SYSTEMS FOR PREDICTION, RAPID DETECTION, WARNING, PREVENTION OR CONTROL OF CHANGES IN ACTIVITY STATES IN THE BRAIN

Abstract: A system (10) analyzes signals representative of a subject's brain activity in a signal processor (12) for information indicating the subject's current activity state and for predicting a change in the activity state. One preferred embodiment uses a combination of nonlinear filtering methods to perform real-time analysis of the electro-encephalogram (EEG) or electro-corticogram (ECoG) signals from a subject patient for information indicative of or predictive of a seizure, and to complete the needed analysis at least before clinical seizure onset. The preferred system then performs an output task for prevention or abatement of the seizure, or for recording pertinent data.

Description: SYSTEMS FOR PREDICTION, RAPID DETECTION, WARNING, PREVENTION OR CONTROL OF CHANGES IN ACTIVITY STATES IN THE BRAIN BACKGROUND OF THE INVENTION 1 FIELD OF THE INVENTION The present invention relates to the field of neuroscience for analyzing signals representative of a subject's brain activity including signals indicative or predictive of epileptic seizures More particularly, the invention concerns the automated analysis of brain activity signals to detect an activity state and transitions between states, and to detect precursors predictive of a change in the subject's activity state to a different state The invenUon is based on ideas and research in the fields of mathematics, neurolog) , statistics and engineering which enable the real-time analysis of biologic signals such as the eleclro-encephalogram (EEG) or electro-corticogram (ECoG), by the simultaneous execution of multiple methods In the preferred embodiment, these signals are rapidly, accurately, and automatically analyzed in order to 1 ) Detect and signal the occurrence of an epileptic seizure in real time (or contemporaneously with the arrival of the signal at the processor/device), 2) To predict behavioral changes typically associated with seizures, 3) To predict seizures by detecting precursors to the onset of the electrographic or clinical components of a seizure, 4) To detect and further analyze epileptiform discharges (spikes), and 5) To download the detection or prediction outputs to devices lor warning, oi thei apeutic interventions or the storage of data 2 DESCRIPTION OF THE PRIOR ART Humans and animals have several normal states of behavior such as wakefulness and sleep, as well as multiple sub-states such as attentive wakefulness and REM sleep Abnormal states include reversible states such as seizures and progressive states such as dementia Epilepsy, a disabling disease, affects 1 -2% of the American and industi la zed world's population, and up to 10% of people in under-developed countries Electroencephalography is the single most important ancillary test in the investigation of this disease EEG's are lecorded continuously for hours to days in an increasing number of cases with unclear diagnosis or poor response to adequate medical treatment The amount of EEG data for analysis is extremely large (e g , 64 channels of data at 240 Hz gives 1 3 billion data pomts/24 hr or 2 6 Gigabytes/day) and consists of complex waveforms with infinite variations Visual analysis of these signals remains (exclusive of this invention) the "gold standard" but it is impracticable for continuous EEG inteφretation as this is the most time-consuming part of any electrodiagnostic test and requires special training and skills which make this procedure expensive and thus of limited access and use Valuable EEG data is often discarded unexamined The length of recording is unnecessaπlv piolonged in a specially equipped hospital suite until patients have several seizures If the patient is unavvaie ot the seizures a common occurrence, then a nurse or relative must obsen'e and document the occurrence of these changes As seizures are brief and previously considered unpredictable, the need for continuous observation becomes imperative, adding to the cost m a non-effective manner Present methods of seizure detection are not only expensive, but rely on poorly discriminating methods, increasing the review time and nursing assistance because of the large number of false positives, and increas¬ ing the length of hospita zation through the false negatives Furthermore, these methods often "detect" the seizure well after its onset or even its end, when prevention or abatement of the seizure is not possible or irrelevant The inability to process data in real time has thwarted the scientific and clinical development of the fields of epilepsy and electroencephalography Cardiology has developed into a clinical science largeh based on the power of electrocardiography to analyze the heart's electrical acUvity in a rapid and accurate manner This has resulted in pace-makers, implanted defibnlators, and other devices which have saved thousands of mdividuals from premature death The comparison between cardiology/EKG and epilepsy EEG must take into account the fact that the electrical brain signals are far more complex than those oπginating from the heart This explams in large part the developmental lag between these two disciplines Electrical bram signals, because of their spatial and temporal characteristics such as non-stauon- anty, have resisted accurate real-time automatic manipulation The pπor art methods presentlv used to characterize these states are severely limited For example, the pπor art consists of a long historv of failed attempts to identify changes in EEG during certain behavioral states or tasks and to discern epi-phenomenology from phenomenology, a distinction that would help answer questions of fundamental importance Other limitations include the inability to determine whether spikes are a static marker of epilepsy, or whether they are dynamically related to seizure generation Present methods of automatic EEG analysis have many major limitations which render them virtualh useless for widespread, safe and effective clinical applications These limitations include 1 ) Lack of speed The time it takes most methods to analyze the input signals and produce an output which detects or predicts a state change is too great for use in warning, intervention, or prevention of epileptic seizures and other abnormal bram states 2) Lack of accuracy Pπor art methods have a large number of false positives (incorrectly identifying non-seizure activity as a seizure) and false negatives (failure to identify a true seizure), increasing the technical and financial burden 3) Lack of adaptability to subject or seizure type, no compromise between speed vs accuracy 4) Lack of portability and lmplantabi ty 5) High cost Accurate and reproducible prediction of behavioral or biologic signal changes associated with seizures has not been possible as these events occur unpredictably Our methods and devices enable seizure prediction by providing a worthwhile prediction tune that makes warning, abortion/abatement, and prevention of seizures possible The new treatment modalities that can be based on this method will lead to a significant reduction in seizure frequency and consequently, to a reducUon in the occurrence of injuries and fatalities, allowing persons \v ith epilepsy to become productive and lead normal lives The prior art in automated seizure and spike detection consists of vaπations of two primary methods "rule-based" analysis and, more recently, analysis by artificial neural networks The most popular is a "rule- based" method which has been under development since the late 1970's, pπmaπly by Dr Jean Gotman In the Gotman method, the signal is initially replaced bv a piecewise linear approximation which connects maxima and minima In the Gotman method, there is a list of rules which are then applied to throw out some of the smaller line segments m an attempt to remove fast activity that is supenmposed on an underlying vva\e of interest The larger line segments which remain are called "hall waves " Gotman s algoπthm then compares properties of the half waves such as averages of amplitude, duration, rhythmicity, and sharpness in moving 1/3 sec windows to those of past and future data segments As currently implemented, the method uses a total of 30 seconds of past data and 8-10 seconds of future data in these compaπsons A set of rules and thresholds are given to determine when these compaπsons of past, present, and future properties yield a detection of a spike or seizure These rule-based methods have a number of limitations, including a large false positive rate, and usually a long delay to detect even abrupt changes (often 10 or more seconds) Another method for spike and seizure detection involves training an artificial neural network (ANN) using past data tracings with annotated spikes and seizures to "learn" to recognize similar changes in unseen data The large number of "neurons" required for accurate analysis of a multichannel EEG ECoG input signal precludes real-tune analysis Consequently, the current state of the art implementations rely on a smaller number of "neurons" and a parametrized input signal in place of the raw signal The Gotman half-wave decomposition mentioned above is commonlv used m this signal parametπzation step - causing the inclusion of many of the limitations inherent in this method to adversely affect the ANN methods In addition, the adaptation of an ANN to improve its performance for a particular individual or group is performed off-line and requires time consuming retraining by expeπenced epileptologists This important limitation is overcome by the present invention 3 GLOSSARY OF TERMS AND USEFUL DEFINITIONS The onset of the clinical component of a seizure is the earlier of either ( 1 ) the time at which the subject is aware that a seizure is beginning (the "aura"), or (2) the time at which an observer recognizes a significant physical or behavioral change typical of a seizure The onset of the electrographic component of a seizure is defined bv the appearance of a class of signal changes recognized by electroencephalographers as characteπstic of a seizure This analysis requires visual review of signal tracings of varying duration, both before and after the perceived signal changes, using multiple channels of information and clinical correlates The precise determination of the onset is subject to personal lnterpretauon, and may vary based on the skill and attention level of the reviewer, the quality of data and its display The electroencephalogram, or EEG, refers to voltage potentials recorded from the scalp EEG will encompass any recordings outside the dura mater The electrocorticogram, or ECoG, refers to voltage potentials recorded lntracramally, e.g , directly from the cortex EKG is the abbreviation for electrocardiogram, EMG for electromyogram (electπcal muscle activity), and EOG for electrooculogram (eye movements) The peπod of time during which a seizure is occurring is called the ictal peπod (Those skilled in the art will appreciate that the term ictal can be applied to phenomena other than seizures ) The penod of time when the patient is not in the state of seizure, or in transition into or out of the seizure stale, is known as the inteπctal peπod Thepreictal peπod corresponds to the time of transition between the intenctal and the beginning of the ictal penod, and the postictal penod corresponds to the time peπod between the end of the seizure and the beginning of the inteπctal penod Herein the term real-time descπbes a system with negligible latency between input and output The term false positive refers to the case of a system mistakenly detecting a non-seizure signal and classifying it as a seizure The term false negative descπbes the case in which a true seizure goes undetected by a system Systems that have a low rate of false positive detections are called specific, while those with a low rate of false negative detections are called sensitive The terms epileptiform discharge and spike are used interchangeably herein to refer to a class ot shaφly contoured waveforms, usually of relativelv large power, and with duration rarely exceeding 200 msec These spikes can form complexes with slow waves, and can occur in singlets or in multiplets The terms epileptologist and electroencephalographer are used interchangeably SUMMARY OF THE INVENTION The present invention solves the problems and overcomes the limitauons of pπor art, while provid¬ ing pioneering advances in the state of the art Its preferred embodiment enables ( 1 ) the accurate, automated, real-tune detection of seizures, as well as the determination of their site of ongm, propagation path and speed through regions of the brain, and their duration and intensity, (2) the prediction of the onset of the clinical component of seizures, (3) the prediction of the onset of the electrographic component of seizures, (4) the online self-adaptation, or offline adaptation of ( 1 -3 ) to each individual, (5) the automated use of ( 1 -3 ) for diagnosis, quantitative analysis, imaging, warning, treatment, and stoπng of data, and (6) the mmiatunzation of the system to a portable or implantable device The adaptation of the system to each individual takes into account, seizure type and location, and changes in the sιgnal(s) over time, making use of any existing preictal, ictal, or postictal "fingeφπnts" for the subject The speed of analysis and levels of sensitivity and specificity can also be adjusted to desired levels, and the method can be implemented in either digital or analog form (or a combination) The preferred embodiment of the invention uses intracranial or scalp electrodes to obtain signals representative of current brain activity and a signal processor such as a personal computer or micro-processor, for continuous momtonng and analysis of these signals, detecting important changes or the appearance ot precursors predictive of an impending change, as soon as thev occui The output of this analysis is then fed to a device which produces an immediate response (e g , warning, treatment or storage) to the change or predicted change in state The signal processing includes an adaptive analysis of frequency, energy, wave shape and dynamics, phase relationships, measures of rhythmicity, "sequency," and temporo-spaUal stereotypia, vaπabi ty, dimension, complexity of the signal, and noise reduction 1 METHODS FOR REAL-TIME SEIZURE DETECTION The following is an overview of the steps which compπse the preferred embodiment of the invention for real-time seizure detection (1 ) Extract from the entire signal the part with ictal characteristics This step is accomplished with adaptive filtering, allowing the selection of patient- and/or sensor-specific initial parameters, and an adaptation process in which these filters automatically improve as important signal characteπstics are learned online (2) The output of this filter is used to compute an index of ictal activity in each current signal epoch (the "foreground"), which is then divided by a corresponding measure associated with the background signal, forming a ratio Novel application of median filteπng and time and state weighted averaging are used in this step (3) When the value of this raϋo reaches a particular threshold level, a seizure detection is immediately signaled (4) Grading and venfication of seizures is then accomplished using an analysis of duration, intensity, pattern recognition of spatio-temporal propagation, and postictal seizure signal changes In addition, a new seizure imaging method has been developed based on the detection methodology presented here 2 METHODS FOR DETECTING PRECURSORS TO SEIZURE These embodiments detect the occurrence of signal characteπstics or patterns which may be precursors to