifying sharpness
by way of a second derivative of the signal. The Rise patent does
not disclose how the signals are processed at a low level, nor does
it explain detection criteria in any sufficiently specific level of
detail.
[0021] A more computationally demanding approach is to transform
EEG signals into the frequency domain for rigorous spectrum
analysis. See, e.g., U.S. Pat. No. 5,995,868 to Dorfmeister et al.,
which analyzes the power spectral density of EEG signals in
comparison to background characteristics. Although this approach is
generally believed to achieve good results, for the most part, its
computational expense renders it less than optimal for use in
long-term implanted epilepsy monitor and treatment devices. With
current technology, the battery life in an implantable device
computationally capable of performing the Dorfmeister method would
be too short for it to be feasible.
[0022] Also representing an alternative and more complex approach
is U.S. Pat. No. 5,857,978 to Hively et al., in which various
non-linear and statistical characteristics of EEG signals are
analyzed to identify the onset of ictal activity. Once more, the
calculation of statistically relevant characteristics is not
believed to be feasible in an implantable device.
[0023] U.S. Pat. No. 6,016,449 to Fischell, et al. (which is hereby
incorporated by reference as though set forth in full herein),
describes an implantable seizure detection and treatment system. In
the Fischell system, various detection methods are possible, all of
which essentially rely upon the analysis (either in the time domain
or the frequency domain) of processed EEG signals. Fischell's
controller is preferably implanted intracranially, but other
approaches are also possible, including the use of an external
controller. When a seizure is detected, the Fischell system applies
responsive electrical stimulation to terminate the seizure, a
capability that will be discussed in further detail below.
[0024] All of these approaches provide useful information, and in
some cases may provide sufficient information for accurate
detection and prediction of most imminent epileptic seizures.
[0025] However, none of the various implementations of the known
approaches provide 100% seizure detection accuracy in a clinical
environment.
[0026] Two types of detection errors are generally possible. A
"false positive," as the term is used herein, refers to a detection
of a seizure or ictal activity when no seizure or other abnormal
event is actually occurring. Similarly, a "false negative" herein
refers to the failure to detect a seizure or ictal activity that
actually is occurring or shortly will occur.
[0027] In most cases, with all known implementations of the known
approaches to detecting abnormal seizure activity solely by
monitoring and analyzing EEG activity, when a seizure detection
algorithm is tuned to catch all seizures, there will be a
significant number of false positives. While it is currently
believed that there are minimal or no side effects to limited
amounts of over-stimulation (e.g., providing stimulation sufficient
to terminate a seizure in response to a false positive), the
possibility of accidentally initiating a seizure or increasing the
patient's susceptibility to seizures must be considered.
[0028] As is well known, it has been suggested that it is possible
to treat and terminate seizures by applying electrical stimulation
to the brain. See, e.g., U.S. Pat. No. 6,016,449 to Fischell et
al., and H. R. Wagner, et al., Suppression of cortical epileptiform
activity by generalized and localized ECoG desynchronization,
Electroencephalogr. Clin. Neurophysiol. 1975; 39(5): 499-506. And
as stated above, it is believed to be beneficial to perform this
stimulation only when a seizure (or other undesired neurological
event) is occurring or about to occur, as inappropriate stimulation
may result in the initiation of seizures.
[0029] Furthermore, it should be noted that a false negative (that
is, a seizure that occurs without any warning or treatment from the
device) will often cause the patient significant discomfort and
detriment. Clearly, false negatives are to be avoided.
[0030] It has been found to be difficult to achieve an acceptably
low level of false positives and false negatives with the level of
computational ability available in an implantable device with
reasonable battery life.
[0031] Preferably, the battery in an implantable device,
particularly one implanted intracranially, should last at least
several years. There is a substantial risk of complications (such
as infection, blood clots, and the overgrowth of scar tissue) and
lead failure each time an implanted device or its battery is
replaced. Rechargeable batteries have not been found to provide any
advantage in this regard, as they are not as efficient as
traditional cells, and the additional electronic circuitry required
to support the recharging operation contributes to the device's
size and complexity. Moreover, there is a need for patient
discipline in recharging the device batteries, which would require
the frequent transmission of a substantial amount of power over a
wireless link and through the patient's skin and other tissue.
[0032] As stated above, the detection and prediction of ictal
activity has traditionally required a significant amount of
computational ability. Moreover, for an implanted device to have
significant real-world utility, it is also advantageous to include
a number of other features and capabilities. Specifically,
treatment (via electrical stimulation or drug infusion) and/or
warning (via an audio annunciator, for example), recording of EEG
signals for later consideration and analysis, and telemetry
providing a link to external equipment are all useful capabilities
for an implanted device capable of detecting or predicting
epileptiform signals. All of these additional subsystems will
consume further power.
[0033] Moreover, size is also a consideration. For various reasons,
intracranial implants are favored. A device implanted
intracranially (or under the scalp) will typically have a lower
risk of failure than a similar device implanted pectorally or
elsewhere, which require a lead to be run from the device, through
the patient's neck to the electrode implantation sites in the
patient's head. This lead is also prone to receive additional
electromagnetic interference.
[0034] As is well known in the art, the computational ability of a
processor-controlled system is directly related to both size and
power consumption. In accordance with the above considerations,
therefore, it would be advantageous to have sufficient detection
and prediction capabilities to avoid a substantial number of false
positive and false negative detections, and yet consume little
enough power (in conjunction with the other subsystems) to enable
long battery life. Such an implantable device would have a
relatively low-power central processing unit to reduce the
electrical power consumed by that portion.
[0035] At the current time, there is no known implantable device
that is capable of detecting and predicting seizures and yet has
adequate battery life and the consequent acceptably low risk
factors for use in human patients.
SUMMARY OF THE INVENTION
[0036] Accordingly, an implantable device according to the
invention for detecting and predicting epileptic seizures includes
a relatively low-speed and low-power central processing unit, as
well as customized electronic circuit modules in a detection
subsystem. As described herein, the detection subsystem also
performs prediction, which in the context of the present
application is a form of detection that occurs before identifiable
clinical symptoms or even obvious electrographic patterns are
evident upon inspection. The same methods, potentially with
different parameters, are adapted to be used for both detection and
prediction. Generally, as described herein, an event (such as an
epileptic seizure) may be detected, an electrographic "onset" of
such an event (an electrographic indication of an event occurring
at the same time as or before the clinical event begins) may be
detected (and may be characterized by different waveform
observations than the event itself), and a "precursor" to an event
(electrographic activity regularly occurring some time before the
clinical event) may be detected as predictive of the event.
[0037] As described herein and as the terms are generally
understood, the present approach is generally not statistical or
stochastic in nature. The invention, and particularly the detection
subsystem thereof, is specifically adapted to perform much of the
signal processing and analysis requisite for accurate and effective
event detection. The central processing unit remains in a suspended
"sleep" state characterized by relative inactivity a substantial
percentage of the time, and is periodically awakened by interrupts
from the detection subsystem to perform certain tasks related to
the detection and prediction schemes enabled by the device.
[0038] Much of the processing performed by an implantable system
according to the invention involves operations on digital data in
the time domain. Preferably, to reduce the amount of data
processing required by the invention, samples at ten-bit resolution
are taken at a rate less than or equal to approximately 500 Hz (2
ms per sample).
[0039] As stated above, an implantable system according to the
invention is capable of accurate and reliable seizure detection and
prediction. To accomplish this, the invention employs a combination
of signal processing and analysis modalities, including data
reduction and feature extraction techniques, mostly implemented as
customized digital electronics modules, minimally reliant upon a
central processing unit.
[0040] In particular, it has been found to be advantageous to
utilize two different data reduction methodologies, both of which
collect data representative of EEG signals within a sequence of
uniform time windows each having a specified duration.
[0041] The first data reduction methodology involves the
calculation of a "line length function" for an EEG signal within a
time window. Specifically, the line length function of a digital
signal represents an accumulation of the sample-to-sample amplitude
variation in the EEG signal within the time window. Stated another
way, the line length function is representative of the variability
of the input signal. A constant input signal will have a line
length of zero (representative of substantially no variation in the
signal amplitude), while an input signal that oscillates between
extrema from sample to sample will approach the maximum line
length. It should be noted that while the line length function has
a physical-world analogue in measuring the vector distance traveled
in a graph of the input signal, the concept of line length as
treated herein disregards the horizontal (X) axis in such a
situation. The horizontal axis herein is representative of time,
which is not combinable in any meaningful way in accordance with
the invention with information relating to the vertical (Y) axis,
generally representative of amplitude, and which in any event would
contribute nothing of interest.
[0042] The second data reduction methodology involves the
calculation of an "area function" represented by an EEG signal
within a time window. Specifically, the area function is calculated
as an aggregation of the EEG's signal total deviation from zero
over the time window, whether positive or negative. The
mathematical analogue for the area function defined above is the
mathematical integral of the absolute value of the EEG function (as
both positive and negative signals contribute to positive area).
Once again, the horizontal axis (time) makes no contribution to
accumulated energy as treated herein. Accordingly, an input signal
that remains around zero will have a small area value, while an
input signal that remains around the most-positive or most-negative
values will have a high area value.
[0043] Both the area and line length functions may undergo linear
or non-linear transformations. An example would be to square each
amplitude before summing it in the area function. This non-linear
operation would provide an output that would approximate the energy
of the signal for the period of time it was integrated. Likewise
linear and non-linear transformations of the difference between
sample values are advantageous in customizing the line length
function to increase the effectiveness of the detector for a
specific patient.
