as) United States
`a2) Patent Application Publication co) Pub. No.: US 2003/0004428 Al
`(43) Pub. Date: Jan. 2, 2003
`
`Pless et al.
`
`US 20030004428A1
`
`(54) SEIZURE SENSING AND DETECTION
`USING AN IMPLANTABLE DEVICE
`
`Publication Classification
`
`Tint, C17 coececccesssssssssensesssesssssstsssenssnet A6IN 1/18
`(SL)
`(52) US. Ch ceeccsscsssssesnstsnsenstnsseventnernneenetnnnee 600/544
`
`(57)
`
`ABSTRACT
`
`(76)
`
`Inventors: Benjamin D. Pless, Atherton, CA (US);
`Stephen T. Archer, Sunnyvale, CA
`(US); Craig Baysinger, Livermore, CA
`(US); Barbara Gibb, Palo Alto, CA
`(US); Suresh K. Gurunathan, Santa
`Clara, CA (US); Bruce Kirkpatrick,
`Santa Clara, CA (US); Thomas K.
`Tcheng, Pleasant Hill, CA (US)
`
`Correspondence Address:
`NEUROPACE, INC.
`255 SANTA ANA COURT
`SUNNYVALE, CA 94085 (US)
`
`A system and method for detecting and predicting neuro-
`logical 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 (includ-
`ing 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 combin-
`able in various ways according to the invention to detect and
`(21) Appl. No.:—09/896,092
`predict neurological events in real time, enabling responsive
`
`(22)
`
`Filed:
`
`Jun. 28, 2001
`
`action by the implantable device.
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`APPLE 1016
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` Implanted
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`Device
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`Fig. 3
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`410
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` Control] Module
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`Fig. 4
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`Detection Subsystem
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`422
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`Data
`Interface
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`Sensing
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`Waveform
`Analyzer
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`Control
`Interface
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`Fig. 5
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`sensing Front End
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`Analog to
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`Fig. 6
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`7
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`Waveform Analyzer
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`Channel
`Controller
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`Event Detector
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`Patent Application Publication
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`Jan. 2, 2003 Sheet 7 of 22
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`Wave
`Morphology
`Analysis
`Unit
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`Window
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`Event Detector
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`810
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`834
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`an
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`Fig. 11
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`Jan. 2,2003 Sheet 12 of 22
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`Fig. 1 2
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`Jan. 2, 2003 Sheet 15 of 22)
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`Patent Application Publication
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`Fig. 15
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`Initialize Total
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`Fig. 16
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`Jan. 2, 2003 Sheet 18 of 22
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`Patent Application Publication
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`Fig. 18
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`19
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`Total
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`Patent Application Publication
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`Jan. 2, 2003 Sheet 20 of 22)
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`US 2003/0004428 Al
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`
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`Interrupt
`Analysis Toolsinto
`Detection Channels
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`2110
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`Start with First
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`SEIZURE SENSING AND DETECTION USING AN
`IMPLANTABLE DEVICE
`
`FIELD OF THE INVENTION
`
`[0001] The invention relates to systems and methods for
`detecting and predicting neurological dysfunction charac-
`terized by abnormal electrographic patterns, and more par-
`ticularly to a system and methodfor detecting and predicting
`epileptic seizures and their onsets by analyzing electroen-
`cephalogram and
`electrocorticogram signals with an
`implantable device.
`
`BACKGROUND OF THE INVENTION
`
`[0002] Epilepsy, a neurological disorder characterized by
`the occurrence ofseizures (specifically episodic impairment
`or loss of consciousness, abnormal motor phenomena, psy-
`chic 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.
`
`[0003] Because epilepsy is characterized by seizures, its
`sufferers are frequently limited in the kinds ofactivities they
`may participate in. Epilepsy can prevent people from driv-
`ing, 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.
`[0004]
`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.
`
`[0005] 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.
`
`(0006] A numberof drugs are approved and available for
`treating epilepsy, such as sodium valproate, phenobarbital/
`primidone, ethosuximide, gabapentin, phenytoin, and car-
`bamazepine, 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 break-
`through 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.