the clinical and/or electrographic components of seizure, resulting in their prediction Determination of the onset of a seizure by visual analysis (which is considered "the gold standard") is a subjective and empiπc process, at the present level of scientific development Determination of time of seizure onset depends in part upon the specifications and parameters associated with the recording devices and of the location and type of sensors in reference to the tissue from where the seizure oπgmates The intensity and degree of spread of die seizure also affects detection From a practical standpoint, prediction based on the detection of seizure precursors or the electrographic component itself yield a worthwhile time during which warning and intervention can be instituted to abort or prevent the onset of either of the components of the seizure By virtue of their self-tuning ability (detailed in a later section), die continued application of these prediction methods to a given individual or seizure type may improve the reliability of subsequent predictions, and may lengthen the predictively worthwhile time Seizure precursors include, but are not limited to ( 1 ) certain significant patterns of epileptiform discharges (or spikes), (2) significant abrupt attenuation of signal energy on some or all sensors, and (3) significant changes in various charactenstics of the power spectral density associated with each of the signals that are being monitored, e g , a sudden significant drop in the median frequency of a given signal Prediction of seizures may occur duπng different stages of their temporal evolution a) Prediction of the vibratory (or first) state, I e , the state before the seizure spreads beyond the anatomical or functional boundaries of the "cπtical epileptogenic mass" (defined as the smallest mass that, once fully synchronized, consistently generates the next states) b) Prediction of the electrographic component of seizure This component is mainlv defined by temporal continuity of the ictal signal with or without evolution across a frequency spectrum and with some degree of propagation outside (he cπtical mass Prediction of this state can be made by identifying precursors (see examples) Precursors have temporal, spectral, and other characteristics which distinguish them from the electrographic component c) Prediction of the clinical component of seizure The real-time detection of the electrographic seizure component is akin, for partial or secondanly generalized seizures to the prediction of the clinical onset as there is a latency between the two components Precursor detections further lengthen the predictive time of die clinical component 3 A METHOD FOR SPIKE DETECTION, CLASSIFICATION, AND COUNTING The invention also includes a new mediod for measuπng signal "shaφness," which we refer to as least-squares acceleration filtering (LSA-filteπng), that is used as the basis for a new spike detection method It can also be used to improve existing spike detection methods In this method, the signal is continuously monitored for the occurrence of such th gs as spikes, and their amplitude, frequency, waveform, 'sequency" (degree or pattern of clustering), rate of occurrence, location, and polantv, are computed and stored This information is then analvzed for conformance to anv seizure precursor pattern 4 DEVICES FOR THE DETECTION OF SEIZURES, PRECURSORS, AND SPIKES, AND I- OR THE PREDICTION OF SEIZURES The algoπthms listed above and defined in detail herein can be realized in digital or analog form in a signal processor The preferred embodiment has been implemented in an Intel 486 based PC for real-time monitoring and storage of data for patients undergoing clinical evaluation The real-time detection of (a) seizure precursors and the resulting prediction of the electrographic and clinical seizure components, (b) the electrographic component and the resulting prediction of the clinical component, or (c) spikes, enables the institution of safety and therapeutic measures, and initiates or continues the adaptation and self-learning of the methods For example, a seizure prediction can be used to tngger a device for systemic, lntraventncular, or intracerebral administration of a medicament or substance, for electπcal, magnetic, or thermal activation or deactivation of a nerve or a region of the subject's bram, for activation or deactivation of physiologic receptors, for ablation of a region of the subject's bram, for activation of a warning or biofeedback device, or for selection of segments of signals for transmission or storage (or for annotation of continuously recorded signals) and further off-line analysis BRIEF DESCRIPTION OF THE DRAWING FIGURES Fig 1 is a schematic illustration of the preferred apparatus of the present invention showing inputs of brain (or other biologic system) signals of a subject from surface and or implanted (e g , intracranial) sensors to a signal processor and vaπous types of outputs, Fig. 2 is a graphical illustration of the segments of data which may be used m one preferred seizure detection method for operating the apparatus of Fig 1 to represent current ("foreground") signal activity (e g , the most recent 2 seconds), and signal background activity (e g , a segment of 20 or more seconds in length) delaved 1 second from the end of the foreground window, Fig 3A is a graphical illustration showing a finite impulse response (FIR) filter which may be used in the first step of the preferred embodiment for detecting seizures in the input signals to the apparatus of Fig 1 Fig 3B is a graphical illustration showing the power spectral density (PSD) associated with the FIR filter of Fig 3 A, Fig 4 is a graphical illustration of an ECoG signal used as an input to the apparatus of Fig 1 , Fig 5A is a graphical illustration of the result of applying the geneπc FIR filter of Fig 3A to the signal of Fig 4, and squaring the output signal values in an embodiment of the invention, Fig 5B is a graphical illustration of the dimensionless ratio of a 1 second foreground median filter and a 20 second background delayed median filter applied to the squared, FIR-filtered signal shown in Fig 5A in an embodiment of an invention, Fig 6A is a graphical illustration of the part of the ECoG signal from Fig 4 during which the clinical and electrographic seizure onset and subject activation of the event button occurred, and duπng which the apparatus of Fig 1 detected the seizure, Fig 6B is a graphical illustration of the output of the seizure detection embodiment as presented in Fig 5B but restncting the time window to that time peπod corresponding to the signal of Fig 6A Fig 7 is a graphical illustration of an embodiment for a seizure imaging method and apparatus applied to 16 simultaneous ECoG recordings containing a seizure event, Fig 8 A is a graphical illustration of an ECoG signal which contains a number of epileptifotm discharges, Fig 8B is a graphical illustration of die output of first step of the spike detection embodiment, potential spikes with shaφness as computed by LSA filteπng) exceeding a given threshold and their respective polanty (up or down) are identified Fig 9 is a graphical illustration showing the power spectral density (PSD) or the impulse response of the presently preferred IIR filter designed to be used m detection of the seizure precursor spikes as descπbed in Example 1 Fig 10 is a graphical illustration of an ECoG signal which contains a seizure precursor signal (the quasi-peπodic epileptiform discharge precursor detailed in Example 1 ) In this Figure, the times of the electrographic onset of the seizure, the clinical onset of the seizure, and the time of detection of the seizure precursor using an embodiment of the invention are annotated, Fig 1 1 A is a graphical illustration of an ECoG signal of a subject which contains a seizure precursor signal (the signal attenuation precursor detailed in Example 2), Fig 1 IB is a graphical illustration of the output of the precursor detection embodiment for this type of seizure precursor, Fig 12 A shows the illustration of Fig 1 1 A with the time axis restncted to a smaller range of tunes which still include the detection time of the seizure precursor, and the times of the electrographic and clinical onsets for the seizure, Fig 12B shows the illustration of Fig 1 1 B with the time axis restncted to a smaller range of times which still include die detection time of the seizure precursor, and the times of the electrographic and clinical onsets for the seizure, Fig 13A is a graphical illustration of an ECoG signal of a subject which contains anodier seizure precursor signal (a rapid drop in median frequency, as detailed m Example 3) The times of die electrographic onset, die clinical onset, and die event button press by the subject are annotated, Fig 13B is a graphical illustration of the result of applying the embodiment of die invention for detection of dus particular seizure precursor to die signal presented in Fig 13 A The electrographic and clmical seizure onsets, and die time of event button press are also annotated, as well as die time of precursor detection, Fig 14A is a graphical illustration of 4 27 sec of ECoG signal data Fig 14B is a graphical illustration of the power spectral density (PSD) of die signal in Fig 14A, show g die modal frequency, median frequency, and mean frequencv of die signal, Fig 15A is a graphical illustration of 2 seconds of ECoG signal recorded from a subiect duπng an inteπctal (non-seizure) peπod, and Fig 15B is a graphical illustration of the absolute value of wavelet coefficients from levels 1 -4 obtained bv applying the fast wavelet transform to the signal of Fig 14A DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Fig 1 illustrates die preferred apparatus 10 for receiving and analvzmg signals representative ot a subject's brain activity and for producing different types of outputs Apparatus 10 includes signal processor 12, mputs 14 and outputs 16 Signal processor 12 is preterablv a computer such as one with capabilities that meet or exceed those of an Intel 486-based computer having 33 MHz clockspeed and 8 MB of RAM Those skilled in die art wdl appreciate that an appropnate digital signal processor can be used in place of die preferred computer, as could a custom-designed semi-conductor chip having die requisite capability, preferably configured for implantation or as a portable device Signal processor 12 could also be an analog processor or an analog/digital combination Appendix 1 is mcoφorated as part of the disclosure hereof and contains die presentlv preferred computer program for use by apparatus 10 and, m particular, signal processor 12, and for implementing die preferred mediods of the present invention as further descnbed herein Inputs 14 include EEG (or other type of scalp) signals obtained from a plurality of scalp sensors 18 transmitted dirough associated lines 22, or ECoG signals obtained from implanted sensors 23 and transmitted through associated lines 24 The input signals used in die development of this invention were amplified and converted from analog to digital form at a rate of 240 Hz with a dynamic range of [-300,300] μ V and digital resolution of 59 μV (10 bits of precision per datum) This provides 144 Kb ot data per minute per channel Those skilled in die art will appreciate that sampling may be performed at fixed or varying rates (higher or lower than 240 Hz) and precision (widi more or less precision dian 10 bits), using linear or nonlinear analog to digital conversion, and widi constant or varying dynamic range (l e , adjustable gain) Data acquisition may also be performed using adaptive sampling techniques, in which diese sampling parameters vary over time and are determined by characteπstics of the signal being sampled Adaptive sampling techniques can be used to selectively enhance relevant signal charactenstics and increase signal quality and resolution m certain frequency bands Outputs 16 can tπgger portable or implanted devices, electrodes 26 which may be intracranial or extracranial, or placed over or around a nerve 28, a medicament injector or pump 32, an audio or LED output or any other form of warning 34, and auxiliary memory 36 for stoπng input signals and event data Implanted electrodes 26 can be used for any form of activation or deactivation (e g , electncal, thermal, etc ) of local or remote bram cells or for ablation of die epileptogenic tissue Nerve stimulator 28 is preferablv associated wid die vagus nerve as such stimulation has been found to abate or prevent a seizure Physiologic (or natural) stimulation to receptors (e g , light to retinal receptors) can prevent or abate seizures and is die function of stimulator 30 Injector 32 is preferably implanted for automated instantaneous release of the appropπate medicament (inclusive of any efficacious substance) for treating, preventing or abating a seizure Memory 36 is provided to store signal and event data for archival and analysis purposes As discussed further herein, the analysis performed in signal processor 12 can be customized for a particular patient to improve die detection of brain states and state transitions, and the prediction of changes in brain states The customization of die signal processing can be based on the information stored m 36 via feedback of this information to signal processor 12 Foi example, this information may be used to monitor efficac\ of treatment and to optimize seizure/spike detection and prediction, and ierapeutic or safety interventions Those skilled in die art will also appreciate that memory 36 can be included as an integral part of signal processor 12 Those skilled in die art will recognize that changes in cerebral state are highK correlated widi changes in level and type of activity of other oi gan svstems (e g , heart, etc ) and as such these signals av be useful for detection and prediction or validation of seizures or of other changes in brain state The following signals (not annotated in Fig 1 ) mav be used in conjunction with EEG and ECoG signals to further improve performance of the system 1 ) Non-electncal cerebral (global or regional) signals, such as concentrations of glucose . free radicals, metabolic by-products, neuro-transmitters, or other substances, or measurements of lntracranial pressure, temperature, blood flow or indices of metabolic activity, etc , 2) Cardiovascular signals such as heart rate, R-R interval and vanabi ty, etc , 3) Respiratory signals such as tidal volume, peak-to-peak interval, etc , 4) Electrodermal and odier DC potentials, 5) Signals representative of concentrations in the blood or other peπpheral tissues of gases, substances, or chemicals such as lactic acid, etc , 6) Signals representative of the level or type of activity of cranial or peπpheral nerves (e g frequency and pattern of action potentials, etc ), 7) Signals related to EMG activity, force, direction, and patterns of limb or body movements REAL TIME SEIZURE DETECTION Successful real-time detection of seizures depends on die ability of any me od to rapidly and accurately distinguish die ictal from the non-ictal part of the signal We begin widi a detailed descπplion of the geneπc method, dien discuss additional features and modifications which are used to enhance its sensitivity and specificity by making it adaptive, 1 e , allowing it to learn online The preferred embodiment as detailed here is based on a sampling rate of 240 Hz widi 10 bits of precision However, there is a wide range of digitization techniques which may be used, together with die appropπate modifications to the algoπth 's parameters Fig 4 shows a 4 minute segment of ECoG signal which is used to illustrate a preferred embodiment for detecting die electrographic