[0044] The central processing unit receives the line length
function and area function measurements performed by the detection
subsystem, and is capable of acting based on those measurements or
their trends.
[0045] Feature extraction, specifically the identification of half
waves in an EEG signal, also provides useful information. A half
wave is an interval between a local waveform minimum and a local
waveform maximum; each time a signal "changes directions" (from
increasing to decreasing, or vice versa), subject to limitations
that will be set forth in further detail below, a new half wave is
identified.
[0046] The identification of half waves having specific amplitude
and duration criteria allows some frequency-driven characteristics
of the EEG signal to be considered and analyzed without the need
for computationally intensive transformations of normally
time-domain EEG signals into the frequency domain. Specifically,
the half wave feature extraction capability of the invention
identifies those half waves in the input signal having a duration
that exceeds a minimum duration criterion and an amplitude that
exceeds a minimum amplitude criterion. The number of half waves in
a time window meeting those criteria is somewhat representative of
the amount of energy in a waveform at a frequency below the
frequency corresponding to the minimum duration criterion. And the
number of half waves in a time window is constrained somewhat by
the duration of each half wave (i.e., if the half waves in a time
window have particularly long durations, relatively fewer of them
will fit into the time window), that number is highest when a
dominant waveform frequency most closely matches the frequency
corresponding to the minimum duration criterion.
[0047] As stated above, the half waves, line length function, and
area function of various EEG signals are calculated by customized
electronics modules with minimal involvement by the central
processing unit, and are selectively combined by a system according
to the invention to provide detection and prediction of seizure
activity, so that appropriate action can then be taken.
[0048] Accordingly, in one embodiment of the invention, a system
according to the invention includes a central processing unit, a
detection subsystem located therein that includes a waveform
analyzer. The waveform analyzer includes waveform feature analysis
capabilities (such as half wave characteristics) as well as
window-based analysis capabilities (such as line length and area
under the curve), and both aspects are combined to provide enhanced
neurological event detection. A central processing unit is used to
consolidate the results from multiple channels and coordinate
responsive action when necessary.
BRIEF DESCRIPTION OF THE DRAWINGS
[0049] These and other objects, features, and advantages of the
invention will become apparent from the detailed description below
and the accompanying drawings, in which:
[0050] FIG. 1 is a schematic illustration of a patient's head
showing the placement of an implantable neurostimulator according
to an embodiment of the invention;
[0051] FIG. 2 is a schematic illustration of a patient's cranium
showing the implantable neurostimulator of FIG. 1 as implanted,
including leads extending to the patient's brain;
[0052] FIG. 3 is a block diagram illustrating context in which an
implantable neurostimulator according to the invention is implanted
and operated;
[0053] FIG. 4 is a block diagram illustrating the major functional
subsystems of an implantable neurostimulator according to the
invention;
[0054] FIG. 5 is a block diagram illustrating the functional
components of the detection subsystem of the implantable
neurostimulator shown in FIG. 4;
[0055] FIG. 6 is a block diagram illustrating the functional
components of the sensing front end of the detection subsystem of
FIG. 5;
[0056] FIG. 7 is a block diagram illustrating the components of the
waveform analyzer of the detection subsystem of FIG. 5;
[0057] FIG. 8 is a block diagram illustrating the functional
arrangement of components of the waveform analysis of the detection
subsystem of FIG. 5 in one possible programmed embodiment of the
invention;
[0058] FIG. 9 is a graph of an exemplary EEG signal, illustrating
decomposition of the signal into time windows and samples;
[0059] FIG. 10 is a graph of the exemplary EEG signal of FIG. 9,
illustrating the extraction of half waves from the signal;
[0060] FIG. 11 is a flow chart illustrating the process performed
by hardware functional components of the waveform analyzer of FIG.
7 in extracting half waves as illustrated in FIG. 10;
[0061] FIG. 12 is a flow chart illustrating the process performed
by software in the central processing unit in extracting and
analyzing half waves from an EEG signal;
[0062] FIG. 13 is a flow chart illustrating the process performed
by software in the central processing unit in the application of an
X of Y criterion to half wave windows;
[0063] FIG. 14 is a graph of the exemplary EEG signal of FIG. 9,
illustrating the calculation of a line length function;
[0064] FIG. 15 is a flow chart illustrating the process performed
by hardware functional components of the waveform analyzer of FIG.
7 in calculating the line length function as illustrated in FIG.
14;
[0065] FIG. 16 is a flow chart illustrating the process performed
by software in the central processing unit in calculating and
analyzing the line length function of an EEG signal;
[0066] FIG. 17 is a graph of the exemplary EEG signal of FIG. 9,
illustrating the calculation of an area function;
[0067] FIG. 18 is a flow chart illustrating the process performed
by hardware functional components of the waveform analyzer of FIG.
7 in calculating the area function as illustrated in FIG. 17;
[0068] FIG. 19 is a flow chart illustrating the process performed
by software in the central processing unit in calculating and
analyzing the area function of an EEG signal;
[0069] FIG. 20 is a flow chart illustrating the process performed
by event-driven software in the central processing unit to analyze
half wave, line length, and area information for detection
according to the invention;
[0070] FIG. 21 is a flow chart illustrating the combination of
analysis tools into detection channels in an embodiment of the
invention; and
[0071] FIG. 22 is a flow chart illustrating the combination of
detection channels into event detectors in an embodiment of the
invention.
DETAILED DESCRIPTION OF THE INVENTION
[0072] The invention is described below, with reference to detailed
illustrative embodiments. It will be apparent that a system
according to the invention may be embodied in a wide variety of
forms. Consequently, the specific structural and functional details
disclosed herein are representative and do not limit the scope of
the invention.
[0073] FIG. 1 depicts an intracranially implanted device 110
according to the invention, which in one embodiment is a small
self-contained responsive neurostimulator. As the term is used
herein, a responsive neurostimulator is a device capable of
detecting or predicting ictal activity (or other neurological
events) and providing electrical stimulation to neural tissue in
response to that activity, where the electrical stimulation is
specifically intended to terminate the ictal activity, treat a
neurological event, prevent an unwanted neurological event from
occurring, or lessen the severity or frequency of certain symptoms
of a neurological disorder. As disclosed herein, the responsive
neurostimulator detects ictal activity by systems and methods
according to the invention.
[0074] Preferably, an implantable device according to the invention
is capable of detecting or predicting any kind of neurological
event that has a representative electrographic signature. While the
disclosed embodiment is described primarily as responsive to
epileptic seizures, it should be recognized that it is also
possible to respond to other types of neurological disorders, such
as movement disorders (e.g. the tremors characterizing Parkinson's
disease), migraine headaches, chronic pain, and neuropsychiatric
disorders such as depression. Preferably, neurological events
representing any or all of these afflictions can be detected when
they are actually occurring, in an onset stage, or as a predictive
precursor before clinical symptoms begin.
[0075] In the disclosed embodiment, the neurostimulator is
implanted intracranially in a patient's parietal bone 210, in a
location anterior to the lambdoidal suture 212 (see FIG. 2). It
should be noted, however, that the placement described and
illustrated herein is merely exemplary, and other locations and
configurations are also possible, in the cranium or elsewhere,
depending on the size and shape of the device and individual
patient needs, among other factors. The device 110 is preferably
configured to fit the contours of the patient's cranium 214. In an
alternative embodiment, the device 110 is implanted under the
patient's scalp 112 but external to the cranium; it is expected,
however, that this configuration would generally cause an
undesirable protrusion in the patient's scalp where the device is
located. In yet another alternative embodiment, when it is not
possible to implant the device intracranially, it may be implanted
pectorally (not shown), with leads extending through the patient's
neck and between the patient's cranium and scalp, as necessary.
[0076] It should be recognized that the embodiment of the device
110 described and illustrated herein is preferably a responsive
neurostimulator for detecting and treating epilepsy by detecting
seizures or their onsets or precursors, and preventing and/or
terminating such epileptic seizures.
[0077] In an alternative embodiment of the invention, the device
110 is not a responsive neurostimulator, but is an apparatus
capable of detecting neurological conditions and events and
performing actions in response thereto. The actions performed by
such an embodiment of the device 110 need not be therapeutic, but
may involve data recording or transmission, providing warnings to
the patient, or any of a number of known alternative actions. Such
a device will typically act as a diagnostic device when interfaced
with external equipment, as will be discussed in further detail
below.
[0078] The device 110, as implanted intracranially, is illustrated
in greater detail in FIG. 2. The device 110 is affixed in the
patient's cranium 214 by way of a ferrule 216. The ferrule 216 is a
structural member adapted to fit into a cranial opening, attach to
the cranium 214, and retain the device 110.
[0079] To implant the device 110, a craniotomy is performed in the
parietal bone anterior to the lambdoidal suture 212 to define an
opening 218 slightly larger than the device 110. The ferrule 216 is
inserted into the opening 218 and affixed to the cranium 214,
ensuring a tight and secure fit. The device 110 is then inserted
into and affixed to the ferrule 216.
[0080] As shown in FIG. 2, the device 110 includes a lead connector
220 adapted to receive one or more electrical leads, such as a
first lead 222. The lead connector 220 acts to physically secure
the lead 222 to the device 110, and facilitates electrical
connection between a conductor in the lead 222 coupling an
electrode to circuitry within the device 110. The lead connector
220 accomplishes this in a substantially fluid-tight environment
with biocompatible materials.