`
`{0007] 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 mostviable alternative course of treatment.
`
`[0008] Currently practiced surgical approaches include
`radical surgical resection such as hemispherectomy, corti-
`cectomy, 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.¢., functionally important)
`brain regions and the consequent long-term impairment of
`various cognitive and other neurological functions. Further-
`more, for a variety of reasons, such surgical treatments are
`contraindicated in a substantial number of patients. And
`unfortunately, even after radical brain surgery, many epi-
`lepsy patients are still not seizure-free.
`
`[0009] Electrical stimulation is an emerging therapy for
`treating epilepsy. However, currently approved and avail-
`able electrical stimulation devices apply continuouselectri-
`cal
`stimulation to neural
`tissue surrounding or near
`implanted electrodes, and do not perform any detection—
`they are not responsive to relevant neurological conditions.
`
`from
`(NCP)
`[0010] The NeuroCybernetic Prosthesis
`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.
`
`[0011] Continuous stimulation of deep brain structures for
`the treatment of epilepsy has not met with consistent suc-
`cess. To be effective in terminating seizures, it is believed
`that one effective site where stimulation should be per-
`formed is near the focus of the epileptogenic region, The
`focus is often in the neocortex, where continuous stimula-
`tion 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.
`
`[0012] 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”,
`
`[0013] Most prior work on the detection and responsive
`treatment of seizures via electrical stimulation has focused
`on analysis of electroencephalogram (EEG) and electrocor-
`ticogram (ECoG) waveforms. In general, EEG signals rep-
`resent aggregate neuronal activity potentials detectable via
`electrodes applied to a patient’s scalp. ECoG signals, deep-
`brain counterparts to EEG signals, are detectable via elec-
`trodes 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 ECoGsignals.
`
`[0014] 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 elec-
`trodes 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.
`
`24
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`Jan. 2, 2003
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`is generally preferable to be able to detect and
`It
`[0015]
`treat a seizure at or near its beginning, or even before it
`begins. The beginning of a seizureis referred to herein as an
`“onset.” However, it is important to note that there are two
`general varieties of seizure onsets. A “clinical onset” repre-
`sents 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 interven-
`tion 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 capabil-
`ity would be considered seizure prediction, in contrastto the
`detection of a seizure or its onset.
`
`Inthe Gotman system, EEG waveformsarefiltered
`[0016]
`and decomposed into “features” representing characteristics
`of interest in the waveforms. One such feature is character-
`ized 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.
`
`(0017] The Gotman articles address half wave character-
`istics 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.
`
`It should be noted that the schemesset forth in the
`[0018]
`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.
`
`[0019] 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 wavepatterns within a timer period. This
`is accomplished with relatively complex processing, includ-
`ing 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.
`
`[0020] Amore computationally demanding approach is to
`transform EEG signals into the frequency domain for rig-
`orous spectrum analysis. See, e.g., U.S. Pat. No. 5,995,868
`to Dorfineister et al., which analyzes the power spectral
`density of EEG signals in comparison to background char-
`acteristics. 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.
`
`[0021] Also representingan alternative and more complex
`approach Is U.S.Pat. No. 5,857,978 to Hively et al., in which
`various non-linear andstatistical characteristics of EEG
`signals are analyzed to identify the onset ofictal activity.
`Once more, the calculation ofstatistically relevant charac-
`teristics is not believed to be feasible in an implantable
`device,
`
`[0022] 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 andtreat-
`ment system.
`In the Fischell system, various detection
`methods are possible, all of which essentially rely upon the
`analysis (cither in the time domain or the frequency domain)
`of processed EEGsignals. Fischell’s controller is preferably
`implanted intracranially, but other approaches are also pos-
`sible, 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.
`
`[0023] Allof these approachesprovide useful information,
`and in some cases may provide sufficient
`information for
`accurate detection and prediction of most imminent epileptic
`seizures.
`
`(0024] However, none of the various implementations of
`the known approaches provide 100% seizure detection accu-
`racy in a clinical environment.