component of a seizure The first step of die method consists of applying a filter to the signal to decompose the entire signal into its ictal and non-ictal components As a result of research performed for dus invention, identdication of key differences between ictal and non-ictal signal characteristics was successfully accomplished These results enabled die construction of filters to accomplish dus first step of the method These clude "geneπc" digital filters of both finite impulse response (FIR) (also known as a convolution filter and moving average (MA) filter) or infinite impulse response (OR) One such FIR filter is shown in Fig 3, together with an estimate of its power spectral density (PSD) These filters could include analog filters as well and were constructed using a data base of over 100 seizures in the following manner 1) Each seizure was divided into segments according to its temporal evolution, and die PSD of each segment was computed, 2) The resulting PSD's were dien compared with PSD's obtained from inteπctal segments Power- frequency envelopes were dien computed, more heavily weighing the spectra at frequencies which yielded die greatest separation between die ictal and inteπctal PSD values 3) The orders and type (e.g FIR or IIR) of the "genenc" filters were then chosen, taking into account die trade off between computational burden/speed and goodness-of-fit of their impulse response to die desired shape The filter coefficients were then computed from die desired impulse response using standard filter desi n methodol¬ ogy Those skilled in the art are aware of the many degrees of freedom or options available in designing filters For instance, the magnitude and phase specifications of the filter s impulse response mav be adiusted to match me PSD charactenstics of a set of seizures, or infinite impulse response (instead of FIR) filters mav be used when speed of processmg is the primary objective, especially if the filter must precisely discπmmate between extremely low frequencies (e g , less dian 2 Hz ) Genetic algoπdims provide a useful approach to solving die multi-dimensional constrained optimization problem of designing a best filter for a given set of data For a given subject, a filter is initially selected from die filter bank This selection can be done off¬ line by a clinician upon a study of archival wave forms In the alternative, the selection can be completed on-line by applymg all die filters in die filter bank individually to the input signals and selectmg diat filter that produces d e greatest differentiation If the input signal is given by {λ,}° = |, and die FIR filter has m coefficients {b 0 , b,, ,b_, , } , d en the output (filtered) signal { Y,}"=ι is obtamed from die formula Y .H = IW- b,x v , + b 2 x k , + + b m ,x k mt| , where it is assumed that x, = 0 for all j < 1 The output of dus filter is dien squared, and die evolution of die resulting Y^ values is monitored, comparing die most recent values (die "foreground") to less recent values (die "background"), to detect relevant changes Fig 5A shows the graph of the Y values which result from die application of the FIR filter descπbed above to die signal of Fig 4, and then squaring the output In die next step of the ediod, we shall refer to the present information as "foreground," and compare it to die signal history or "background " Once die raw signal has been filtered to extract and enhance its ictal part, the next step in the method is to use the { Y^ } sequence to create measures of the level of ictal activitv in die most recent signal epoch, and compare it widi that contained in die inteπctal signal To compute a measure of ictal activity in the foreground, we apply a (nonlinear) median filter of order 480 (2 seconds) to die { Y k } sequence to obtain die sequence {F k } (240 values per second per channel), F k = median* Y^„Y^ 2 , ,YJ „YJ}, where p is the order of die median filter (e g , p = 480 @ 240 Hz ) This step is used to monitor a change in central tendency of the distnbution of die recent (foreground) Y k values The median (or odier order-statistic is preferred for dus step because of its ability to measure die central tendency of a distnbution widiout being overlv sensitive to outliers such as those which result from single or multiple spikes To compute a measure of the ictal activity in the background, {B k }, which is used as a reference against which foreground changes are measured, we apply another median filter to die Y k values, widi die following modifications 1 The order is increased (e g , to 20 seconds, p=4800) to obtain a more stable background, 2 The filter is delayed by a certain time (e g , 1 second) from die current time to remove any possible effect of the most recent signal on the background value, B k , dius making a foreground change easier and faster to distinguish/detect, 3 Updates of background, B^ are disabled during seizures or odier anomalies (e g , transient artifacts), and 4 An exponentially forgetting time average (or a more general time- and state-weighted avei age) of this dela\ed median filter output is used to increase the peπod of signal history represented bv die background sequence {B , without increasing memory/storage requirements or computations Use of this technique decreases the size and number of fluctuations in dus background sequence, improving detection bv allowing more sensitive thresholds to be set widiout significantlv increasing false detections Details regarding more general time- and state- weighted averagmg techniques which mav be employed in this step are presented in Appendix 2 which is mcoφorated as part of the disclosure hereof To now be more precise, the background sequence, {B k }, is computed as follows We beg by applying a delayed median filter of order h and delay d to the squared output of die FIR-filter, { Y k }, to obta the output sequence, {w k }, w k = med.an{Y h .„Y . 2 . .Y. ..Y.-i>. with, e g , h = 4800 (to use 20 seconds of data), and d = 240+480 = 720 (l e , dus median filter is delayed 1 second from die end of die 2 sec foreground window, l e , 3 seconds from the current time), so diat w t = medianf Yj. JS ,„ ,Y n „Yl 1V) } Then define B k ,f r k ≥C where λ is a "forgetting factor" which controls die rate of exponential forgetting (where dus factor is between 0 and 1), r k is die current ratio of die foreground "ictal index" to die background "ictal index," given by B. and C 2 is a direshold level used to disable updates of B k when C, is exceeded by r k For accurate, real-time detection of seizures, die ictal signal should not be allowed to affect die background signal from which die seizures are distinguished Accordingly, to further improve the detection method, die constant, C 2 , above was introduced to disable updates of die background ictal index, B k , whenever die ratio, r k , is large (i e , exceeding C 2 ) Failure to disable these updates would allow unwanted rapid growdi in die background ictal index duπng a seizure, resulting in a loss of ability to measure seizure duration, and a degradation in die ability to detect latter seizures (or die regeneration of a smgle evolving seizure) when diese events are closely spaced in time This enhancement of the mediod is especially important when one wishes to detect die end (and hence die duration) of a seizure in addition to die onset time A seizure detection is immediately signaled when die ratio, r k , exceeds C„ which is the detection direshold When die foreground and background median filters are 1 -2 sec, and 20 sec, respectively, nominal preferred values for the above defined constants are C, = 20, C 2 = 5, and λ = 9997 Also note that because die ratio, r b is dimensionless, the direshold may be set without regard for die units used in die particular signal being monitored Figs 5B and 6B show die graph of dus ratio, when the mediod is applied to the signal of Figs 4 and 6A In this example, the mediod detects the seizure 5 seconds pπor to die electrographic onset (as determined lndependendy bv a trained epileptologist), 8 seconds pπor to the clinical onset of die seizure (as determined by review of die videotape record by die same epileptologist), and 1 1 seconds pπor to die patient's activation of an event button (signaling die beginning of die seizure according to the patient), as Fig 6B illustrates By varying die lengths of background and foreground, die accuracy and speed of detections mav be adjusted to improve performance as desired for a particular application of die mediod The preferred embodiment constitutes a substantial improvement in die state of the art in bodi speed and accuracv of detection In addition, the adaptabdity of the system's parameters provide flexibility and improved performance for a vaπety of applications For example, if the speed, but not die accuracy of detection is the ovemding factor for success in seizure abortion, dien a decrease in die foreground window lengdi is one wav to achieve dus result If, on die odier hand, dus method is to be used in a device to destroy die part of the brain from where the seizures oπgmate, then maximal accuracy (and not detection speed) is needed to avoid damagmg healthy tissue This can be accomplished by increasing die lengdi of die foreground w dow The novel use of order-statistic filtering in dus invention and, specifically, a median filter, to characterize the ictal activity m the foreground significantlv improved die accuracy of seizure detection by enabling discrimination between ictally organized and non-organized epileptdbrm activity The further additions of time- and state-weighted averagmg, and die formation of a ratio compaπng the level of ictal activity in the recent signal (foreground) to diat which is normally present duπng mtenctal (non-seizure /background) penods, among other ideas (described below) have enabled die present invention to provide improved results not only in speed, but also in accuracy and adaptability Additionally, die mediod allows for online learning and adaptation as discussed in later sections As die signal from each individual sensor is monitored in die aforementioned manner for me detection of seizures, spatio-temporal correlations between die ratios simultaneously computed for multiple sensors which may be obtaining signals of the same class (e , all ECoG), or different classes (e g , ECoG, EKG, and blood chemistrv) can then be used, if necessary, to increase sensitivity and specdϊcity of the algoπdim, c g , b\ allowing for sensor and signal dependent direshold settings and die use of multi-channel information in bou seizure detection and artifact rejection Those skilled in die art recognize that external sensors produce a lower signal to noise ratio than implanted sensors This drawback is particularly prominent for sensors placed on die scalp for EEG moniloπng due to volume conduction and low-pass filter effects on die cortical signal caused by die structures surrounding the bram Furthermore, die signal recorded from the scalp may be delayed from mat direcdy recorded from die bram In order to compensate for iese drawbacks, die following strategies have been adopted (individually, or in combination) 1 ) Prefilteπng of die scalp signal and other inputs to extract the useful signals (e g , separate cortical voltage potentials from artifacts) 2) Artifact pattern recognition Artifacts, defined as signals that are not die direct product of cortical neuronal activity occur duπng normal activity and also duπng seizures During dieir clinical component, seizures generate a host of artifact signals that, for a given individual, may be highly stereotypical in their spectral and spatio- temporal characteπstics and as such are distinguishable from cortical seizure activity These artifacts, which correspond to body, mouth, or eye movements, etc , are first recognized by compaπson widi artifacts diat have been catalogued mto a general library according to their characteristics As diese artifacts aie identified their temporal evolution is tested for conformance to a pattern of artifacts stereotypic of that individual's seizures 3) Use of odier signals such as EKG, respiratory rate, magnetic fields, diermic potentials, concentrations of chemicals, recordings from a dynamometer or accelerometer attached to die patient or bed, etc for use in seizure detection or prediction Once a seizure has ended, diere is usually a decrease in signal power in certain frequency bands This decrease is dependent upon die intensity and duration of die seizure, among other factors The method/device tests for and measures die duration of any loss of power following a large increase (such as that which occurs with seizures), and the results of diese tests are used to retrospectively assess die accuracy of seizure detection For mstance, a large sustained increase in power, without a subsequent power decrease in certain bands, would be routinely subjected to off-line review THE ADAPTIVE AND EVOLUTIONARY NATURE OF THE METHOD Large scale validation studies have proven diat the above-detailed embodiment for seizure detection is both highly sensitive and specific However, to account for possible intra- or inter-individual signal vaπabihty, and to further improve performance as needed, a number of additional steps have been implemented which allow die system to learn and adapt on and offline These steps can be grouped into the following categories ( 1 ) adaptive signal acquisition mediods, (2) intelligent parameter adjustment, and (3) adaptive use of detection and prediction information The first step m adapting die method to a particular subject or application consists of selecting die appropnate set of signals to be monitored, and controlling die manner in which diese signals are acquired Ald ough die detailed example above makes use of signals consisting of electπcal potentials (EEG/ECoG) sampled at 240Hz and digitized wtdi 10 bits of precision, in some cases one may, as discussed earlier, vary the analog to digital conversion parameters, or use the analog signal itself, in order to ascertain vaπous characteπstics of die signal which may be important in die subsequent analysis For example, sampling rate may vary continuously widi die frequency content of die signal, increasing widi die slope of die wave (duferential sampling), the steeper the slope, the higher the sampling rate In addition, online signal quality control mediods can be used to detect various forms of signal degradation and warn die user or others For example, the system can produce an output indicating die presence of a large quantity of 60 Hz activity, or abnormally high "clipping" in die analog to digital conversions, etc Moreover, in many cases it is advantageous to record signals from odier sites (or organs) in addition to the brain or its encasing structures, and to analyze biological signals other dian EEG/ECoG, including, but not limited to, ( 1 ) other electrical potentials (AC or DC), (2) EKG, (3) EMG, (4) respiratory rate, (4) concentrations of glucose, free radicals, or other substances (e , neurotransmitters) in die brain, blood or odier tissues, (5) magnetic fields, and (6) bram temperature It is also important to note diat while, for die sake of claπtv, attention is pnmaπly focused on data from a single sensor in the detailed example above, in practice die mediod can be applied to signals from a large number of individual sensors and combinations of sensors (e g , a weighted sum of die electrical potentials between a given sensor and all odier sensors) simultaneously (i.e , in parallel), momtoπng spatio-temporal correlations of die resulting outputs The parameters of the method may be adjusted as needed Parameters such as foreground and back- ground window lengdi, filter shape and type, time-weight, and direshold settings mav be adapted on or offline to the particular subject and application For example, if data exists containing a pπor seizure for a given subject then one can process dus data to realize a filter adapted to any known signal charactenstics oi seizure "fingeφπnt" of diat subject A filter with die spectral charactenstics of its impulse response matching die typical PSD at seizure onset for this subject, can be used to initialize the adaptive filtering procedure, dierebv improving die sensitivity and specificity of die algoπthm for that subject The FIR filter may also be adapted to die particular location ot the sensor, or the type of signal being monitored For example, when monitoπng posteπor electrodes (e g occipital), die preferred filter is designed to recognize and de-emphasize irrelevant signals m the alpha range (8- 13 Hz widi normal reactivity), when its power is below a given percentile While spectral characteπstics ot seizures lor a given subject mav differ from those analyzed for die design of the geneπc filter, this method, bv virtue of its adaptability, is well suited to prediction and detection of seizure patterns with a wide range of spectral and other relevant characteristics To increase computational efficiency, one may also use a stable infinite impulse response (IIR) filter (also known as an auto-regressive moving-average or "ARMA" filter) in place of die FIR filter Such filters use a linear combination of past filtered signal values in addition to present and past put signal values to compute each new filtered signal value That is, given IIR filter coefficients A={a j a j a 3 a 4 . aj, and B=[b, b 2 b, bj, and input signal {x,, x 2 , , x N }, we compute the sequence {Y N } using die recursive formula. r N = - a \ r r.