[0081] The lead 222, as illustrated, and other leads for use in a
system or method according to the invention, is a flexible
elongated member having one or more conductors. As shown, the lead
222 is coupled to the device 110 via the lead connector 220, and is
generally situated on the outer surface of the cranium 214 (and
under the patient's scalp 112), extending between the device 110
and a burr hole 224 or other cranial opening, where the lead 222
enters the cranium 214 and is coupled to a depth electrode (see
FIG. 4) implanted in a desired location in the patient's brain. If
the length of the lead 222 is substantially greater than the
distance between the device 110 and the burr hole 224, any excess
may be urged into a coil configuration under the scalp 112. As
described in U.S. Pat. No. 6,006,124 to Fischell, et al., which is
hereby incorporated by reference as though set forth in full
herein, the burr hole 224 is sealed after implantation to prevent
further movement of the lead 222; in an embodiment of the
invention, a burr hole cover apparatus is affixed to the cranium
214 at least partially within the burr hole 224 to provide this
functionality.
[0082] The device 110 includes a durable outer housing 226
fabricated from a biocompatible material. Titanium, which is light,
extremely strong, and biocompatible, is used in analogous devices,
such as cardiac pacemakers, and would serve advantageously in this
context. As the device 110 is self-contained, the housing 226
encloses a battery and any electronic circuitry necessary or
desirable to provide the functionality described herein, as well as
any other features. As will be described in further detail below, a
telemetry coil may be provided outside of the housing 226 (and
potentially integrated with the lead connector 220) to facilitate
communication between the device 110 and external devices.
[0083] The neurostimulator configuration described herein and
illustrated in FIG. 2 provides several advantages over alternative
designs. First, the self-contained nature of the neurostimulator
substantially decreases the need for access to the device 110,
allowing the patient to participate in normal life activities. Its
small size and intracranial placement causes a minimum of cosmetic
disfigurement. The device 110 will fit in an opening in the
patient's cranium, under the patient's scalp, with little
noticeable protrusion or bulge. The ferrule 216 used for
implantation allows the craniotomy to be performed and fit verified
without the possibility of breaking the device 110, and also
provides protection against the device 110 being pushed into the
brain under external pressure or impact. A further advantage is
that the ferrule 216 receives any cranial bone growth, so at
explant, the device 110 can be replaced without removing any bone
screws--only the fasteners retaining the device 110 in the ferrule
216 need be manipulated.
[0084] As stated above, and as illustrated in FIG. 3, a
neurostimulator according to the invention operates in conjunction
with external equipment. The device 110 is mostly autonomous
(particularly when performing its usual sensing, detection, and
stimulation capabilities), but preferably includes a selectable
part-time wireless link 310 to external equipment such as a
programmer 312. In the disclosed embodiment of the invention, the
wireless link 310 is established by moving a wand (or other
apparatus) having communication capabilities and coupled to the
programmer 312 into range of the device 110. The programmer 312 can
then be used to manually control the operation of the device 110,
as well as to transmit information to or receive information from
the device 110. Several specific capabilities and operations
performed by the programmer 312 in conjunction with the device 110
will be described in further detail below.
[0085] The programmer 312 is capable of performing a number of
advantageous operations in connection with the invention. In
particular, the programmer 312 is able to specify and set variable
parameters in the device 110 to adapt the function of the device
110 to meet the patient's needs, download or receive data
(including but not limited to stored EEG waveforms, parameters, or
logs of actions taken) from the device 110 to the programmer 312,
upload or transmit program code and other information from the
programmer 312 to the device 110, or command the device 110 to
perform specific actions or change modes as desired by a physician
operating the programmer 312. To facilitate these functions, the
programmer 312 is adapted to receive physician input 314 and
provide physician output 316; data is transmitted between the
programmer 312 and the device 110 over the wireless link 310.
[0086] The programmer 312 may be coupled via a communication link
318 to a network 320 such as the Internet. This allows any
information downloaded from the device 110, as well as any program
code or other information to be uploaded to the device 110, to be
stored in a database at one or more data repository locations
(which may include various servers and network-connected
programmers like the programmer 312). This would allow a patient
(and the patient's physician) to have access to important data,
including past treatment information and software updates,
essentially anywhere in the world that there is a programmer (like
the programmer 312) and a network connection.
[0087] An overall block diagram of the device 110 used for
measurement, detection, and treatment according to the invention is
illustrated in FIG. 4. Inside the housing 226 of the device 110 are
several subsystems making up a control module 410. The control
module 410 is capable of being coupled to a plurality of electrodes
412, 414, 416, and 418 (each of which may be connected to the
control module 410 via a lead that is analogous or identical to the
lead 222 of FIG. 2) for sensing and stimulation. In the illustrated
embodiment, the coupling is accomplished through the lead connector
220 (FIG. 2). Although four electrodes are shown in FIG. 4, it
should be recognized that any number is possible, and in the
embodiment described in detail below, eight electrodes are used. In
fact, it is possible to employ an embodiment of the invention that
uses a single lead with at least two electrodes, or two leads each
with a single electrode (or with a second electrode provided by a
conductive exterior portion of the housing 226 in one embodiment),
although bipolar sensing between two closely spaced electrodes on a
lead is preferred to minimize common mode signals including
noise.
[0088] The electrodes 412-418 are connected to an electrode
interface 420. Preferably, the electrode interface is capable of
selecting each electrode as required for sensing and stimulation;
accordingly the electrode interface is coupled to a detection
subsystem 422 and a stimulation subsystem 424. The electrode
interface also may provide any other features, capabilities, or
aspects, including but not limited to amplification, isolation, and
charge-balancing functions, that are required for a proper
interface with neurological tissue and not provided by any other
subsystem of the device 110.
[0089] The detection subsystem 422 includes an EEG analyzer
function. The EEG analyzer function is adapted to receive EEG
signals from the electrodes 412-418, through the electrode
interface 420, and to process those EEG signals to identify
neurological activity indicative of a seizure, an onset of a
seizure, or a precursor to a seizure. One way to implement such EEG
analysis functionality is disclosed in detail in U.S. Pat. No.
6,016,449 to Fischell et al., incorporated by reference above;
additional inventive methods are described in detail below. The
detection subsystem may optionally also contain further sensing and
detection capabilities, including but not limited to parameters
derived from other physiological conditions (such as
electrophysiological parameters, temperature, blood pressure,
etc.).
[0090] The stimulation subsystem 424 is capable of applying
electrical stimulation to neurological tissue through the
electrodes 412-418. This can be accomplished in any of a number of
different manners. For example, it may be advantageous in some
circumstances to provide stimulation in the form of a substantially
continuous stream of pulses, or on a scheduled basis. Preferably,
therapeutic stimulation is provided in response to abnormal events
detected by the EEG analyzer function of the detection subsystem
422. As illustrated in FIG. 4, the stimulation subsystem 424 and
the EEG analyzer function of the detection subsystem 422 are in
communication; this facilitates the ability of stimulation
subsystem 424 to provide responsive stimulation as well as an
ability of the detection subsystem 422 to blank the amplifiers
while stimulation is being performed to minimize stimulation
artifacts. It is contemplated that the parameters of the
stimulation signal (e.g., frequency, duration, waveform) provided
by the stimulation subsystem 424 would be specified by other
subsystems in the control module 410, as will be described in
further detail below.
[0091] Also in the control module 410 is a memory subsystem 426 and
a central processing unit (CPU) 428, which can take the form of a
microcontroller. The memory subsystem is coupled to the detection
subsystem 422 (e.g., for receiving and storing data representative
of sensed EEG signals and evoked responses), the stimulation
subsystem 424 (e.g., for providing stimulation waveform parameters
to the stimulation subsystem), and the CPU 428, which can control
the operation of the memory subsystem 426. In addition to the
memory subsystem 426, the CPU 428 is also connected to the
detection subsystem 422 and the stimulation subsystem 424 for
direct control of those subsystems.
[0092] Also provided in the control module 410, and coupled to the
memory subsystem 426 and the CPU 428, is a communication subsystem
430. The communication subsystem 430 enables communication between
the device 110 (FIG. 1) and the outside world, particularly the
external programmer 312 (FIG. 3). As set forth above, the disclosed
embodiment of the communication subsystem 430 includes a telemetry
coil (which may be situated outside of the housing 226) enabling
transmission and reception of signals, to or from an external
apparatus, via inductive coupling. Alternative embodiments of the
communication subsystem 430 could use an antenna for an RF link or
an audio transducer for an audio link.
[0093] Rounding out the subsystems in the control module 410 are a
power supply 432 and a clock supply 434. The power supply 432
supplies the voltages and currents necessary for each of the other
subsystems. The clock supply 434 supplies substantially all of the
other subsystems with any clock and timing signals necessary for
their operation.
[0094] It should be observed that while the memory subsystem 426 is
illustrated in FIG. 4 as a separate functional subsystem, the other
subsystems may also require various amounts of memory to perform
the functions described above and others. Furthermore, while the
control module 410 is preferably a single physical unit contained
within a single physical enclosure, namely the housing 226 (FIG.
2), it may comprise a plurality of spatially separate units each
performing a subset of the capabilities described above. Also, it
should be noted that the various functions and capabilities of the
subsystems described above may be performed by electronic hardware,
computer software (or firmware), or a combination thereof. The
division of work between the CPU 428 and the other functional
subsystems may also vary--the functional distinctions illustrated
in FIG. 4 may not reflect the integration of functions in a
real-world system or method according to the invention.