`
`[0025] Two types of detection errors are generally pos-
`sible. A“ false positive,” as the term is used herein, refers to
`a detection ofa 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.
`
`In most cases, with all known implementations of
`[0026]
`the known approachesto detecting abnormalseizure 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 noside
`effects to limited amounts ofover-stimulation (¢.g., provid-
`ing 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.
`
`[0027] As is well known,it has been suggestedthat it is
`possible totreat and terminate seizures by applying electri-
`cal 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
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`ECoG desynchronization, Electroencephalogr. Clin. Neuro-
`physiol. 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.
`{0028]
`Furthermore, it should be noted that a false nega-
`tive (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 nega-
`lives are to be avoided.
`
`to achieve an
`It has been found to be difficult
`[0029]
`acceplably low level of false positives and false negatives
`with the level of computational ability available in an
`implantable device with reasonable batterylife.
`(0030]
`Preferably,
`the battery in an implantable device,
`particularly one implantedintracranially, 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 deviceor its
`battery is replaced. Rechargeable batteries have not been
`found to provide any advantagein this regard, as they are not
`as efficient as traditional cells, and the additional electronic
`circuitry required to support the recharging operation con-
`tributes 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 othertissue.
`[0031] 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-worldutility, 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.
`[0032] 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 lowerrisk offailure 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.
`[0033] 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 consumelittle enough power (in con-
`junction 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 consumedbythat portion.
`(0034] 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 accept-
`ably low risk factors for use in human patients.
`
`SUMMARY OF THE INVENTION
`
`[0035] Accordingly, an implantable device according to
`the invention for detecting and predicting epileptic seizures
`includes a relatively low-speed and low-power central pro-
`cessing unit, as well as customized electronic circuit mod-
`ules 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 symptomsor 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. Gen-
`erally, 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 wave-
`form 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
`ofthe event.
`
`[0036] 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 per-
`form much ofthe signal processing andanalysis requisite for
`accurate andeffective event detection. The central process-
`ing 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 detec-
`tion subsystem to perform certain tasks related to the detec-
`tion and prediction schemes enabled by the device.
`
`[0037] Much of the processing performed by an implant-
`able 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).
`
`[0038] 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 elec-
`tronics modules, minimally reliant upon a central processing
`unit.
`
`In particular, it has been found to be advantageous
`[0039]
`to utilize twodifferent data reduction methodologies, both of
`which collect data representative of EEG signals within a
`sequence of uniform time windows each having a specified
`duration.
`
`Thefirst data reduction methodology involves the
`[0040]
`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
`lime window.Stated another way, the line length function is
`representative of the variability of the input signal. A con-
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`stant input signal will have a line length of zero (represen-
`tative 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.
`
`(0041] 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 hori-
`zontal 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.
`
`[0042] Both the area and line length functions may
`undergo linear or non-linear transformations. An example
`wouldbe to square each amplitude before summingit 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 transformationsof the difference between sample
`values are advantageous in customizing the line length
`function to increase the effectiveness of the detector for a
`specific patient.
`
`[0043] 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.
`
`Feature extraction, specifically the identification of
`[0044]
`half waves in an EEG signal, also provides useful informa-
`tion. 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 waveis identified.
`
`[0045] 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
`wavesin 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 tn
`a time window meeting those criteria is somewhat repre-
`sentative of the amount of energy in a waveform at a
`frequency below the frequency corresponding to the mini-
`mum duration criterion. And the number of half waves in a
`
`time window is constrained somewhat by the duration of
`each half wave(i.c., 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.
`
`[0046] As stated above, the half waves, line length func-
`tion, and area function ofvarious EEGsignals are calculated
`by customized electronics modules with minimal involve-
`ment by the central processing unit, and are selectively
`combinedby a system according to the invention to provide
`detection and prediction of seizure activity, so that appro-
`priate action can then be taken.
`
`[0047] Accordingly, in one embodimentof 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 neuro-
`logical event detection. A central processing unit is used to
`consolidate the results from multiple channels and coordi-
`nate responsive action when necessary.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`[0048] These and other objects, features, and advantages
`of t