-l - a . Y N-2- - β » Λ r l + +ύ A-m This feedback of die filtered signal allows the IIR-filtered output to be produced widi fewer computations and shorter delay tune. The FIR filter step utilized in die preferred embodiment example is a special case of more general adaptive filteπng mediods which may be employed to decompose die signal, extracting and enhancing signal activity indicative of an ongoing or impending change of bram state For example, one may aliow die FIR filter coefficients (and order) mentioned above to vary widi time as a function of die current bram state, to increase the accuracy of the method under certain conditions The mediod can use a different filter when die subject is in slow-wave sleep, from mat which is used during vigorous exercise, since die charactenstics of the background signal and noise level/type, from which a change is detected, are markedly different in diese two states Those skilled in die art are aware of many known methods of digital signal processing which can be used to achieve such online adaptation (e g , "active noise cancellation") In the presently preferred implementation, one begins widi a geneπc filter (as above), diat allows for the detection of seizures which may begin in a wide range of frequency bands As the input signal evolves over tune in the non-seizure state, windowed power spectral density (PSD) estimates of die signal can be successively computed, and a PSD representative of the recent (or entire) signal history may be obtained, togedier widi confidence intervals, a median, mm, max, and odier percentiles, etc This representative PSD is dien used to modify die current filter's impulse response, in accordance widi die newly acquired subject-specific and state-representative "background" information to improve detection of subsequent state changes Inteπctal PSD's which do not fit any predetermined ictal pattern, but which are sufficiently different from die background PSD, may be archived and reviewed, diese and parameters which may be computed from die PSD (see Appendix 2), may be used as templates for detecting odier state changes, precursors to state transitions, and for signal quality control Archived seizure segments can also be used in die online adaptation of this filter, focusing attention on the frequency bands of past seizures which are maximally differentiated from their respective inteπctal segments Those skilled in die art will further recognize that several mediods exist for online filter design from a given PSD, e g , die mediod of Parks-McClelland. In addition to filters whose design is mainly based on die PDS, phase, shape and odier characteπstics (e g , neurally-based filters or odier signal-shape filters) can be used to realize new filters as necessary Parameters can be adjusted in dus mediod, to detect ictal activity on a wide range of time scales and sensitivities In particular, by reducing die lengdi of die foreground window from the 2 second lengdi used above to .2 seconds, individual spd es can be detected with this mediod The detection of diese spikes as they occur enables die creation of a biofeedback system in which die subjects are made immediately aware ot epileptiform activity in their brain (e , by an auditory transduction of die abnormal signal activity for auditory pattern recognition) Such a system can be used to leach the subjects to control or eliminate their abnormal own discharges The diresholds, C, and C 2 , can also be adiusted online, based on accumulated statistics and functionals of the computed ratios, {r k }, over long time peπods. including die ratio values attained duπng seizures, togedier with die maximum and minimum values reached intenctally Finally, the ways in which die system uses detection and prediction information can be made adaptive as well For example, a wammg alarm could have a feedback mechamsm to detect background noise, and ad j ust its output signal accordingly - from a vibration when in die library to a loud tone when on an auport runway As anodier example, changes in stimulation parameters for intervention or seizure abortion can be made based on a history of therapeutic effectiveness OTHER EMBODIMENTS OF THE SEIZURE DETECTION METHOD The task of seizure detection requires near-simultaneous temporal-frequency localization of die signal Real-tune seizure detection imposes the additional demand of high processing speed The use of adaptive filtenng in die preferred embodiment is ideally suited to meet diese demands Those skdled in the art will appreciate that a vaπety of odier methods can be used for nearly-simultaneous temporal- frequency localization, including die windowed Fouπer (or Gabor) transform (WFFT), the wavelet transform, the Hartley transform, and die Wigner- Ville transform Any of these methods can be used to extract or enhance the ictal part of die signal from the entire signal as in the first step of die adaptive filtenng method In addition, a number of odier transforms can be used to accurately and rapidly extract and enhance the ictal portion of the signal We give two examples 1 Windowed correlation integrals Embed die oπginal (windowed) signal a higher- dimensional space using die mediod of time-delays, a standard technique in non neai dynamics and statistics Then count die number of pairs of points whose separation is less than some cntical distance (dus is called die sample correlation integral) Monitor dus statistic as a function of time Ictal activity is indicated when die number of such pair falls by an order of magnitude or more Higher order correlations can also be used 2 The windowed "kinetic energy," defined as follows take die first difference of die time series represented by the signal, re-embed dus deπved time senes widi a suitable lag time, en choose a window size (time interval), in each window, and compute for each window die sum of die squared lengths of all die vectors, and monitor dus statistic as a function of time Bodi methods are robust against changes in die control parameters such as embedding dimension, delay time, and window lengdi In particular, both have been effectively employed with short window lengths and embedding dimensions as low as 3, enabling real-time monitoring, detection and prediction Both of diese mediods measure what might be charactenzed as a relative dispersion, at a certain scale, of the points on die re-embedded trajecton As such, they are remarkably insensitive to small fluctuations in die position of the points, which in turn means that these mediods are extremely robust agamst the contamination of the signal by noise For example, using method 1, only a slight decrease in sensitivity occurs when the signal is contaminated bv sufficient Gaussian noise to reduce the signal-to-noise ratio to 5dB It should be noted that die transforms listed in dus section are also useful for preprocessing signals when little is known a pπoπ about the frequency bands of interest or the time scale of changes In particular, diese mediods can be used as initial screening tools in determining fiequency bands in which changes are correlated widi particular changes m brain state Additional background details regarding die Fouπer and wavelet mediods may be found in Appendix 2 Coherence analysis, and higher order spectra and cumulants, etc , also provide additional important information regarding signal frequency changes over time The presently preferred computer program for seizure detection shown in Appendix 1 performs on-line median filteπng, updating die moving foreground and background median filters at 240 Hz For this task, die program makes use of circular doubly-linked lists For certain applications (e g , to conserve processing resources when a large number of signals are being monitored simultaneously), these computations may be performed less often using well known batch sorting algonthms (e.g., Quicksort), to compute die median in moving windows One may also replace the median filter by a similar order-statistic filter, or with some odier measure of central tendency (e g , α-tπmmed means) For computation of die background index descπbed above in die case when a very large background window is desirable, one may instead compute the sequence, {B k } using die exponentially forgetting average of die median of penodicaliy sampled foreground values For example, the background value can be updated once per second by first computing die median of die foreground values diat occurred at each of the last 300 seconds, dien adding this result (properly weighted), to die previous background value Another preferred embodiment of die above med odology consists of the analog implementation of die digital methods descπbed herein Those stalled in die art will appreciate that every step of the mediod presented herein can be implemented in analog This fact is important in die context of miniatunzation and die development of implantable devices, since analog devices generally require significantly less battery power to operate and, thus, last much longer AN APPLICATION OF THE SEIZURE DETECTION METHOD TO IMAGING The seizure detection mediod applied to a signal recorded from a particular sensor produces a dimensionless ratio representing the current level of ictal activity present at diat corresponding location As part of die present invention, a new "seizure imaging" method was developed based on this seizure detection mediodology The seizure image is obtained using a contour plot of the brain regions which achieve die same seizure ratio ("equi- lctal"), and tracking die temporal and spatial evolution of this ratio, the contours are directly obtained by mteφolating diese ratios over bodi space and time Fig 7 illustrates the application of dus seizure imaging mediod to a seizure recorded simultaneously from two implanted needles each containing 8 contacts Contacts 1 -8 recorded activity from die left, and contacts 9- 16 recorded activity from the nght amygdalo-hippocampal regions The times of seizure detection (solid line, 107 sec after an arbitrary initial zero time), electrographic onset (dashed line, 1 12 sec ), clinical onset (dot-dashed line, 1 15 sec), and event button press (dotted line, 1 18 sec ), are annotated This graph illustrates diat the seizure onginates from contact 12, then weakens somewhat, followed by a more widespread resurgence at neighboring contacts, first in those more antenor (9- 1 1 ), and dien later involving the more posteπor nght temporal contacts (13,14) The onset of activity in die left hemisphere begins at 156 sec on contact 4, but dien spreads to contacts 1 -3, and 5 within 3 sec, and to the posterior contacts 6-8 four seconds later Ictal activity on the left is evident for 15 seconds after the cessation of nght temporal involvement This particular imaging technique can be very helpful to the clinician in spatio-temporal localization of seizure activity and, in particular, in localization of the site of oπgin of die seizure and characterization of die pattern of spread Ithas been found as part of die present invention, through die application of this seizure imaging method, that seizures from the same subject usuallv have a high degree of spatio-temporal congruency of ratio lntensiU Accordingly, such images and die padiwavs of ictal propagation which thev depict, can be used in die analvsis of spatial correlations in die outputs of the simultaneously evolving, single-channel detection ratios computed as descπbed in die preferred embodunent of the mediod Specifically, any detected seizure may be compared m terms of its spatial evolution, propagation speed, and intensity evolution (including, e g , die trajectory of maximal absolute and relative intensities), widi similar information from pπor ictal events, and die degree of conformance can be quantified and used m assessing whedier (a) die detection was a seizure, or (b) seizures onginate from one or multiple sites These mediods may also allow therapeutic intervention at die sites to where the seizure spreads, w hen treatment at the site of ongm is of limited efficacy Those skilled in the art will appreciate that odier quantities computed from die input signals (not just the above-mentioned ratios) can have their spatio-temporal evolution depicted via similar imaging techniques, and diat a vanety of ddϊerent existing inteφolation techniques may be employed A METHOD FOR DETECTION OF EPILEPTIFORM DISCHARGES OR SPIKES The mediod descπbed herein for detection and classification of spikes, is based on a new mediod developed as part of this invention for computing relative shaφness of die signal as it evolves, and companng it to die shaφness of other waveforms In pnor art mediods, die shaφness is computed at each successive signal maximum or minimum using die numeπcal second denvative at that point, or by connecting adjacent signal minima and maxima wi i straight lines and measuring the resulting angles These approaches, however, are inaccurate because diey ignore signal characteπstics near each peak, and rely too heavily on a small number of digital points approximating die underlying continuous signal, using too few points near the peak (the region of primary interest) and too many points far away from the peak The present invention includes a new mediod for computing dus shaφness using what we shall call a "least-squares acceleration filter" (LSA filter), which overcomes these limitations of pπor art This mediod can be used independently, or as an improvement to existing mediods, many of which relv heavilv on relative signal shaφness in their detection of spikes This method consists of first fitting a function to die data surrounding die point at which the shaφness is to be computed, and then determining the shaφness of the fitted function The preferred function for use in this fit is a parabola, and die preferred cnteπon for die fit is die minimization of mean-squared error of fit (least squares) The parabola is used because its second denvative can be obtained