[0095] FIG. 5 illustrates details of the detection subsystem 422
(FIG. 4). Inputs from the electrodes 412-418 are on the left, and
connections to other subsystems are on the right.
[0096] Signals received from the electrodes 412-418 (as routed
through the electrode interface 420) are received in an electrode
selector 510. The electrode selector 510 allows the device to
select which electrodes (of the electrodes 412-418) should be
routed to which individual sensing channels of the detection
subsystem 422, based on commands received through a control
interface 518 from the memory subsystem 426 or the CPU 428 (FIG.
4). Preferably, each sensing channel of the detection subsystem 422
receives a bipolar signal representative of the difference in
electrical potential between two selectable electrodes.
Accordingly, the electrode selector 510 provides signals
corresponding to each pair of selected electrodes (of the
electrodes 412-418) to a sensing front end 512, which performs
amplification, analog to digital conversion, and multiplexing
functions on the signals in the sensing channels. The sensing front
end will be described further below in connection with FIG. 6.
[0097] A multiplexed input signal representative of all active
sensing channels is then fed from the sensing front end 512 to a
waveform analyzer 514. The waveform analyzer 514 is preferably a
special-purpose digital signal processor (DSP) adapted for use with
the invention, or in an alternative embodiment, may comprise a
programmable general-purpose DSP. In the disclosed embodiment, the
waveform analyzer has its own scratchpad memory area 516 used for
local storage of data and program variables when the signal
processing is being performed. In either case, the signal processor
performs suitable measurement and detection methods described
generally above and in greater detail below. Any results from such
methods, as well as any digitized signals intended for storage
transmission to external equipment, are passed to various other
subsystems of the control module 410, including the memory
subsystem 426 and the CPU 428 (FIG. 4) through a data interface
520. Similarly, the control interface 518 allows the waveform
analyzer 514 and the electrode selector 510 to be in communication
with the CPU 428.
[0098] Referring now to FIG. 6, the sensing front end 512 (FIG. 5)
is illustrated in further detail. As shown, the sensing front end
includes a plurality of differential amplifier channels 610, each
of which receives a selected pair of inputs from the electrode
selector 510. In a preferred embodiment of the invention, each of
differential amplifier channels 610 is adapted to receive or to
share inputs with one or more other differential amplifier channels
610 without adversely affecting the sensing and detection
capabilities of a system according to the invention. Specifically,
in an embodiment of the invention, there are at least eight
electrodes, which can be mapped separately to eight differential
amplifier channels 610 representing eight different sensing
channels and capable of individually processing eight bipolar
signals, each of which represents an electrical potential
difference between two monopolar input signals received from the
electrodes and applied to the sensing channels via the electrode
selector 510. For clarity, only five channels are illustrated in
FIG. 6, but it should be noted that any practical number of sensing
channels may be employed in a system according to the
invention.
[0099] Each differential amplifier channel 610 feeds a
corresponding analog to digital converter (ADC) 612. Preferably,
the analog to digital converters 612 are separately programmable
with respect to sample rates--in the disclosed embodiment, the ADCs
612 convert analog signals into 10-bit unsigned integer digital
data streams at a sample rate selectable between 250 Hz and 500 Hz.
In several of the illustrations described below where waveforms are
shown, sample rates of 250 Hz are typically used for simplicity.
However, the invention shall not be deemed to be so limited, and
numerous sample rate and resolution options are possible, with
tradeoffs known to individuals of ordinary skill in the art of
electronic signal processing. The resulting digital signals are
received by a multiplexer 614 that creates a single interleaved
digital data stream representative of the data from all active
sensing channels. As will be described in further detail below, not
all of the sensing channels need to be used at one time, and it may
in fact be advantageous in certain circumstances to deactivate
certain sensing channels to reduce the power consumed by a system
according to the invention.
[0100] It should be noted that as illustrated and described herein,
a "sensing channel" is not necessarily a single physical or
functional item that can be identified in any illustration. Rather,
a sensing channel is formed from the functional sequence of
operations described herein, and particularly represents a single
electrical signal received from any pair or combination of
electrodes, as preprocessed by a system according to the invention,
in both analog and digital forms. See, e.g., U.S. patent
application Ser. No. 09/517,797 to D. Fischell et al., filed on
Mar. 2, 2000 and entitled "Neurological Event Detection Using
Processed Display Channel Based Algorithms and Devices
Incorporating These Procedures," which is hereby incorporated by
reference as though set forth in full herein. At times
(particularly after the multiplexer 614), multiple sensing channels
are processed by the same physical and functional components of the
system; notwithstanding that, it should be recognized that unless
the description herein indicates to the contrary, a system
according to the invention processes, handles, and treats each
sensing channel independently.
[0101] The interleaved digital data stream is passed from the
multiplexer 614, out of the sensing front end 512, and into the
waveform analyzer 514. The waveform analyzer 514 is illustrated in
detail in FIG. 7.
[0102] The interleaved digital data stream representing information
from all of the active sensing channels is first received by a
channel controller 710. The channel controller applies information
from the active sensing channels to a number of wave morphology
analysis units 712 and window analysis units 714. It is preferred
to have as many wave morphology analysis units 712 and window
analysis units 714 as possible, consistent with the goals of
efficiency, size, and low power consumption necessary for an
implantable device. In a presently preferred embodiment of the
invention, there are sixteen wave morphology analysis units 712 and
eight window analysis units 714, each of which can receive data
from any of the sensing channels of the sensing front end 512, and
each of which can be operated with different and independent
parameters, including differing sample rates, as will be discussed
in further detail below.
[0103] Each of the wave morphology analysis units 712 operates to
extract certain feature information from an input waveform as
described below in conjunction with FIGS. 9-11. Similarly, each of
the window analysis units 714 performs certain data reduction and
signal analysis within time windows in the manner described in
conjunction with FIGS. 12-17. Output data from the various wave
morphology analysis units 712 and window analysis units 714 are
combined via event detector logic 716. The event detector logic 716
and the channel controller 710 are controlled by control commands
718 received from the control interface 518 (FIG. 5).
[0104] A "detection channel," as the term is used herein, refers to
a data stream including the active sensing front end 512 and the
analysis units of the waveform analyzer 514 processing that data
stream, in both analog and digital forms. It should be noted that
each detection channel can receive data from a single sensing
channel; each sensing channel preferably can be applied to the
input of any combination of detection channels. The latter
selection is accomplished by the channel controller 710. As with
the sensing channels, not all detection channels need to be active;
certain detection channels can be deactivated to save power or if
additional detection processing is deemed unnecessary in certain
applications.
[0105] In conjunction with the operation of the wave morphology
analysis units 712 and the window analysis units 714, a scratchpad
memory area 516 is provided for temporary storage of processed
data. The scratchpad memory area 516 may be physically part of the
memory subsystem 426, or alternatively may be provided for the
exclusive use of the waveform analyzer 514. Other subsystems and
components of a system according to the invention may also be
furnished with local scratchpad memory, if such a configuration is
advantageous.
[0106] The operation of the event detector logic 716 is illustrated
in detail in the functional block diagram of FIG. 8, in which four
exemplary sensing channels are analyzed by three illustrative event
detectors.
[0107] A first sensing channel 810 provides input to a first event
detector 812. While the first event detector 812 is illustrated as
a functional block in the block diagram of FIG. 8, it should be
recognized that it is a functional block only for purposes of
illustration, and may not have any physical counterpart in a device
according to the invention. Similarly, a second sensing channel 814
provides input to a second event detector 816, and a third input
channel 818 and a fourth input channel 820 both provide input to a
third event detector 822.
[0108] Considering the processing performed by the event detectors
812, 816, and 822, the first input channel 810 feeds a signal to
both a wave morphology analysis unit 824 (one of the wave
morphology analysis units 712 of FIG. 7) and a window analysis unit
826 (one of the window analysis units 714 of FIG. 7). The window
analysis unit 826, in turn, includes a line length analysis tool
828 and an area analysis tool 830. As will be discussed in detail
below, the line length analysis tool 828 and the area analysis tool
830 analyze different aspects of the signal from the first input
channel 810
[0109] Outputs from the wave morphology analysis unit 824, the line
length analysis tool 828, and the area analysis tool 830 are
combined in a Boolean AND operation 832 and sent to an output 834
for further use by a system according to the invention. For
example, if a combination of analysis tools in an event detector
identifies several simultaneous (or near-simultaneous) types of
activity in an input channel, a system according to the invention
may be programmed to perform an action in response thereto. Details
of the analysis tools and the combination processes used in event
detectors according to the invention will be set forth in greater
detail below.
[0110] In the second event detector 816, only a wave morphology
analysis unit 836 is active. Accordingly, no Boolean operation
needs to be performed, and the wave morphology analysis unit 836
directly feeds an event detector output 838.
[0111] The third event detector 822 operates on two input channels
818 and 820, and includes two separate detection channels of
analysis units: a first wave morphology analysis unit 840 and a
first window analysis unit 842, the latter including a first line
length analysis tool 844 and a first area analysis tool 846; and a
second wave morphology analysis unit 848 and a second window
analysis unit 850, the latter including a second line length
analysis tool 852 and a second area analysis tool 854. The two
detection channels of analysis units are combined to provide a
single event detector output 856.