from die computation of a single value (its leading coefficient), and no function evaluations (which can be computationally expensive) are necessary Other performance cπtena for an optimal fit could also be used The choice of least squares is relativeh standard, highly effective, and has been made computationally feasible here The computation of die shaφness (I e , die acceleration) of the sιgnal(s) at each data point has been reduced to applying a small order FIR filter to the data from mat sensor The output of this filter is equivalent to first computing die parabola which attains die optimal fit (in die least squares sense) to die data near die point in question, en computing die second denvative of dus parabola, which is used as a measure of shaφness One may then obtain an index ratio of relative shaφness at a particular point in die signal by dividing dus computed peak shaφness bv, e g , die median shaφness of all waves occumng in a mo\ ing background window of the signal If die absolute value of the resulting dimensionless relative shaφness exceeds a given direshold, Cj, dien the wave is classified as a potential spike The preferred nominal value for C 3 is 50, but mav be ad j usted as needed When a potential spike is detected, the polaπtv of die discharge mav then be obtained from die sign of the computed acceleration Fig 8 shows a 10 second segment of the ECoG signal containing a number of spikes along widi die resulting detections (and dieir respective polaπties) made using dus method Now spatio-temporal correlations may be used to (a) reject artifacts, (b) group detected "spikes" into complexes, and (c) detect the occurrence of discharge patterns which may serve as precursors to seizure Source code useful in accordance widi the present invention is included in die microfiche appendix Those skilled in the art will appreciate that genetic algoπduns alone or in conjunction with neural network or fuzzy logic may be effectively used to analyze the spatio-temporal correla¬ tions of detected potential "spikes" for artifact rejection PREDICTION OF SEIZURES THROUGH THE DETECTION OF PRECURSORS It has been found as part of die present invention diat many patients have precursor signals diat consistently precede die onset of bodi die electrographic and clinical components of dieir seizures Three examples descπbed below illustrate such precursors, present detailed mediods for detecting diem in real-lime, and demonstrate their utility in seizure prediction EXAMPLE 1 Often subjects have an onset of large energy, pπmaπly low frequency, quasi-penodic epileptiform discharges which occur seconds to minutes pπor to the electrographic component of a seizure Such patterns are rare to non-existent in die inteπctal state The following is a descπption of how the present invention mav detect die occurrence of such a precursor First, a linear combination of die signals from relevant sensors is formed in a manner that enables the preservation of the polaπty of each discharge so diat diere is no cancellation through supeφosition of waves Then a filter designed to extract precursor spd es widi patient-individualized shape is applied to dus composite signal A geneπc choice of filter for use in this step (when the signal is sampled at 240 Hz), is an IIR filter widi PSD as shown in Fig 9 This filter was designed using a data base of diese precursor signals from vaπous patients in a manner similar to that descnbed earlier for die geneπc seizure detection filter An IIR filter is preferred here instead of an FIR filter because of its advantages in filteπng into such low frequency bands as that required here The output of this filter is then squared, and, as in die seizure detection mediod, a background signal is computed by applymg a 20 second median filter to these squared values, and dien exponentially forgetting (slowly) die result The relevant spikes are d en identified by detecting die instant that the ratio of the current filtered signal (squared) divided by die background signal value exceeds a direshold value, C 4 Since these spikes are now individually detected, die mediod then tests for dieir occurrence in a particular rhydimic manner which is highly correlated with a later onset of the electrographic and then clinical components of seizure Specifically, we impose "peπodicity constraints" which test it there is at least t, seconds and at most seconds between spikes, and that at least N spikes occur in succession according to diese spacings before a detection is signaled The prefeπed nonadaptive settings of the above constants are C, = 100, t, = 1 , = 10, and N = 2 Each of diese parameters (and die filter employed) can be adapted to patterns which may known a pπon, or learned on-lme for a particular subject via retrospective analysis of previously detected seizures Fig 10 presents a graph of an ECoG segment recorded from one of die subjects diat exhibits this particular precursor, and die time of precursor detection using this mediod is annotated, along with the onset times of the electrographic and clmical components of me seizure This mediod has produced a prediction of clinical onset an average of 54 seconds pπor to its occurrence, and a prediction of electrographic onset an average of 42 seconds pnor to its occurrence The detection of this precursor has been followed by a seizure widnn two minutes in 100% of cases analyzed to date EXAMPLE 2 As part of the present invention, it has been found diat signal attenuation or "quieting" precedes, by up to tens of seconds, die onset of die clinical and electrographic components of seizure in many subjects In diose that exhibit this preictal quieting, the application of die following preferred method to detect the onset of this attenuation results in a prediction of die electrographic and clinical seizure components For any signal, {X, , t > 0}, the average signal energy over die interval of time, t, < t < t- j , is given by A(t„t 2 )-- - f 'l x Λ'] 2 ds - Λ -It, The average signal energy, E„ in a moving time window of lengdi T seconds is given by I E, = A(t-T,t) X? ds, /,: which can be computed recursively and efficiently online using die formula E t mn, = E , + U+At) - A(t-T j *Lmt-T ) = E, CΛ7 .V; -T )/Δr The long-time average energy, FL, used as an adaptive background value against which energy changes are measured, is given (recursively) by H,^ = X H, l -λ) £„ ■Δr with the preferred value of lambda being slightly less dian 1 The above recursions can be initialized using die formulae E τ -- [ τ X ds, H.-E τ J O Now a ratio, R,, is computed as , and a precursor detection is made die instant that this ratio exceeds a threshold value C, Note dial an increase in dus ratio corresponds to a decrease in average signal energy Preferred nominal values for die parameters in this mediod are T = 5, and C, = 5, but diese aga may be adapted to particular subjects This method has produced a prediction of clinical onset an average of 23 seconds pπor to its occurrence Figs 1 1 and 12 present a graph of an ECoG segment recorded from a subject that exhibits this par¬ ticular precursor, and indicates when die detection is made using dus method relative to die clinical and electrographic onset times of the seizure The detection is made on die graph 278 seconds after an arbitrary zero, which is 15 seconds pπor to the independendy determined electrographic onset, and 19 seconds pπor to die time of clinical seizure onset EXAMPLE 3 It has been found as part of die present invention mat, for some subjects, certain sudden changes in die power spectral density (PSD) of die signal mav be used to predict an impending seizure onset For example, a sudden significant drop in the median frequencv of the signal (defined in Appendix 2) is a consistent precursor to seizure for some subjects The following is a descπption ot the pieteπed mediod for detecting the occuπence of such a precursor Begin by computing the median frequency of die particular signal of interest in moving windows of lengdi T t (as descπbed in Appendix 2) Compute a background median frequency using an average of die median frequencv v alues in a moving window of lengdi T 2 Then compute die ratio of die background median frequency divided bv the median frequency in die current T, second window If dus ratio exceeds a threshold value C , a detection of dus precursor is immediately signaled The preferred nominal/non-adaptive choices ot parameters are T, = 256 240 sec (approx 1 067 sec ), T, = 60 sec , and C 6 = 5 Fig 13A shows a 5 minute segment of ECoG data which contains a seizure in the last minute The times of electrographic and clmical seizure onset, and the time at which die event button was pressed are annotated (dashed, dash-dot, and dotted lines, respectively) Fig 13B shows die graph of die ratio descπbed above over dus 5 minute segment, with a detection occurπng 12 seconds pπor to die electrographic onset, 15 seconds pπor to the clmical onset, and 18 seconds pπor to die patient's pressing of the event button In die example used m Fig 13, it should be noted diat there ts some signal energy attenuation as well (die precursor descπbed in the previous example), but that dus is not used in die detection of die precursor described now Odier precursors which may be relevant for predicting an impending seizure for a particular subject or group can be isolated using modem pattern recognition techniques As mentioned previously, preictal or intenctal patterns which are present in the output of our seizure imaging method (which uses spatio-temporal inteφolation of the ratios or other relevant quantities computed at each sensor) are strong candidates for precursoi s Another useful method for determining precursor signals for a given subject or group makes use of waveform classification and cluster analysis, followed by pattern recognition techniques applied to the resulting sequence of waveform "labels " The following illustrates how dus can be done One begins by segmenting an arnving input signal into individual "waves " This can be done in a variety of wavs, for example, one could define a wave as 512 consecutive data points, or, more adaptively, as die number of data points between 2 successive baseline crossings of die signal As each new waveform is segmented it can be extensively analvzed and subsequenth classified into a (possibly evolving) library of distinct waveforms or "cvma " For example, die duration, amplitude, number of baseline crossings, arclengdi, maximal shaφness, number of local maxima and minima, area bounded between the waveform and a hoπzontal axis, total power, and the fraction of power in a particular frequencv band (and an\ number of other quantities) could each be computed (and, if desired statistically normalized), widi the resulting n measures used to form a vector in n-dimensional space quantifying diat particular waveform (in this example, n=9) The distances (eidier Euclidean or non-Euclidean) between dus new "pomt" in n-dimensional space and a library list of other "points" can dien be computed and analyzed to see which waveform or "cymeme" in the library most closely resembles the new waveform In this way, the put signal can be labeled as a sequence of cymemes (or indices into a list of waveforms), and die resulting "cyma" can be analyzed as descπbed above for die occurrence of patterns which may serve as precursors to a given change of state Such waveform lists can be constructed from available data using currently known methods of cluster analysis, and neural networks can be employed m making online or offline decisions classifying a given waveform Specifically, we have successfully used a competitive learning network trained widi die Kohonen learning rule and an adaptive learning rate which decreases over time for this task of classifying the sequence of n-dimensional points into groups These analyses can be performed consecutively where, in each step, a duferent analysis method can be used CORRELATION DETECTION AND AUTOMATIC PRECURSOR IDENTIFICATION AND ISOLATION This section descnbes the preferred mediods for automatic identification of signal characteπstics which are significantly correlated widi later changes of state in dus subject's brain This is based on correlational analysis and pattern recognition techniques applied to die signal recorded pnor to each change of state The transformations which apply to a given input signal or set of signals (e g , FIR filteπng, fast wavelet and Fouπer transforms, etc ) decompose die input and present its information content in an organized and useable form The anginal signal, and die product of diese transformations are dien automatically analyzed for the occurrence of patterns sigmficantlv correlated widi detected changes of state Signal analysis for correlation may occur on-line, or off-line from signals previously recorded and stored in memory Further, the analysis can be done on die basis of predetermined patterns or patterns developed dirough correlation analysis, or bodi One may select die segments of signal for analysis which precede diese changes of state with "markers" in some externally controlled way, or one may simply let die software accumulate correlations between "major changes" and precursors in an inclusive fashion for eventual use It is also important to note diat die process can either be done off-line using data obtained from die subject or putative patterns, or on-line by automated systems installed as part of the device The following is an example descnbing die use of correlations for determining average signal power Let b^ be the 1 th time coefficient in the W wavelet basis expansion (see next section for more details), using a fundamental wavelet tune scale dt Let the average value of b^ over an interval, chosen for illustration to be one minute, be denoted < b k (t) > This is an average over roughly !„,„= 2 kΛ " values of 1 The interval begins one minute pπor to time t Let < p(t) > be the average power in die signal over die same time interval, and < p > be die average power over many previous intervals, such as 100 Form the running deviation of coefficients from the average, b 4 - < b k (t) > Form ie running deviation of die power from its long term average, < p(t) > - < p > A con elation matrix C„( ) is formed bv multiplying C„(k) = (<p(t)> - < p >)(b Λ -<b k (t)>) This matπx depends on die level, k, die time step within die interval, 1, and the interval start time t If diere is no correlation between the signal and die power in a particulai level k, dien the matπx for that k value will be a random vaπable m the index 1 If a persistent correlation begins at time t + l*dt, at some level k*, then the product will nse at pomt 1* and stay large, up to statistical fluctuations Applying dus procedure over many intervals of "normal" or background state, typical values of C„(k) will be fairly independent of 1 and t, and only depend on the level k Distnbutions of these values are formed and evaluated When C,(k) πses well above a statistically likely value it represents a correlation between b^ and the power, < p(t) >, in diat mterval In one application, restπctmg the analysis first to intervals marked t* where < p(t*) > πses well above die background, one identifies die region of the seizure Only die C„(k) values for die immediately preceding interval need to be kept m dus case Then by examining the correlations C„.(k) as a function of level k and time 1, the level or levels k* where large correlations occur for 1 > 1* can be determined automatical The time for die precursor to predict m advance the seizure is found by computing (l m „ - l*)dt These values of k* and 1* can be output and stored, leading to automatic recognition of precursors Another similar method is to tra any assembly consisting of a number of logic elements widi connected direshold rules (such as a neural network) to recognize conelations between the precursor si nals, and die macroscopic change of state in this case die correlations between precursors and state change mav be automatically registered and analyzed widiout die intermediate step of forming correlation matnces The same technique can be applied to linear combinations of the signal after application of vanous signal filters, e g , considering simultaneously denved wavelet coefficients from different levels of resolution after application of the discrete wavelet transform One could also project the input sιgnal(s) into various odier bases (i e , apply other signal decompositions) and monitor the resulting signal components as they evolve over time ("pattern recognition") THE USE OF GENETIC ALGORITHMS AND GENETIC PROGRAMMING IN THE ADAPTATION AND EVOLUTION OF THE METHODS FOR SEIZURE, SPIKE, AND PRECURSOR DETECTION Algorithms (GA) and genetic programming (GP) may be used as part of die overall strategy of algoπthm adaptation and evolution in die mediods for seizure and spike detection, and seizure prediction The problem of detection and prediction of seizures is a highly complex one, due to die large intra- and inter-individual variability of brain signals intenctally and ictally The problem of selecting parameters such as filter coefficients, diresholds, window lengdis, type of signals, etc to optimize a particular performance cntena is ideallv suited for die use of GA In dus mediod, parameter sets are assembled into chromosome- 1 ike strings which are interchangeable and form die "first generation " This first generation is evaluated by a fitness function which will assign a score to each element of die generation After the sconng is completed, the "best" elements are saved and reproduced for the next generation, possibly combined toge ier ("cross-over"), and/or new elements ("mutations") are introduced This process of generation, reproduction and testing is repeated, until elements of die resulting generation achieve die desired level of performance for the particular application The following example illustrates how GA mav be utilized to choose a time-mvanant FIR that maximizes performance The first generation consists ol a group of bandpass filters, each with 2 Hz bandwiddi, of varying orders (but less dian 240 Hz ), centered at odd integral frequencies between 1 Hz and 1 19 Hz and designed according to die Parks-McClelland method The resulting sensitivity and specdicitv are measured when each filter is used m die seizure detection method presented herein (e g , bv computing the mean squared time between detection and the electrographic onset as independently scored bv an electroencephalographer), and the best filters are saved and "reproduced" for the next generation These filters aie also combined together via cascading ("cross-over"), and new "mutation" filters of different bandwiddis are added at random frequencies Mutations may also involve randomly decreasing or mcreasmg die order of certain filters from one generation to the next, but staying below die upper limit of 240, and or using anodier filter design mediodology This evolution is continued until some stabilization is reached, or until die resulting performance achieves a high enough degree of sensitivity and specificity for the given data base. Genetic programming, a form of program induction, enables die method/system to evolve (widiout external inputs or re-programming) based on internal fitness functions GP may enable die method to self-learn and extract the most relevant and useful charactenstics of the signal Having thus descπbed die preferred embodiments of the present invention, the following is claimed as new and desired to be secured by Letters Patent APPENDIX 2 SOME BACKGROUND INFORMATION ON MATHEMATICAL METHODS This Appendix presents some background detail helpful in understanding die preferred mediods of die present invention THE DISCRETE FOURIER TRANSFORM It is well known diat every discrete signal defined on eveniy spaced discrete time points {t^.t,, ,t N i } , where t k = 2πk/N, can be inteφolated by a tngonometnc polynomial which can be wntten as Λ/+Θ - 1 /(/) = ∑ ce ' j ; (/ = v /7 T) where θ = l / = 0V- 2)/2 if N is even and This discrete Fouπer transform (DFT) represents a given signal (or "time series") as a superposition of sine and cosine waves with duTerent frequencies and amplitudes The number of Fouπer coefficients is equal to die number of data points in the signal and reproduces (in some sense) the information contained in die onginal signal In practice, the DFT is computed usmg die fast Fouπer transform (FFT) Once die signal is decomposed into these fundamental frequencies, one may analyze die signal's components in separate frequency ranges (bands), and examine die coefficients for dominant frequencies One generallv computes an estimate of die power spectral density (PSD) from the Fouπer coefficients in order to determine the dominate modes of die system The simplest such estimator is the penodogram, which is a graph of die squares of magnitude of each of die Fouπer coefficients, in order to see dominant modes of the system As used herein, PSD means anv of the estimators commonly employed in signal analysis The PSD of the signal {f(t o ),f(t|), ,f(t N .,) } is obtained from the Fouπer coefficients, {c,}, as Here p s may be mteφreted as die total power at frequency w, in the segment of signal which was transformed There are M + θ ddϊerent frequencies, w,, at which signal power is computed, and diese frequencies are evenly spaced between w 0 = 0 Hz, and M+ , = F./2 (the so-called Nyquist frequency) which is half of die sampling frequency, F„ of the signal (240 Hz above) The PSD contains precise frequency information about die given signal ("frequency localization"), but contains no information about die particular times at which a given frequency occurred However, in e preferred applications, die time of a particular frequency change is important The windowed fast Founer transform (WFFT) was created to address die lack of temporal resolution of die discrete Founer transform, die signal is partitioned mto successive segments or windows and the PSD in each window is computed, allowing the user to track changes in the frequency content of die signal window-by- window, i e , in time It must be understood, however, that die temporal resolution of die time of frequency changes in e signal is only on the order of die lengdi of die window Also, due to die discrete nature of die data, diere is a degradation in die ability to accurately compute the full frequency content of a signal as the size of window is decreased since die number of frequency coefficients returned by the DFT is equal to the number of data points transformed (the window length) An important application of Founer mediods in die present invention involves die analysis of die temporal evolution of vanous parameters associated with die frequency domain representation of the signal (I e , die Fouπer ttansformed signal) To accomplish dus, one first computes the windowed fast Founer transform (WFFT) of the signal obtained from a s gle sensor in a moving window of current data values and dien die corresponding PSD estimate for each window Then each PSD curve is converted to a probability density function (pdf) via normalization, and die resulting densities are treated as if they were characterizations of a non-stationary random frequency To be more precise, suppose p(x) is a non-negative function defined on die interval, [0,L],(0 < x < L) Without loss of generality, one may assume mat the area under die graph of p(x) is equal to one, I e , one may treat p as a pdf If /oP(x)dx * 1 , dien one may simply redefine p via normalization, as P x) Jo Pi* * 1 * Consideπng p(x) as a pdf, one can compute statistics of the signal frequency distnbution usmg probabilistic mediods. In doing so, we obtain a number of parameters from the pdf in each window of time and monitor their absolute and relative temporal changes along with die time evolution of vanous relations between them Some of the parameters computed are measures of central tendency m the PSD, including order statistics such as die median frequency (w.r t PSD), vanous moments of die pdf (e.g , die mean frequency), modal frequencies (l e , die frequency with maximum power), and odier parameters measure fluctuations in frequency in each w indow (e g , the frequency vanance, skewness, and kurtosis) Precise formulae are now presented for some of diese measures which are most commonly used If p(x) is a continuous p.d f (defined discretely in dus case) which is zero for x < 0 and x > L, dien the mean of die distnbution (also known as the "first moment," oi "expected value") is given bv L μ = f xp(x) dx. 0 the n* moment is given by „ = / x "p(x) dx The vanance of die distnbution is given by μ 2 -μ 1 The median, m f , of the distnbution is defined by die equation "7 / PM dx = - = 50% Note that one may also define odier percentiles (order statistics) in this manner The median frequency of a signal in a given window of data is the frequency which divides the area under die PSD in half The mode, M f , of die p d f. is given by M r = argmax{p(x)} The modal frequency of the PSD (1 e , die frequency at which die maximum power occurs) is dius defined Fig 14A presents a signal compnsed of 1024 points of ECoG data (about 4 267 sec ) recorded from a subject dunng an interictal (non-seizure) peπod Fig 14B illustrates the corresponding power spectral density (PSD) of die signal, showing the modal frequency, median frequency, and mean frequencv of die signal, computed according to die above definitions One can use the modal frequency and vaπants of dus concept to test die signal for rhythmicity, I e , to detect segments of data which are nearly peπodic When this quasi-peπodicity ot the signal occurs, die power in die signal is concentrated at a few resonant modal frequencies Many useful measures capable of detectmg hypersynchronous patterns of neuronal fiπng often found in the recruitment and entrainment associated with seizures can be deπved using a combination of measures such as those defined above For example, to detect hypersynchronous behavior of neurons focusing attention near 15 Hz activity, one can compute a frequency-biased functional of the modal frequency such as e l0 p(M f ), where p is die PSD of the signal in, for example, a moving window of 256 points, and M r is the modal frequency for diat window One mav men monitor the evolution ot dus measure for significant increases relative to background to produce a rapid detection of dus type of signal activity This mediod for detecting rhythmic discharges in the signal can be further enhanced bv weighing a measure such as that above by other measures of quasi-periodicity that can be computed from die signal itself (widiout first applying die FFT) For example, the reciprocal of the standard deviation of ten successive mter-zero- crossings (or mteφeak intervals) increases dramatically in the event of hypersynchrony The product of this measure widi die one mentioned in die last paragraph provides an excellent method for the detection of dus type of phenomena A number of odier nonlinear functions based on these quantities (e g , die product of the average energy and die inverse of die median frequency) can also be utilized as a means to obtain precursor information regarding an imminent change of bram state THE DISCRETE WAVELET TRANSFORM While die Founer transform descπbed above gives precise frequency localization of a particular signal, die discrete wavelet transform (DWT) has recently gained populaπty because of its ability to provide useful temporal and frequency information simultaneously The DWT expresses a given signal in terms of a basis consistmg of translations and dilations of a single "mother wavelet," W(j) To be more precise, a given signal {x,}",,, is expressed as X j = Σf bmWJj), l,k here W ar Q =2 2 i l turXt21 --1'j. - k) The wavelet coefficients are defined by The first "dilation" index of the coefficient controls die level of resolution of information, while the second "translation" index controls the temporal information contained in die coefficient Representing a given signal in terms of wavelet coefficients rather dian in the usual time domain is analogous in many ways to representing a musical composition as sheet music radier than looking at die graph of the sound waves through an oscilloscope as the piece is played Each chord on die sheet music contains information about both die frequencies that are played and d e tune at which they occur As widi die DFT, fast, efficient algoπdims exist to compute the wavelet coefficients {b^} The fast wavelet transform (FWT) which is based on die pyramid algoπthm developed by Mallat makes the FWT even easier to compute than d e FFT The FWT is applied to a given signal in windows of data of length 2" points, for some prescπbed value of n (e g , n=6) The window size may vary based on a particular application and implementation Use of die FWT may be ctesirable if one wishes to monitor signal changes in multiple frequencv bands simultaneously and diese desired frequencv bands match those obtained by dilations of a particular mother wavelet One mav then group the wavelet coefficients by "level," 1 e , by like dilation factor, ordered in each level according to their progi ession in time The time between level 1 wavelet coefficients is 2'dt, where dt is the sample interval of the ongmal signal Hence, die level 1 coefficients contain die finest resolution (i e , the highest frequency) information contained in die signal, and each lower level provides signal information at mcreasmg time scales and decreasing frequency ranges Level 1 corre- spends to die smallest scale of resolution, and contains twice as many coefficients as level 2, which contains twice as many as level three, and so on Recall that die EEG signal used in illustrating die preferred seizure detection embodiment desenbed above was sampled at 240 Hz, so that a new data point appears roughly every 004 seconds Computing die FWT usmg, e g , 64 data points ( 267 sec ) results in 32 level 1 coefficients, 16 level 2 coefficients, 8 level 3 coefficients, etc Thus for each level, diere is a corresponding time seπes of wavelet coefficients representing die temporal evolution of signal power in a number of frequency bands, each higher frequency level "covering" a frequencv band of widdi twice that of the next lower frequency level Fig 15 shows (the absolute value of) diese seπes for levels I -4 for a typical intenctal segment of 512 data points (about 2 seconds) togedier with the oπginal signal A LEAST SQUARES ACCELERATION FILTER In the mediod descπbed earlier herein for computing the shaφness of a given waveform at a particular pomt, one first obtains an optimal parabolic fit to the data near die point in question One dien may use die second denvative (i e , die acceleration) of the resulting parabola as a measure of shaφness at the peak The cπteπon of minimizing the mean square error of this fit may be applied to obtain the best fitting parabola (in a least squares sense) This subsection provides the necessary mathematical details for accomplishing this step If i(\y , J = 1 > n > are data points to be inteφolated by die parabola, p(x) = a,x : + a,x + a„, then the optimal coefficients a 2 , a,, and a^ which minimize the mean square error n ∑ (y - p( χ )) 7 = 1 over all choices of such coefficients, are obtamed by solving the equation [a 2 <J, a 0 )' - (X * X)'(X * )\ where , = j 1 , for 1 < I < n, 1 <j < 3, Y = [y,„ yj', and * denotes die Moore-Penrose pseudoinverse of die matπx