[0112] In the first detection channel of analysis units 840 and
842, outputs from the first wave morphology analysis unit 840, the
first line length analysis tool 844, and the first area analysis
tool 846 are combined via a Boolean AND operation 858 into a first
detection channel output 860. Similarly, in the second detection
channel of analysis units 848 and 850, outputs from the second wave
morphology analysis unit 848, the second line length analysis tool
852, and the second area analysis tool 854 are combined via a
Boolean AND operation 862 into a second detection channel output
864. In the illustrated embodiment of the invention, the second
detection channel output 864 is invertible with selectable Boolean
logic inversion 866 before it is combined with the first detection
channel output 860. Subsequently, the first detection channel
output 860 and the second detection channel output 864 are combined
with a Boolean AND operation 868 to provide a signal to the output
856. In an alternative embodiment, a Boolean OR operation is used
to combine the first detection channel output 860 and the second
detection channel output 864.
[0113] In one embodiment of the invention, the second detection
channel (analysis units 848 and 850) represents a "qualifying
channel" with respect to the first detection channel (analysis
units 840 and 842). In general, a qualifying channel allows a
detection to be made only when both channels are in concurrence
with regard to detection of an event. For example, a qualifying
channel can be used to indicate when a seizure has "generalized,"
i.e. spread through a significant portion of a patient's brain. To
do this, the third input channel 818 and the fourth input channel
820 are configured to receive EEG waveforms from separate amplifier
channels coupled to electrodes in separate parts of the patient's
brain (e.g., in opposite hemispheres). Accordingly, then, the
Boolean AND operation 868 will indicate a detection only when the
first detection output 860 and the second detection output 864 both
indicate the presence of an event (or, when Boolean logic inversion
866 is present, when the first detection output 860 indicates the
presence of an event while the second detection output 864 does
not). As will be described in further detail below, the detection
outputs 860 and 864 can be provided with selectable persistence
(i.e., the ability to remain triggered for some time after the
event is detected), allowing the Boolean AND combination 868 to be
satisfied even when there is not precise temporal synchronization
between detections on the two channels.
[0114] It should be appreciated that the concept of a "qualifying
channel" allows the flexible configuration of a device 110
according to the invention to achieve a number of advantageous
results. In addition to the detection of generalization, as
described above, a qualifying channel can be configured, for
example, to detect noise so a detection output is valid only when
noise is not present, to assist in device configuration in
determining which of two sets of detection parameters is preferable
(by setting up the different parameters in the first detection
channel and the second detection channel, then replacing the
Boolean AND combination with a Boolean OR combination), or to
require a specific temporal sequence of detections (which would be
achieved in software by the CPU 428 after a Boolean OR combination
of detections). There are numerous other possibilities.
[0115] The outputs 834, 838, and 856 of the event detectors are
preferably represented by Boolean flags, and as described below,
provide information for the operation of a system according to the
invention.
[0116] While FIG. 8 illustrates four different sensing channels
providing input to four separate detection channels, it should be
noted that a maximally flexible embodiment of the present invention
would allow each sensing channel to be connected to one or more
detection channels. It may be advantageous to program the different
detection channels with different settings (e.g., thresholds) to
facilitate alternate "views" of the same sensing channel data
stream.
[0117] FIG. 9 illustrates three representative waveforms of the
type expected to be manipulated by a system according to the
invention. It should be noted, however, that the waveforms
illustrated in FIG. 9 are illustrative only, and are not intended
to represent any actual data. The first waveform 910 is
representative of an unprocessed electroencephalogram (EEG) or
electrocorticogram (ECoG) waveform having a substantial amount of
variability; the illustrated segment has a duration of
approximately 160 ms and a dominant frequency (visible as the
large-scale crests and valleys) of approximately 12.5 Hz. It will
be recognized that the first waveform is rather rough and peaky;
there is a substantial amount of high-frequency energy represented
therein.
[0118] The second waveform 912 represents a filtered version of the
original EEG waveform 910. As shown, most of the high-frequency
energy has been eliminated from the signal, and the waveform 912 is
significantly smoother. In the disclosed embodiment of the
invention, this filtering operation is performed in the sensing
front end 512 before the analog to digital converters 612 (FIG.
6).
[0119] The filtered waveform 912 is then sampled by one of the
analog to digital converters 612; this operation is represented
graphically in the third waveform 914 of FIG. 9. As illustrated, a
sample rate used in an embodiment of the invention is 250 Hz (4 ms
sample duration), resulting in approximately 40 samples over the
illustrated 160 ms segment. As is well known in the art of digital
signal processing, the amplitude resolution of each sample is
limited; in the disclosed embodiment, each sample is measured with
a resolution of 10 bits (or 1024 possible values). As is apparent
upon visual analysis of the third waveform, the dominant frequency
component has a wavelength of approximately 20 samples, which
corresponds to the dominant frequency of 12.5 Hz.
[0120] Referring now to FIG. 10, the processing of the wave
morphology analysis units 712 is described in conjunction with a
filtered and sampled waveform 1010 of the type illustrated as the
third waveform 914 of FIG. 9.
[0121] In a first half wave 1012, which is partially illustrated in
FIG. 10 (the starting point occurs before the illustrated waveform
segment 1010 begins), the waveform segment 1010 is essentially
monotonically decreasing, except for a small first perturbation
1014. Accordingly, the first half wave 1012 is represented by a
vector from the starting point (not shown) to a first local
extremum 1016, where the waveform starts to move in the opposite
direction. The first perturbation 1014 is of insufficient amplitude
to be considered a local extremum, and is disregarded by a
hysteresis mechanism (discussed in further detail below). A second
half wave 1018 extends between the first local extremum 1016 and a
second local extremum 1020. Again, a second perturbation 1022 is of
insufficient amplitude to be considered an extremum. Likewise, a
third half wave 1024 extends between the second local extremum 1020
and a third local extremum 1026; this may appear to be a small
perturbation, but is greater in amplitude than a selected
hysteresis threshold. The remaining half waves 1028, 1030, 1032,
1034, and 1036 are identified analogously. As will be discussed in
further detail below, each of the identified half waves 1012, 1018,
1024, 1028, 1030, 1032, 1034, and 1036 has a corresponding duration
1038, 1040, 1042, 1044, 1046, 1048, 1050, and 1052, respectively,
and analogously, a corresponding amplitude determined from the
relative positions of each half wave's starting point and ending
point along the vertical axis, and a slope direction, increasing or
decreasing.
[0122] In a method performed according to the invention, it is
particularly advantageous to allow for a programmable hysteresis
setting in identifying the ends of half waves. In other words, as
explained above, the end of an increasing or decreasing half wave
might be prematurely identified as a result of quantization (and
other) noise, low-amplitude signal components, and other perturbing
factors, unless a small hysteresis allowance is made before a
reversal of waveform direction (and a corresponding half wave end)
is identified. Hysteresis allows for insignificant variations in
signal level inconsistent with the signal's overall movement to be
ignored without the need for extensive further signal processing
such as filtering. Without hysteresis, such small and insignificant
variations might lead to substantial and gross changes in where
half waves are identified, leading to unpredictable results.
[0123] The processing steps performed with regard to the waveform
1010 and half waves of FIG. 10 are set forth in FIG. 11. The method
begins by identifying an increasing half wave (with an ending
amplitude higher than the starting amplitude, as in the second half
wave 1018 of FIG. 10). To do this, a variable corresponding to half
wave time is first initialized to zero (step 1110); then half wave
duration, ending threshold, peak amplitude, and first sample value
are all initialized (step 1112). Specifically, the half wave
duration value is set to zero; the peak amplitude and first sample
values are set to the amplitude value of the last observed sample,
which as described above is a value having 10-bit precision; and
the ending threshold is set to the last observed sample minus a
small preset hysteresis value. After waiting for a measurement of
the current EEG sample (step 1114), the half wave time and half
wave duration variables are incremented (step 1116). If the current
EEG sample has an amplitude greater than the peak amplitude (step
1118), then the amplitude of the half wave is increasing (or
continues to increase). Accordingly, the ending threshold is reset
to be the current EEG sample's amplitude minus the hysteresis
value, and the peak is reset to the current EEG sample's amplitude
(step 1120), and the next sample is awaited (step 1114).
[0124] If the current EEG sample has an amplitude less than the
ending threshold (step 1122), then the hysteresis value has been
exceeded, and a local extremum has been identified. Accordingly,
the end of the increasing half wave has been reached, and the
amplitude and duration of the half wave are calculated (step 1124).
The amplitude is equal to the peak amplitude minus the first sample
value; the duration is equal to the current half wave duration.
Otherwise, the next ample is awaited (step 1114).
[0125] If both the amplitude and the duration qualify by exceeding
corresponding preset thresholds (step 1126), then the amplitude,
duration, half wave time, half wave direction (increasing) are
stored in a buffer (step 1128), and the half wave time is reset to
zero (step 1130).
[0126] At the conclusion of the increasing half wave, the process
continues by initializing wave duration, the ending threshold, the
peak amplitude, and the first sample value (step 1132). Wave
duration is set to zero, the ending threshold is set to the last
sample value plus the hysteresis value, the peak amplitude and the
first sample value are set to the most recent sample value.
[0127] After waiting for a measurement of the current EEG sample
(step 1134), the half wave time and half wave duration variables
are incremented (step 1136). If the current EEG sample has an
amplitude lower than the peak amplitude (step 1138), then the
amplitude of the half wave is decreasing (or continues to
decrease). Accordingly, the ending threshold is reset to be the
current EEG sample's amplitude plus the hysteresis value, the peak
is reset to the current EEG sample's amplitude (step 1140), and the
next sample is awaited (step 1134).
[0128] If the current EEG sample has an amplitude greater than the
ending threshold (step 1142), then the hysteresis value has been
exceeded, and a local extremum has been identified. Accordingly,
the end of the decreasing half wave has been reached, and the
amplitude and duration of the half wave are calculated (step 1144).
The amplitude is equal to the first sample value minus the peak
amplitude, and the duration is equal to the current half wave
duration. Otherwise, the next EEG sample is awaited (step
1134).
[0129] If both the amplitude and the duration qualify by exceeding
corresponding preset thresholds (step 1146), then the amplitude,
duration, half wave time, half wave direction (decreasing) are
stored in a buffer (step 1148), and the half wave time is reset to
zero (step 1150). It should be noted that, in the context of this
specification, the term "exceed" in regard to a threshold value
means to meet a specified criterion. Generally, to exceed a
threshold herein is to have a numeric value greater than or equal
to the threshold, although other interpretations (such as greater
than, or less than, or less than or equal to, depending on the
context) may be applicable and are deemed to be within the scope of
the invention.
[0130] At the conclusion of the decreasing half wave, further half
waves are then identified by repeating the process from step 1112.
As half wave detection is an ongoing and continuous process, this
procedure preferably does not exit, but may be suspended from time
to time when conditions or device state call for it, e.g. when the
device is inactive or when stimulation is being performed. Once
suspended in accordance with the invention, the procedure should
recommence with the first initialization step 1110.
[0131] Accordingly, the process depicted in FIG. 11 stores
parameters corresponding to qualified half waves, including their
directions, durations, amplitudes, and the elapsed time between
adjacent qualified half waves (i.e. the half wave time variable).
In the disclosed embodiment of the invention, to reduce power
consumption, this procedure is performed in custom electronic
hardware; it should be clear that the operations of FIG. 11 are
performed in parallel for each active instance of the wave
morphology analysis units 712 (FIG. 7). It should also be noted,
however, that certain software can also be used to advantageous
effect in this context.
[0132] This stored information is used in the software process
illustrated in FIG. 12, which is performed on a periodic basis,
preferably once every processing window (a recurring time interval
that is either fixed or programmable) by a system according to the
invention. Consistent with the other analysis tools described
herein, the duration of an exemplary processing window is in one
embodiment of the invention 128 ms, which corresponds to 32 samples
at a 250 Hz sampling rate.
[0133] Each time the software process of FIG. 12 is invoked, the
half wave window flag is first cleared (step 1210). Any qualified
half waves identified by the process set forth in FIG. 11 that are
newly identified since the last invocation of the procedure (i.e.,
all qualified half waves that ended within the preceding processing
window) are identified (step 1212). A "current half wave" pointer
is set to point to the oldest qualified half wave identified in the
most recent processing window (step 1214). The time interval
between the current half wave and the prior x half waves is then
measured (step 1216), where x is a specified minimum number of half
waves (preferably a programmable value) to be identified within a
selected half wave time window (the duration of which is another
programmable value) to result in the possible detection of a
neurological event. If the time interval is less than the duration
of the half wave time window (step 1218), then the half wave window
flag is set (step 1220), logic inversion is selectively applied
(step 1222), and the procedure ends (step 1224). Logic inversion, a
mechanism for determining whether an analysis unit is triggered by
the presence or absence of a condition, is explained in greater
detail below. Otherwise, the current half wave pointer is
incremented to point to the next new half wave (step 1228), and if
there are no more new half waves (step 1230), logic inversion is
applied if desired (step 1222), and the procedure ends (step 1224).
Otherwise, the next time interval is tested (step 1216) and the
process continues from there.
[0134] Logic inversion allows the output flag for the wave
morphology analysis unit (or any other analyzer) to be selectively
inverted. If logic inversion is configured to be applied to an
output of a particular analysis unit, then the corresponding flag
will be clear when the detection criterion (e.g., number of
qualified half waves) is met, and set when the detection criterion
is not met. This capability provides some additional flexibility in
configuration, facilitating detection of the absence of certain
signal characteristics when, for example, the presence of those
characteristics is the norm.
[0135] In a preferred embodiment of the invention, the half wave
window flag (set in step 1220) indicates whether a sufficient
number of qualified half waves occur over an interval ending in the
most recent processing window. To reduce the occurrence of spurious
detections, an X of Y criterion is applied, causing the wave
morphology analysis unit to trigger only if a sufficient number of
qualified half waves occur in X of the Y most recent processing
windows, where X and Y are parameters individually adjustable for
each analysis tool. This process is illustrated in FIG. 13.
[0136] Initially, a sum (representing recent processing windows
having the half wave window flag set) is cleared to zero and a
current window pointer is initialized to point to the most recent
processing window (step 1310). If the half wave window flag
corresponding to the current window pointer is set (step 1312),
then the sum is incremented (step 1314). If there are more
processing windows to examine (for an X of Y criterion, a total of
Y processing windows, including the most recent, should be
considered) (step 1316), then the window pointer is decremented
(step 1318) and the flag testing and sum incrementing steps (steps
1312-1314) are repeated.
[0137] After Y windows have been considered, if the sum of windows
having set half wave window flags meets the threshold X (step
1320), then the half wave analysis flag is set (step 1322),
persistence (described below) is applied (step 1324), and the
procedure is complete. Otherwise, the half wave analysis flag is
cleared (step 1326).
[0138] Persistence, another per-analysis-tool setting, allows the
effect of an event detection (a flag set) to persist beyond the end
of the detection window in which the event occurs. In the disclosed
system according to the invention, persistence may be set anywhere
from one second to fifteen seconds (though other settings are
possible), so if detections with multiple analysis tools do not all
occur simultaneously (though they should still occur within a
fairly short time period), a Boolean combination of flags will
still yield positive results. Persistence can also be used with a
single analysis tool to smooth the results.
[0139] When the process of FIG. 13 is completed, the half wave
analysis flag (set or cleared in steps 1322 and 1326, respectively)
indicates whether an event has been detected in the corresponding
channel of the wave morphology analysis units 712, or stated
another way, whether a sufficient number of qualified half waves
have appeared in X of the Y most recent processing windows.
Although in the disclosed embodiment, the steps of FIGS. 12 and 13
are performed in software, it should be recognized that some or all
of those steps can be performed using custom electronics, if it
proves advantageous in the desired application to use such a
configuration.
[0140] FIG. 14 illustrates the waveform of FIG. 9, further
depicting line lengths identified within a time window. The time
window used with respect to FIGS. 14-16 may be different from the
half wave processing window described above in connection with
FIGS. 12-13, but in a preferred embodiment, refers to the same time
intervals. From an implementation standpoint, a single device
interrupt upon the conclusion of each processing window allows all
of the analysis tools to perform the necessary corresponding
software processes; the line length analysis process of FIG. 16
(described below) is one such example. A waveform 1410 is a
filtered and otherwise pre-processed EEG signal as received in one
of the window analysis units 714 from the sensing front end 512. As
discussed above, line lengths are considered within time windows.
As illustrated in FIG. 14, the duration of an exemplary window 1412
is 32 samples, which is equivalent to 128 ms at a 250 Hz sampling
rate.
[0141] The total line length for the window 1412 is the sum of the
sample-to-sample amplitude differences within that window 1412. For
example, the first contribution to the line length within the
window 1412 is a first amplitude difference 1414 between a previous
sample 1416 occurring immediately before the window 1412 and a
first sample 1418 occurring within the window 1412. The next
contribution comes from a second amplitude difference 1420 between
the first sample 1418 and a second sample 1422; a further
contribution 1424 comes from a third amplitude difference between
the second sample 1422 and a third sample 1426; and so on. At the
end of the window 1412, the final contribution to the line length
comes from a last amplitude difference 1430 between a second-last
sample 1432 in the window 1412 and a last sample 1434 in the window
1412. Note that all line lengths, whether increasing or decreasing
in direction, are accumulated as positive values by the invention;
accordingly, a decreasing amplitude difference 1414 and an
increasing amplitude difference 1428 both contribute to a greater
line length.
[0142] As illustrated herein, and as discussed in detail above,
there are thirty-two samples within the window 1412. The
illustrated window 1412 has a duration of 128 ms, and accordingly,
the illustrated sample rate is 250 Hz. It should be noted, however,
that alternate window durations and sample rates are possible and
considered to be within the scope of the present invention.
[0143] The line lengths illustrated in FIG. 14 are calculated as
shown by the flow chart of FIG. 15, which is invoked at the
beginning of a time window. Initially, a line length total variable
is initialized to zero (step 1510). The current sample is awaited
(step 1512), and the absolute value of the amplitude difference
between the current sample and the previous sample (which, when
considering the first sample in a window, may come from the last
sample in a previous window) is measured (step 1514).
[0144] In various alternative embodiments of the invention, either
the measured difference (as calculated in step 1514, described
above), or the sample values used to calculate the difference may
be mathematically transformed in useful nonlinear ways. For
example, it may be advantageous in certain circumstances to
calculate the difference between adjacent samples using the squares
of the sample values, or to calculate the square of the difference
between sample values, or both. It is contemplated that other
transformations (such as square root, exponentiation, logarithm,
and other nonlinear functions) might also be advantageous in
certain circumstances. Whether or not to perform such a
transformation and the nature of any transformation to be performed
are preferably programmable parameters of the device 110.