X If we let A = X * X and B = XΥ, dien we have A,, = ££_, ? >, 1 ≤ l, j ≤ 3,and the column vector B, = ∑J., xJ ι= 1 ,2,3. Once one solves this equation to obtain the optimal value for the leading coefficient, a 2 , die second denvative of d e parabola is equal to twice this optimal coefficient The sign of this second denvative indicates the polaπty of die potential spike as well, e g , a negative second derivative indicates that the peak of die spike is a local maximum for the signal. The above formulae for computing a 2 may be simplified using die symbolic pseudo-inverse of die 3 3 matrix and subsequent matπx multiplication, resulting in die following formulae for a "least squares acceleration filter" which have been implemented to compute this shaφness measure in real time (l e , the computation of die second denvative at a given point requires only p(p- 1 ) floating point operations) a(p,dt) = dt 4 p (p-1) (2p-l) (3p 2 -3p-l)/30, b(p,dt) = dt 3 p 2 (p-l) /4, c(p,dt) = dt 2 p(p-l)(2p-l)/6, d(p,dt) = dtp(p-l)/2, e(p) = p, A = c e - d 2 , B = cd-be, C = b d - c 2 , D = aA + 2bcd-eb 2 -c\ E = [A B C]/D, f=[0dt 2 (2dt) 2 (3dt) 2 ((p-l)dt) 2 g = [0dt2dt (p-l)dt], h=[l 11 1], F = 2E[f g'hT, and a,(k-[[(p- 1 )/2]]) = F(p) x(k) + F(p- 1 ) x(k- 1 ) + F(2) x(k-p+2) + F( 1 ) x(k-p+ 1 ), where {x k ,k= 1 ,2, } is die signal being analyzed, p is die number of points used in die parabolic fit (e g p=7), and dt is the time step of die signal being analyzed Note diat, for a fixed value of p, the filter coefficients F may be computed once and stored for later use in the computation of a, from die above FIR filter The delav in computmg a, at a given point, which is instilled by usmg [[(p- 1 )/2]] (I e , die greatest integer less dian or equal to (p- 1 )/2) future data points, is only (p+ 1 ) *dt2 seconds For example, widi p=7 and dt= 1 /240, the delav is 1 /60 sec TIME-WEIGHTED AVERAGING In the methods descπbed above, there are many cases in which it is desirable to compute some background or reference value for a particular signal By accurately representing die history of die signal, one may improve e memod's ability to identify relevant changes which standout from dus background In dus invention, time-weighted averaging is prefeπed A subset of these techniques are able to determine a suitable long time-average of any desu-ed functional of die input signal in a very computationally efficient manner, taking into account die entire recorded history of die signal and using only a minimal amount of computer memory A particular example of the more general mediod is that of exponential forgetting, in which the recent signal information is weighted more heavily that die distant past The general form referred to as a time-weighted average of a contmuous-time signal {X,,t>0} widi tune-weight {f,„t > 0, 0 < s < t} is given by {m,,t>0}, where f /,Λ* M τ - ϋ The discrete version of dus time-average is obtained bv simply replacing die integrals in the above definition by the corresponding summations over die index vanable s For certain time-weights, the above formula may be written recursiveh , in particular, dus may be achieved for the case when f is independent of t If die time-weight f = e**, then a version of this time-weighted average can be simplified to the exponentially forgetting mediod that are employed in some of the embodiments of die mvention descπbed herein Vaπants on this choice may be useful for certain applications For example, by making λ a penodic function of s widi a peπod of one day, the time-weighted average may be used to weight signal information at a particular time of day more heavily than at other times This may be particularly important for an individual who may commonlv have seizure events only during certain tunes of day Also note that the choice of time-weight f M = χ iu ^ gives πse to the usual movmg average I A ι-t> Where χ denotes die indicator function In the more general case of "time and state weighted averagmg," die weight function, f, may also depend upon die signal X itself This additional generalization incorporates the case in which the historical average not only depends on die epoch leng i over which die signal value is averaged, but also upon die signal value over dus epoch This technique may be useful when one desires to allow certain signal charactenstics to, b\ their existence, modify die desired signal background average Appendix 1 The following is a group of MAT AB scripts and function files, and C subroutines which are used in our signal analysis. We have omitted some plotting routines to display the data. The code is organized by it's particular use and a brief description of each piece of code is given. online.c This c program contains subroutines necessary to implement the seizure detection algorithm in real-time. There are two primary functions, and utility functions that they call and a MATLAB Cmex "gateway" program which calls them. SetupCompute initializes arrays and pointers at the beginning of monitoring, while Compute is called once for each acquired raw data point. The program filters the raw signal using a FIR filter, computes the median of filter coefficients (squared) in a foreground moving window, and compares the ratio of this median to one similarly computed from a background window delayed from the end of the foreground window. If this ratio is above a preset threshold, a seizure detection is made. ***** online.c *************** /* Mark G. Frei 10-10-94 */ /* online.c The calling syntax is: [ratios, fg_vals, bg_vals] = online5_mex(x, filter_coeffs, FG_LENGTH, DELAY_LENGTH, BG_LENGTH, THRESHOLD) ; Input arguments: x signal to be filtered; filter_coeffs filter coeffitients; FG_LENGTH foreground length; DELAY_LENGTH delay length; BG LENGTH background length; THRESHOLD threshold. Output arguments: ratios the ratio of filtered foreground signal to the filtered background. fg_vals filtered foreground signal bg_vals filtered background signal * / /include <stdlib.h> #include <math.h> /*—» /include <stdio.h>*/ /include "mex.h" /* /include "compute.hpp" */ /* /include "pdmalβ.hpp" */ /* extern Status_Word statusWord; */ /* extern float ratio; */ /* extern int raw; */ /* extern Computation_Parameters compParam; */ float raw; /* Should be global */ float fg_med, bg_med, ratio; int DATA_LENGTH, FG_LENGTH, DELAY_LENGTH, BG_LENGTH, NPTS, FILTER_LENGTH, THRESHOLD; double *filter_coeffs; /* DEFINITIONS: */ /define max(A, B) ((A) > (B) ? (A) : (B) ) /define min(A, B) ((A) < (B) ? (A) : (B) ) /define FILTER_LENGTH_MAX 600 /define lambda 0.9997 /* Forgetting factor for background */ /define bg_threshold 5.0 /* Level of ratio used to control when to update background */ float new_data[FILTER_LENGTH_MAX] ; /* Here are the definitions of the data structures */ struct data_node{ struct data_node *next; struct index_node *ind_ptr; float data; } (*filter_data) []; /* Define pointers to this linked list */ struct data_node *begin_fg_data, *end_fg_data, *begin_bg_data, *end_bg_data; struct data_node *begin_delay_data, *end_delay_data; struct index node{ struct Tndex_node *prev, *next; struct data_node *dat_ptr; } (*ind_fg)[], (*ind_bg)[] ; /* Define pointers to this linked list */ struct index_node *i_fg_min, *i_fg_med, *i_bg_min, *i_bg_med; struct index_node *ind_place, *ind_fg_new, *ind_bg_new; ************************ Function Prototypes: ***************************/ void SetupCompute( void ) ; void Compute( void ) ; struct index_node *remove_node( struct index_node *iptr) ; int insert_after(struct index_node *place, struct index_node *new_node) ; struct index_node *search( struct index_node *iptr, struct index_node *min_ptr, float val) ; /A********************************************************* *****************/ void SetupCompute( void ) /* This function initializes all the data structures and arrays used in Compute */ { int i; /* Allocate big arrays */ filter_data=calloc(NPTS, sizeof(struct data_node) ) ; ind_fg=calloc(FG_LENGTH, sizeof(struct index_node) ) ; ind_bg=calloc(BG_LENGTH, sizeof(struct index_node) ) ; /* Initialize new_data array */ for (i=0; i < FILTER_LENGTH; i++) new_data[i]=0.0; /* Initialize and connect filter_data, ind_bg, and ind_fg structures */ bg_med=0.0001; ratio=l.0/THRESHOLD; for (i=0; i < NPTS-l; i++) { (*filter_data) [i] .next = &( (*filter_data) [i+1] ) ; (*filter_data) [i .data = bg_med; /* Some arbitrary value */ } (*filter_data) [NPTS-l] .next = &( (*filter_data) [0]) ; (*filter_data) [NPTS-l] .data = bg_med; for ( i=0 ; i < FG_LENGTH-1 ; i++) { ( *ind_fg) [ i+1 ] . prev = & ( (*ind_fg) [ i ] ) ; (*ind_fg) [ i] . next = & ( ( *ind_fg) [ i+l ] ) ; ( *ind_fg) [ i] . dat_ptr = & ( (*f ilter_data) [NPTS - 1 - i ] ) ; ( *filter_data) [ NPTS - 1 - i ] . ind_ptr = & ( ( *ind_fg) [ i ] ) ; } (*ind_fg) [0] .prev » &( (*ind_fg) [FG_LENGTH - l]); (*ind_fg) [FG_LENGTH - l].next = &( (*ind_fg) [0]) ; (*ind_fg) [FG_LENGTH - l].dat_ptr = &( (*filter_data) [NPTS - FG_LENGTH]) ; (*filter_data) [NPTS-FG_LENGTH] . ind_ptr = &( (*ind_fg) [FG_LENGTH - 1]) ; for (i=0; i < BG_LENGTH-1; i++) { (*ind_bg) [i+l] .prev = &( (*ind_bg) [i]) ; (*ind_bg) [i] .next = &( (*ind_bg) [i+1] ) ; (*ind_bg) [i] .dat_ptr = &( (*filter_data) [i]) ; (*filter_data) [i] .ind_ptr = &( (*ind_bg) [i] ) ; } (*ind_bg) [0].prev = &( (*ind_bg) [BG_LENGTH - 1]); (*ind_bg) [BG_LENGTH - l].next = &( (*ind_bg) [0]) ; (*ind_bg) [BG_LENGTH - l].dat_ptr = &( (*filter_data) [BG_LENGTH - 1]); (*filter_data) [BG_LENGTH - l].ind_ptr = &( (*ind_bg) [BG_LENGTH - 1]) ; /* Initialize other pointers to structures */ begin_fg_data = &( (*filter_data) (NPTS - 1]); end_fg_data = &( (*filter_data) [NPTS-FG_LENGTH] ) ; begin_delay_data = &( (*filter_data) [NPTS-FG_LENGTH - i]); end_delay_data •= & ( (*filter_data) [NPTS-FG_LENGTH-DELAY_LENGTH] ) ; begin_bg_data = & ( (*filter_data) [NPTS-FG_LENGTH-DELAY_LENGTH - 1]) ; end_bg_data = &( (*filter_data) [0]) ; i_fg_min = & ( (*ind_fg) [ 0] ) ; i_f g_med «= & ( (*ind_fg) [ (FGJLENGTH/2 ) ] ) ; i_bg_min = & ( (*ind_bg) [ 0] ) ; i_bg_med = & ( ( *ind_bg) [ (BGJ ENGTH/2 ) ] ) ; } /********************************************************** *****************/ void Compute( void ) { int i , iflag_fg , iflag_bg; float fc , int_rem, int_rem_bg , int_med, int_add , tmpl , bg_med_tmp ; /* Begin by computing the inner product of recent data with filter */ fc= raw*( (float) filter_coeffs[FILTER_LENGTH-l] ) ; for (i=0; i <= FILTER_LENGTH-2; i++) { fc= fc+ new_data[i+l]*filter_coeffs[i] ; new_data[i]=new_data[i+1] ; } new_data[FILTER_LENGTH-1]=raw; fc=fc*fc; /* Store oldest background filter_data value for a later comparison */ int_rem_bg= end_bg_data->data; /* Detach "old" index_nodes corresponding to end_fg_data and end_bg_data */ /* making a temporary copy of each so no pointers are lost */ ind_fg_new*remove_node( end_fg_data->ind_ptr ) ; ind_bg_new=remove_node( end_bg_data->ind_ptr ); /* If a node corresponding to a minumum value is removed, then move min pointer up one */ if (ind_fg_new == i_fg_min) i_fg_min=i_fg_min->next; if (ind_bg_new == i_bg_min) i_bg_min=i_bg_min->next; /* If a node corresponding to a median value is removed, set a flag to recompute it later */ iflag_fg=0; if (ind_fg_new == i_fg_med) iflag_fg=l; iflag_bg=0; if (ind_bg_new == i_bg_med) iflag_bg=l; /* Search ind_fg (beginning at place where value corresponding to begin_fg_data */ /* was placed) to find where the filter coefficient corresponding to end_bg_data */ /* (our new filtered data value) belongs in sorted order. */ - 3.6 — tmpl=(i_fg_med->dat_ptr->data)-(begin_fg_data->data) ; tmpl = tmpl*(fc-(i_fg_med->dat_ptr->data) ) ; if (tmpl > 0) { ind_place=search( i_fg_med, i_fg_min, fc) ; } else { ind_place=search( begin_fg_data->ind_ptr, i_fg_min, fc); } /* Insert detached node after place determined by the search */ insert_after( ind_place, ind_fg_new ) ; /* Interconnect the newly placed index node with the corresponding data_node */ ind_fg_new->dat_ptr = end_bg_data; end_bg_data->ind_ptr = ind_fg_new; /* Update the minimum pointer (if necessary) . Note that if the new filter_data value */ /* is less than or equal to previous minimum, then the new node will be inserted between*/ /* i_fg_min and i_fg_min->prev the latter of which has its dat_ptr pointing to the */ /* maximum foreground filter_data value */ if (fc <= i_fg__min->dat_ptr->data) i_fg_min=i_fg_min->prev; /* Update median pointer (if necessary) */ int_rem=end_fg_data->data; /* filter_data value removed from foreground */ int_med=i_fg_med->dat_ptr->data; /* median from previous foreground window */ int_add=fc; /* newly added filter_data value */ i_fg_med=ind_place; } /** Now we do the same thing to find the median for the background window **/ /* Search ind_bg (beginning at place where value corresponding to */ /* begin_bg_data was placed) to find where the filter coefficient */ /* corresponding to end_delay_data belongs in sorted order. */ tmpl=(i_bg_med->dat_ptr->data)-(begin_bg_data->data) ; tmpl = tmpl* ( (end_delay_data->data)-(i_bg_med->dat_ptr->data) ) ; if (tmpl > 0) { ind_place=search( i_bg_med, i_bg_min, end_delay_data->data) ; } else { ind_place=search( begin_bg_data->ind_ptr, i_bg_min, end_delay_data->data) ; } /* Insert detached node after place determined by the search */ insert_after( ind_place, ind_bg_new) ; /* Interconnect the newly placed index node with the corresponding data_node */ ind_bg_new->dat_ptr = end_delay_data; end_delay_data->ind_ptr = ind_bg_new; /* Update the minimum pointer (if necessary) . Note that if the new */ /* filter_data value is less than or equal to previous minimum, then */ /* the new node will be inserted between i_fg_min and i_fg_min->prev */ /* the latter of which has its dat_ptr pointing to the maximum */ /* foreground filter_data value */ if (end_delay_data->data <= i_bg_min->dat_ptr->dat ) i_bg_min=i_bg_min->prev; /* Update median pointer (if necessary) */ int_rem=int_rem_bg; /* filter_data value removed from background */ int_med=i_bg_med->dat_ptr->data; /* median from previous background window */ if (iflag_bg==l) { int_med=int_rem_bg; } int_add=end_delay_data->data; /* newly added filter_data value */ if ((int_rem < int_med) && (int_add > int_med) ) i_bg_med=i_bg_med->next; if ((int_rem > int_med) && (int_add <= int_med) ) i_bg_med=i_bg_med->prev; if ((int_rem == int_med) | | (iflag_bg==l) ) /* Update "the hard way" */ { ind_place=i_bg_min; for (i-1; i<=(BG_LENGTH/2) ; i++) { ind_place=ind_place->next; } i_bg_med=ind_place; ) /* Now place new filter coefficient (squared) into place in data_node */ /* vacated by the last value in background which we are throwing away */ end_bg_data->data= fc; /***** Move begin/end data_node pointers forward *****/ begin_fg_data=begin_fg_data->next; begin_bg_data=begin_bg_data->next; begin_delay_data=»begin_delay_data->neκt; end_fg_data=end_fg_data->next; end_bg_data=end_bg_data->next; end_delay_data«end_delay_data->next; /* Compute ratio from medians and return fg_med= i_fg_med->dat_ptr->data; bg_med_tmp= i_bg_med->dat_ptr->data; if (fabs(bg_med_tmp) < le-12) { ratio=0.0; bg_med=bg_med_tmp; } else { if (ratio >= bg_threshold/THRESHOLD) / * don ' t update bg_med */ { ratio = fg_med / bg_med; } else { /* update bg_rαed using exp forgetting */ bg_med = lambda*bg_med + (3.