[0145] For use in the next iteration, the previous sample is
replaced with the value of the current sample (step 1516), and the
calculated absolute value is added to the total (step 1518). If
there are more samples remaining in the window 1412 (step 1520),
another current sample is awaited (step 1512) and the process
continues. Otherwise, the line length calculation for the window
1412 is complete, and the total is stored (step 1522), the total is
re-initialized to zero (step 1510), and the process continues.
[0146] As with the half wave analysis method set forth above, the
line length calculation does not need to terminate; it can be
free-running yet interruptible. If the line length calculation is
restarted after having been suspended, it should be re-initialized
and restarted at the beginning of a window. This synchronization
can be accomplished through hardware interrupts.
[0147] The line lengths calculated as shown in FIG. 15 are then
processed as indicated in the flow chart of FIG. 16, which is
performed after each window 1412 is calculated and stored (step
1522).
[0148] The process begins by calculating a running accumulated line
length total over a period of n time windows. Where n>1, the
effect is that of a sliding window; in an alternative embodiment an
actual sliding window processing methodology may be used. First,
the accumulated total is initialized to zero (step 1610). A current
window pointer is set to indicate the n.sup.th-last window, i.e.,
the window (n-1) windows before the most recent window (step 1612).
The line length of the current window is added to the total (step
1614), the current window pointer is incremented (step 1616), and
if there are more windows between the current window pointer and
the most recent (last) window (step 1618), the adding and
incrementing steps (1614-1616) are repeated. Accordingly, by this
process, the resulting total includes the line lengths for each of
the n most recent windows.
[0149] In the disclosed embodiment of the invention, the
accumulated total line length is compared to a dynamic threshold,
which is based on a trend of recently observed line lengths. The
trend is recalculated regularly and periodically, after each
recurring line length trend interval (which is preferably a fixed
or programmed time interval). Each time the line length trend
interval passes (step 1620), the line length trend is calculated or
updated (step 1622). In a presently preferred embodiment of the
invention, this is accomplished by calculating a normalized moving
average of several trend samples, each of which represents several
consecutive windows of line lengths. A new trend sample is taken
and the moving average is recalculated upon every line length trend
interval. The number of trend samples used in the normalized moving
average and the number of consecutive windows of line length
measurements per trend sample are preferably both fixed or
programmable values.
[0150] After the line length trend has been calculated, the line
length threshold is calculated (step 1624) based on the new line
length trend. In the disclosed embodiment of the invention, the
threshold may be set as either a percentage of the line length
trend (either below 100% for a threshold that is lower than the
trend, or above 100% for a threshold that is higher than the trend)
or alternatively a fixed numeric offset from the line length trend
(either negative for a threshold that is lower than the trend, or
positive for a threshold that is higher than the trend). It should
be observed that other methods for deriving a numeric threshold
from a numeric trend are possible and deemed to be within the scope
of the invention.
[0151] The first time the process of FIG. 16 is performed, there is
generally no line length trend against which to set a threshold.
Accordingly, for the first several passes through the process
(until a sufficient amount of EEG data has been processed to
establish a trend), the threshold is essentially undefined and the
line length detector should not return a positive detection. Some
"settling time" is required to establish trends and thresholds
before a detection can be made.
[0152] If the accumulated line length total exceeds the calculated
threshold (step 1626), then a flag is set (step 1628) indicating a
line-length-based event detection on the current window analysis
unit channel 714. As described above, in the disclosed embodiment
of the invention, the threshold is dynamically calculated from a
line length trend, but alternatively, the threshold may be static,
either fixed or programmed into the device 110. If the accumulated
line length total does not exceed the threshold, the flag is
cleared (step 1630). Once the line length flag has been either set
or cleared, logic inversion is applied (step 1632), persistence is
applied (step 1634), and the procedure terminates.
[0153] The resulting persistent line length flag indicates whether
the threshold has been exceeded within one or more windows over a
time period corresponding to the line length flag persistence. As
will be discussed in further detail below, line length event
detections can be combined with the half wave event detections, as
well as any other applicable detection criteria according to the
invention.
[0154] FIG. 17 illustrates the waveform of FIG. 9 with area under
the curve identified within a window. Area under the curve, which
in some circumstances is somewhat representative of a signal's
energy (though energy of a waveform is more accurately represented
by the area under the square of a waveform), is another detection
criterion in accordance with the invention.
[0155] The total area under the curve represented by a waveform
1710 within the window 1712 is equal to the sum of the absolute
values of the areas of each rectangular region of unit width
vertically bounded by the horizontal axis and the sample. For
example, the first contribution to the area under the curve within
the window 1712 comes from a first region 1714 between a first
sample 1716 and a baseline 1717. A second contribution to the area
under the curve within the window 1712 comes from a second region
1718, including areas between a second sample 1720 and the baseline
1717. There are similar regions and contributions for a third
sample 1722 and the baseline 1717, a fourth sample 1724 and the
baseline 1717, and so on. It should be observed that the region
widths are not important--the area under each sample can be
considered the product of the sample's amplitude and a unit width,
which can be disregarded. In a similar manner, each region is
accumulated and added to the total area under the curve within the
window 1712. Although the concept of separate rectangular regions
is a useful construct for visualizing the idea of area under a
curve, it should be noted that a process for calculating area need
not partition areas into regions as shown in FIG. 17--it is only
necessary to accumulate the absolute value of the waveform's
amplitude at each sample, as the unit width of each region can be
disregarded. The process for doing this will be set forth in detail
below in connection with FIG. 18.
[0156] The areas under the curve illustrated in FIG. 17 are
calculated as shown by the flow chart of FIG. 18, which is invoked
at the beginning of a time window. Initially, an area total
variable is initialized to zero (step 1810). The current sample is
awaited (step 1812), and the absolute value of the current sample
is measured (step 1814).
[0157] As with the line length calculation method described above
(with reference to FIG. 15), in various alternative embodiments of
the invention, the current sample (as measured in step 1814,
described above) may be mathematically transformed in useful
nonlinear ways. For example, it may be advantageous in certain
circumstances to calculate the square of the current sample rather
than its absolute value. The result of such a transformation by
squaring each sample will generally be more representative of
signal energy, though it is contemplated that other transformations
(such as square root, exponentiation, logarithm, and other
nonlinear functions) might also be advantageous in certain
circumstances. Whether or not to perform such a transformation and
the nature of any transformation to be performed are preferably
programmable parameters of the device 110.
[0158] The calculated absolute value is added to the total (step
1816). If there are more samples remaining in the window 1712 (step
1818), another current sample is awaited (step 1812) and the
process continues. Otherwise, the area calculation for the window
1712 is complete, and the total is stored (step 1820), the total is
re-initialized to zero (step 1810), and the process continues.
[0159] As with the half wave and line length analysis methods set
forth above, the area calculation does not need to terminate; it
can be free-running yet interruptible. If the area calculation is
restarted after having been suspended, it should be re-initialized
and restarted at the beginning of a window. This synchronization
can be accomplished through hardware interrupts.
[0160] The line lengths calculated as shown in FIG. 18 are then
processed as indicated in the flow chart of FIG. 19, which is
performed after each window 1712 is calculated and stored (step
1820).
[0161] The process begins by calculating a running accumulated area
total over a period of n time windows. Where n>1, the effect is
that of a sliding window; in an alternative embodiment an actual
sliding window processing methodology may be used. First, the
accumulated total is initialized to zero (step 1910). A current
window pointer is set to indicate the n.sup.th-last window, i.e.,
the window (n-1) windows before the most recent window (step 1912).
The area for the current window is added to the total (step 1914),
the current window pointer is incremented (step 1916), and if there
are more windows between the current window and the most recent
(last) window (step 1918), the adding and incrementing steps
(1914-1916) are repeated. Accordingly, by this process, the
resulting total includes the areas under the curve for each of the
n most recent windows.
[0162] In the disclosed embodiment of the invention, the
accumulated total area is compared to a dynamic threshold, which is
based on a trend of recently observed areas. The trend is
recalculated regularly and periodically, after each recurring area
trend interval (which is preferably a fixed or programmed time
interval). Each time the area trend interval passes (step 1920),
the area trend is calculated or updated (step 1922). In a presently
preferred embodiment of the invention, this is accomplished by
calculating a normalized moving average of several trend samples,
each of which represents several consecutive windows of areas. A
new trend sample is taken and the moving average is recalculated
upon every area trend interval. The number of trend samples used in
the normalized moving average and the number of consecutive windows
of area measurements per trend sample are preferably both fixed or
programmable values.
[0163] After the area trend has been calculated, the area threshold
is calculated (step 1924) based on the new area trend. As with line
length, discussed above, the threshold may be set as either a
percentage of the area trend (either below 100% for a threshold
that is lower than the trend, or above 100% for a threshold that is
higher than the trend) or alternatively a fixed numeric offset from
the area trend (either negative for a threshold that is lower than
the trend, or positive for a threshold that is higher than the
trend).
[0164] The first time the process of FIG. 19 is performed, there is
generally no area trend against which to set a threshold.
Accordingly, for the first several passes through the process
(until a sufficient amount of EEG data has been processed to
establish a trend), the threshold is essentially undefined and the
area detector should not return a positive detection. Some
"settling time" is required to establish trends and thresholds
before a detection can be made.
[0165] If the accumulated total exceeds the calculated threshold
(step 1926), then a flag is set (step 1928) indicating an
area-based event detection on the current window analysis unit
channel 714. Otherwise, the flag is cleared (step 1930). Once the
area flag has been either set or cleared, logic inversion is
applied (step 1932), persistence is applied (step 1934), and the
procedure terminates.
[0166] The resulting persistent area flag indicates whether the
threshold has been exceeded within one or more windows over a time
period corresponding to the area flag persistence. As will be
discussed in further detail below, area event detections can be
combined with the half wave event detections, line length event
detections, as well as any other applicable detection criteria
according to the invention.
[0167] In a preferred embodiment of the invention, each threshold
for each channel and each analysis tool can be programmed
separately; accordingly, a large number of individual thresholds
may be used in a system according to the invention. It should be
noted thresholds can vary widely; they can be updated by a
physician via the external programmer 312 (FIG. 3), and some
analysis tool thresholds (e.g., line length and area) can also be
automatically varied depending on observed trends in the data. This
is preferably accomplished based on a moving average of a specified
number of window observations of line length or area, adjusted as
desired via a fixed offset or percentage offset, and may compensate
to some extent for diurnal and other normal variations in brain
electrophysiological parameters.
[0168] With regard to the flow charts of FIGS. 11-13, 15-16, and
18-19, it should be noted that there can be a variety of ways these
processes are implemented. For example, state machines, software,
hardware (including ASICs, FPGAs, and other custom electronics),
and various combinations of software and hardware, are all
solutions that would be possible to practitioners of ordinary skill
in the art of electronics and systems design. It should further be
noted that the steps performed in software need not be, as some of
them can be implemented in hardware, if desired, to further reduce
computational load on the processor. In the context of the
invention, it is not believed to be advantageous to have the
software perform additional steps, as that would likely increase
power consumption.
[0169] In an embodiment of the invention, one of the detection
schemes set forth above (e.g., half wave detection) is adapted to
use an X of Y criterion to weed out spurious detections. This can
be implemented via a shift register, as usual, or by more efficient
computational methods. As described above, half waves are analyzed
on a window-by-window basis, and as described above (in connection
with FIG. 13), the window results are updated on a separate
analysis window interval. If the detection criterion (i.e., a
certain number of half waves in less than a specified time period)
is met for any of the half waves occurring in the most recent
window, then detection is satisfied within that window. If that
occurs for at least X of the Y most recent windows, then the half
wave analysis tool triggers a detection. If desired, other
detection algorithms (such as line length and area) may operate in
much the same way: if thresholds are exceeded in at least X of the
Y most recent windows, then the corresponding analysis tool
triggers a detection.
[0170] Also, in the disclosed embodiment, each detection flag,
after being set, remains set for a selected amount of time,
allowing them to be combined by Boolean logic (as described below)
without necessarily being simultaneous.
[0171] As indicated above, each of the software processes set forth
above (FIGS. 12-13, 16, and 19) correspond to functions performed
by the wave morphology analysis units 712 and window analysis units
714. Each one is initiated periodically, typically once per
detection window (1212, 1512). The outputs from the half wave and
window analysis units 712 and 714, namely the flags generated in
response to counted qualified half waves, accumulated line lengths,
and accumulated areas are combined to identify event detections as
functionally illustrated in FIG. 8 and as described via flow chart
in FIG. 20.
[0172] The process begins with the receipt of a timer interrupt
(step 2010), which is typically generated on a regular periodic
basis to indicate the edges of successive time windows.
Accordingly, in a system or method according to the disclosed
embodiment of the invention, such a timer interrupt is received
every 128 ms, or as otherwise programmed or designed. Then the half
wave (step 2012, FIGS. 12-13), line length (step 2014, FIG. 16),
and area (step 2016, FIG. 19) analysis tools are evaluated with
respect to the latest data generated thereby, via the half wave
analysis flag, the line length flag, and the area flag for each
active channel. The steps of checking the analysis tools (steps
2012, 2014, and 2016) can be performed in any desired order or in
parallel, as they are generally not interdependent. It should be
noted that the foregoing analysis tools should be checked for every
active channel, and may be skipped for inactive detection
channels.
[0173] Flags, indicating whether particular signal characteristics
have been identified in each active channel, for each active
analysis tools, are then combined into detection channels (step
2018) as illustrated in FIG. 8. In the disclosed embodiment of the
invention, this operation is performed as described in detail below
with reference to FIG. 21. Each detection channel is a Boolean AND
combination of analysis tool flags for a single channel, and as
disclosed above, there are preferably at least eight channels in a
system according to the invention.
[0174] The flags for multiple detection channels are then combined
into event detector flags (step 2020), which are indicative of
identified neurological events calling for action by the device.
This process is described below, see FIG. 20, and is in general a
Boolean combination of detection channels, if there is more than
one channel per event detector.
[0175] If an event detector flag is set (step 2022), then a
corresponding action is initiated (step 2024) by the device.
Actions according to the invention can include the presentation of
a warning to the patient, an application of therapeutic electrical
stimulation, a delivery of a dose of a drug, an initiation of a
device mode change, or a recording of certain EEG signals; it will
be appreciated that there are numerous other possibilities. It is
preferred, but not necessary, for actions initiated by a device
according to the invention to be performed in parallel with the
sensing and detection operations described in detail herein. It
should be recognized that the application of electrical stimulation
to the brain may require suspension of certain of the sensing and
detection operations, as electrical stimulation signals may
otherwise feed back into the detection system 422 (FIG. 4), causing
undesirable results and signal artifacts.
[0176] Multiple event detector flags are possible, each one
representing a different combination of detection channel flags. If
there are further event detector flags to consider (step 2026),
those event detector flags are also evaluated (step 2022) and may
cause further actions by the device (step 2024). It should be noted
that, in general, actions performed by the device (as in step 2024)
may be in part dependent on a device state--even if certain
combinations of events do occur, no action may be taken if the
device is in an inactive state, for example.
[0177] As described above, and as illustrated in FIG. 20 as step
2018, a corresponding set of analysis tool flags is combined into a
detection channel flag as shown in FIG. 21 (see also FIG. 8).
Initially the output detection channel flag is set (step 2110).
Beginning with the first analysis tool for a particular detection
channel (step 2112), if the corresponding analysis tool flag is not
set (step 2114), then the output detection channel flag is cleared
(step 2116).
[0178] If the corresponding analysis tool flag is set (step 2114),
the output detection channel flag remains set, and further analysis
tools for the same channel, if any (step 2118), are evaluated.
Accordingly, this combination procedure operates as a Boolean AND
operation--if any of the enabled and active analysis tools for a
particular detection channel does not have a set output flag, then
no detection channel flag is output by the procedure.
[0179] A clear analysis tool flag indicates that no detection has
been made within the flag persistence period, and for those
analysis tools that employ an X of Y criterion, that such criterion
has not been met. In certain circumstances, it may be advantageous
to also provide detection channel flags with logic inversion. Where
a desired criterion (i.e., combination of analysis tools) is not
met, the output flag is set (rather than cleared, which is the
default action). This can be accomplished by providing selectable
Boolean logic inversion (step 2120) corresponding to each event
detector.
[0180] Also as described above, and as illustrated in FIG. 20 as
step 2020, multiple detection channel flags are combined into a
single event detector flag as shown in FIG. 22 (see also FIG. 8).
Initially the output event detector flag is set (step 2210).
Beginning with the first detection channel for a particular event
detector (step 2212), if the channel is not enabled (step 2214),
then no check is made. If the channel is enabled and the
corresponding detection channel flag is not set (step 2216), then
the output event detector flag is cleared (step 2218) and the
combination procedure exits. If the corresponding detection channel
flag is set (step 2216), the output event detector flag remains
set, and further detection channels, if any (step 2220), are
evaluated after incrementing the channel being considered (step
2222). Accordingly, this combination procedure also operates as a
Boolean AND operation--if any of the enabled and active detection
channels does not have a set output flag, then no event detector
flag is output by the procedure. It should also be observed that a
Boolean OR combination of detection channels may provide useful
information in certain circumstances; a software or hardware flow
chart accomplishing such a combination is not illustrated, but
could easily be created by an individual of ordinary skill in
digital electronic design or computer programming.
[0181] An implantable version of a system according to the
invention advantageously has a long-term average current
consumption on the order of 10 microamps, allowing the implanted
device to operate on power provided by a coin cell or similarly
small battery for a period of years without need for replacement.
It should be noted, however, that as battery and power supply
configurations vary, the long-term average current consumption of a
device according to the invention may also vary and still provide
satisfactory performance.
[0182] It should be observed that while the foregoing detailed
description of various embodiments of the present invention is set
forth in some detail, the invention is not limited to those details
and an implantable neurostimulator or neurological disorder
detection device made according to the invention can differ from
the disclosed embodiments in numerous ways. In particular, it will
be appreciated that embodiments of the present invention may be
employed in many different applications to detect anomalous
neurological characteristics in at least one portion of a patient's
brain. It will be appreciated that the functions disclosed herein
as being performed by hardware and software, respectively, may be
performed differently in an alternative embodiment. It should be
further noted that functional distinctions are made above for
purposes of explanation and clarity; structural distinctions in a
system or method according to the invention may not be drawn along
the same boundaries. Hence, the appropriate scope hereof is deemed
to be in accordance with the claims as set forth below.
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