-lambda) *bg_med_tmp; ratio = fg_med / bg_med; } ratio - ratio/THRESHOLD; } } /* end of compute */ ********************************************************** ***************** struct index_node *remove_node( struct index_node *iptr) /* This function detaches an index node and connects the */ /* adjacent nodes, returning a pointer to the detached node */ { (iptr->prev)->next = iptr->next; (iptr->next)->prev = iptr->prev; return (iptr) ; } /A********************************************************* *****************/ int insert_after(struct index_node *place, struct index_node *new_node) /* This function inserts the index node pointed to by new_node after the */ /* node pointed to by place (i.e., the data value corresponding to */ /* new_node is slightly larger than that corresponding to place) */ { new_node->prev=place; new_node->next=place->next; place->next->prev=new_node; place->next=new_node; return 0; } ********************************************************** *****************/ struct index_node *search( struct index_node *iptr, struct index_node *min_ptr, float val) /* This function performs a linear search through filter coefficients */ /* corresponding to elements of an index node, to find the no - place */ /* where the new filter coefficient, val, belongs in an ordered list */ /* The input index_node pointer, iptr, points initially to where the search */ /* begins and is incremented as the search proceeds. */ /* The function returns a pointer to the index_node after which a node */ /* corresponding to val should be inserted in the list. */ { float temp; temp=iptr->dat_ptr->data; if (val > temp) /* Need to search larger values than initial guess */ { while ((val > temp)) { iptr=iptr->next; temp=iptr->dat_ptr->data; if (iptr==min_ptr) { iptr=iptr->prev; return(iptr) ; ) } iptr=iptr->prev; /* Back pointer up one node since we've just passed it */ return(iptr) ; } else { while (val <= temp) { iptr=iptr->prev; temp=iptr->dat_ptr->data; if (iptr==min_ptr->prev) return(iptr) ; } return (iptr) ; /* No need to back up in this direction of search */ } } /********************************************************** ***************** static /ifdef STDC void online( double ratios[], /* Declare output */ double fg_vals[], double bg_vals[] , double x[] /* Declare inputs */ ) /else online(ratios, fg_vals, bg_vals, x) double ratios[], /* Declare output */ fg_vals[ ] , bg_vals[ ] ; double x[]; /* Declare inputs */ endif { int i,jj, temp; struct index_node *iptr; SetupCompute() ; for (jj=l; jj<=DATA_LENGTH; jj++) { raw = (float) x[jj-l]; Compute() ; ratios[jj-1] = (double) ratio; fg_vals[ j-l]= (double) fg_med; bg_vals[jj-l]= (double) bg_med; } return; } /* GATEWAY FUNCTION */ ifdef STDC void mexFunction( int nlhs, Matrix *plhs[], int nrhs, Matrix *prhs(] ) /else mexFunction(nlhs, plhs, nrhs, prhs) int nlhs, nrhs; Matrix *plhs(], *prhs[]; /endif { double *ratios, *fg_vals, *bg_vals, *x; /* Check for proper number of arguments */ if (nrhs ! = 6 ) { mexErrMsgTxt ( "ONLINE requires six input arguments . " ) ; } else if ( (nlhs ! = l ) && (nlhs ! = 3 ) ) { exErrMsgTxt("ONLINE requires one or three output argument. ") ; } FILTER_LENGTH = max( xGetM(prhs[1] ) ,mxGetN(prhs[1] )) ; DATA_LENGTH = ma (mxGetM(prhs[0) ) ,mxGetN(prhs[0] ) ) ; /* Create matrix for the return argument */ plhs[0] = mxCreateFull(DATA_LENGTH, 1, REAL); ratios = mxGetPr(plhs[0] ) ; if (nlhs == 3) { plhs[l] = mxCreateFull(DATA_LENGTH, 1, REAL); fg_vals = mxGetPr(plhs[l]) ; plhs[2] = mxCreateFull(DATA_LENGTH, 1, REAL); bg_vals = mxGetPr(plhs[2]) ; } x = mxGetPr(prhs[0] ) ; filter_coeffs = mxGetPr(prhs[1]) ; FG_LENGTH = (int) (mxGetPr(prhs[2] ) ) [0) ; DELAY_LENGTH = (int) (mxGetPr(prhs[3] ) ) [0] ; BG_LENGTH = (int) (mxGetPr(prhs[4] ) ) (0] ; THRESHOLD = (int) (mxGetPr(prhs[5] ) ) [0] ; NPTS « FG_LENGTH + DELAY_LENGTH + BG_LENGTH; online(ratios, fg_vals, bg_vals, x) ; return; precursorl.m This MATLAB script shows how one can detect a rhythmic discharge type precursor to seizure from raw data. % precursorl.m % load iir_spike_filt; % b and a are IIR filter coeffs % t=0:dt:tf; % iprint=0; % Set to 1 if you want print plots % % pat='fl'; eval(['load pat_' pat]); % x=2*(dat(: ,10)-dat(: ,ll)+dat(: ,12))-dat(: ,13)-dat(: ,9) ; % % %Threshold related parameters: % c=100; % Threshold for ratio of filter output squared to background % ncross=2; % Number of "rhythmic crossings" required for precursor detection % min_space=.5; max_space=10; % You must have at least 2 spikes % separated by at least 1 sec and less than 10 sec % bg_time=50; % (c) Mark Frei 10-21-94 % Remark (May 95) : use of sharpl.m is more effective than % this program for detecting these precursors. xlflag=0; elf;xf=filter(b,a,x) ;xf=xf. '2 ; bg=median(xf(1:240*bg_time) ) ; tl=t(find(xf/bg>=c) ) ; dtl-diff(tl) ; k=find(min_space<=dtl & dtl<=max_space) ; if length(tl)>=ncross, k=[l; l+k(:)]; if (length(k) >= ncross); t2=tl(k(ncross: length(k) ) ) ; subplot(2, 1,2) ; stem(t2,ones(size(t2) ) ) ;set(gca, 'xlim' , [0 240] , 'ylim' , [0 1.5]) title( [ 'Precursor detection with ' int2str(ncross) ' crossings required, min_space = ' num2str(min_space) ... ', max_space = ' num2str(max_space) ] ) t_predict=t2(l) ; disp([ 'Precursor detection at t = ' num2str(t_predict) ] ) ; else disp('Not enough rhythmic crossings'); xlflag=l; end else disp(('Only ' int2str(length(tl) ) ' crossings of threshold: no detection*]); return end subplot(2,1,1) ; plot(t,xf/bg) set(gca, 'xlim' , [0 240] , 'ylim' , [0 min(5*c, max(xf/bg) ) ] ) eo=etime(elec_onset,tO) ;co=etime(clin_onset,tO) ;aeb=etime(e b,t0); add_eventsv(eo,co,aeb) ; plot([0 240] , [c c] , 'm' ) ; title([ 'Seizure prediction: pat_' pat ' => ' num2str(co-t_predict) ... ' sec prediction ' ] ) if xlflag*=l , xlabel ( ' Not enough rhythmic crossings ' ) ; else ; xlabel ( ' Time (sec) ' ) ; end orient landscape ; date_stamp ; if iprint==l , print -dps -P651 end precursor2.m This MATLAB script shows how one can detect the seizure precursor consisting of signal energy attenuation for a specified duration in a given input signal. %precursor2.m % % This mfile gives an example of how one can detect quieting as % a precursor to seizure... % %load pat_a2; co=294; dt=.005; %n=length(dat) ; t=(0: (n-1) ) *dt; tf=max(t) ; %nwin=floor(tf) -5; %chan=13:32; % Look for quieting on the grid electrodes (for example) %y=dat( : ,chan) . "2 ;y=sum(y' ) ' ; %c=5; % Set threshold % (c) Mark Frei 10-24-94 rl=l:1000; % Look for quieting of duration 5 sec. Eavg=zeros(nwin, 1) ; % Overlapping 5 s windows translated by 1 sec each for j=l:nwin,Eavg(j)=mean(y(200*(j-l)+rl) ) ;end r=l:200*200; % Use 1st 200 sec to compute background bg=mean(y(r) ) ; % Could also use median but this weighs "spike quieting" heavily elf;plot(4+(1:nwin) ,bg. /Eavg) xl=get(gca, 'xlim' ) ; hold on;plot([0 xl(2) ], [c c], 'm') t_detect=4+min(find(bg. /Eavg>=c) ) t predict=co-t detect precursor3.α This MATLAB script shows how one can detect the seizure precursor characterized by a significant drop in median frequency w.r.t. PSD for a given input signal. %precursor3.m % % This mfile gives an example of how one can use an % abrupt drop in median frequency as a precursor to % seizure in some patients. % ( c) Mark Frei 10-26-94 load pat_ai % File containing data matrix dat c=10; %Threshold dt=.005;n=length(dat) ; t=(0: (n-1) ) *dt; tf=max(t); chan=l:32; nchan=length(chan) ; nf=256; psd_stats bg=median(med(l:floor(nwin/2) , : ) ) ; s=zeros(length(med) ,1) ; for j=chan, s«s+(med( : ,j) /bg(j) ) . * (-2) ;end plot(nf*dt*(l:nwin) , (s/nchan) . "2) t detect=nf*dt*min(find( (s/nchan) . ~2>=c) ) Upcrossings This section contains the MATLAB mfiles necessary to compute the time of a signal's upcrossings from below a certain level to above that level. It can be used to detect signal rhythmicity and neuronal hypersynchrony if, e.g., the standard deviation of the most recent say 10 inter-zero-upcrossing times is small relative to background. This is used to enhance seizure detection and artifact rejection, and can also be useful for precursor detection. upcross2.m function t_upcross=upcross2 (t,x,c) %function t_upcross=upcross2(t,x,c) % % This function returns the times at which the linearly % interpolated function x(t) up-crosses the line x(t)=c. % This version does not assume that the points of t are % evenly spaced. If they are, use upcrossl.m % % (c) Mark Frei 10-15-94 i_up=find(diff(x>=c)==l) ; t_upcross=(c-x(i_up) ) .*(t(i_up+l)-t(i_up) ) , / (x(i_up+l)-x(i up))+t(i up) ; psd_stats This section contains the MATLAB mfiles necessary to compute statistics of the power spectral density of a signal such as mean, modal, and median frequency (and well as other \%-tiles and moments) , the frequency variance, and average signal energy. The main routine psd\ stats. calls pdf\_stats2.m to compute general statistics of a probability density function. The "-built-in' ' MATLAB function PSD. is used to compute the power spectral density (Welch's periodogram method is used here). %psd_stats.m % Need dat and t defined to run %chan=l:16; % channels to analyze in dat %pat=' (pat,evnt,chan) ' ; %plot title header %dt=l/240; % Time step %nf=512; % Number of points per window (and 2* num of freqs) %ql=.25;q2=.75; % Used for frequency percentiles % (c) Mark Frei 7-20-94 if " exist( 'chan') , chan=l:min(size(dat) ) ; end if " exist( 'pat' ) ,pat=' (pat,evnt,chan) ' ;end if " exist( 'dt') ,dt=l/240;end if " exist( 'nf' ) ,nf=512;end if " exis ( 'ql') ,ql=.25;end if " exist( 'q2' ) ,q2=.75;end if " exist("tf') ,tf=ceil(max(t)) ;end nchan=length(chan) ; n=length(dat) ; r=l:nf;p=zeros(nf/2+1,nchan) ;nwin=floor(n/nf) ; mu=zeros(nwin,nchan) ;mu2=mu;sigma=mu;med=mu; % Preallocation maxp=mu;f_low=mu;f_high=mu;Eavg=mu; h_wait=waitbar(0, 'Computing Statistics of PSD ... '); for k=l:nwin, rl=r+(k-l)*nf; for i=l:nchan, [P(: ,i) , t]=psd(dat(rl,chan(i) ) ,nf,1/dt) ; % Welch periodogram method end (mu(k, : ) ,mu2 (k, : ) ,sigma(k, : ) ,med(k, : ) ,maxp(k, : ) , f_low(k, : ) , f_high(k, :))=... pdf_stats2 (p,f,ql,q2) ; Eavg(k, : )=mean(dat(rl,chan) . * 2) ; waitbar(k/nwin) ; end delete(h_wait) ; ************************************************* %pdf_stats2.m: function [ u,mu2,sigraa,med,maxp,x_lo ,x_high)=pdf_stats2 (p, ,ql,q2) ; %function ( u,mu2,sig ,med, axp,x_low,x_high]=pdf_stats2 (p,x,ql,q2) ; % Inputs: vector x and matrix p % such that x is increasing of length N, and % p is N x M, with each column of p is a % (possibly non-normalized) probability density % function p_j (x) . % % Optional: ql and q2 (low and high percentiles to use in returning % the values x_low and x_high) % % Output: % % 1 x M vectors mu, mu2, sigma, and med, where % mu(j) is the mean, % mu2(j) is the second moment, % sigma(j) is the variance, and % med(j) is the median (50-th percentile) % corressponding to the j-th column of p. % maxp(j) is the x-value at which p_j(x) is maximized % x_low(j) and x_high(j) are defined by the relation % Prob(X < p_j (x_low) )=ql, Prob(X < p_j (x_high) )=q2 % % (med(j) is the x value which divides the pdf p_j (x) % in half in terms of area under the curve) . % % (c) Mark Frei 6-13-94 x = x(: ) ■ [N,M]=size(p); if min(N,M)==l, P-P( : ) ; else if N " = length(x) , disp('Error, number of rows in p should equal the length of x' ) ; end mu=zeros(1,M) ;mu2=mu;sigma β mu;med=mu; if nargout > 4, maxp=mu;x_low=mu;x_high=mu; %preallocate end end % Convert to pdf(s) s=trapz(x,p) ; for j=find(s " =l) ; P(:. J)-P(: ,j)/s(j) ; end % Compute stats x2=x. "2; for j=l:M, mu(j)=trapz(x,x.*p(: ,j) ) ; mu2(j)=trapz(x,x2.*p(: , )) ; end sigma=mu2-mu. "2 ; if nargout > 4, [a,b]-max(p) ; maxp=x(b) ' ; end dx=diff(x) ;k=l: (N-l) ; for j=l:M, ipdx=[0; .5*cumsum( (p(k,j)+p(k+l,j) ) .*dx) ] ; ind=[0; find(diff (ipdx)>0) ]+l; %makes ipdx increasing med(j)=tablel( [ipdx(ind) x(ind) ] , .5) ; if nargin > 2, x_low(j)=tablel( [ipdx(ind) x(ind) ] ,ql) ; end if nargin > 3, x_high(j)=tablel( [ipdx(ind) x(ind) ] ,q2) ; end end end sharpl.m function s=sharpl(y,p,dt) ; % function s=sharpl(y,p,dt) ; % % This function returns the 2nd-derivative % (i.e. acceleration) of the best approximating % parabola (LS sense) to data y, in a moving % window of p points. % % Warning: The FIR filter used here instills % a delay of approximately dt*(p/2) in time % (one needs approx. p/2 future points to % evaluate the acceleration/curvature at the % current time point) . We shift the output % backward in time to correct for this, so % that the output "sharpness," s, is the same % length as the input, and corresponds temporally % with where the sharpness occured (not when it % was available from computations (which would % be about (p/2)*dt later...) . % (c) Mark G. Frei 6-11-95 y=y( : ) ; npts=length(y) ; if nargin <3, dt=l; end if nargin <2, p=3; dt=l; end a=(p*(p-l)*(2*p-l)*(3*p " 2-3*p-l)*dt"4)/30; b=(dt"3*(p-l) "2*p"2)/4; c=(p*(p-l)*(2*p-l)*dt * 2)/6; d=p*(p-l)*dt/2; e=p; g=0:dt: (p-l)*dt; f=g."2; h=ones (l,p) ; A=c*e-d"2; B=c*d-b*e; C=b*d-c"2; D=a*A+2*b*c*d-e*b"2-c " 3; H=(A B C]/D; filt_coeffs=2*H*[f; g; h) ; r=p:-l:2; s=filter(filt_coeffs,l,y) ; % Now shift the output to match temporally with input s(l:p)=zeros(p,l) ; s{l:floor(p/2))=(]; s= [ s ( : ) ; zeros (npts- length (s) , 1 ) ] ;

Claims: CLAI S 1 A mediod of detecting the occurrence of abnormal activity in the brain ot a subject, said mediod compπsing the steps of (a) receiving into a signal processor input signals indicative of die subject's brain activirv , (b) determining ictal components in said input signals by applymg to said input signals a first filter configured for extracting and enhancing ictal components from said input signals, (c) measuring die ictal activity in a foreground epoch of said input signals by applying an order-statistic filter to said ictal components corresponding to said epoch to produce a foreground measure of ictal activity, (d) determuung whedier said foreground measure reaches a direshold level, such being indicative of the occurrence of said abnormal activity and (e) performing steps (a) - (d) while said abnormal activity is occurring The mediod as set forth m claim 1 , step (b) including the step of using a digital filter as said first filter 3 The method as set forth m claim 2, step (b) including die step of using a finite impulse response filter as said digital filter 4 The mediod as set forth in claim 2, step (b) including die step of usmg an infinite impulse response filter as said digital filter 5 The mediod as set forth in claim 1 , step (b) including die step of using an analog filter as said first filter 6 The mediod as set forth in claim 1 , step (b) including die step of squai ing the results of die application of said first filter to produce said ictal components 7 The mediod as set forth in claim 1 , step (c) including die step of measuring die ictal activity in a background epoch of said input signals by applymg said order-statistic to said ictal components corresponding to said background epoch to produce a background measure of ictal activity, said background epoch occurring before said foreground epoch, step (d) including the step of determining whether die ratio of said foreground measure to said background measure reaches said direshold level 8 The method as set forth in claim 7, step (c) including die step of configuring said foreground and background epochs to present a time delav therebetween 9 The mediod as set forth in claim 8, step (c) including die step configuring said foreground epoch as about two seconds, said background epoch as about twenty seconds and said delay as about one second 10 The method as set forth in claim 7, step (c) including the step of continuously updating said foreground and background measures 1 1 The mediod as set forth in claim 10, said threshold level being a first threshold level, step (c) including die step of suspending updating said background measure if said ratio reaches a second direshold level 12 The method as set forth in claim 1 , step (e) including the step of performing steps (a) - (d) before die onset of die clinical component of die seizure diereby being predictive of the clinical component 13 The method as set forth in claim 12, step (e) including die step of performing steps (a) - (d) before die onset of die electrographic component of the seizure thereby being predictive of the electrographic component 1 The mediod as set forth in claim 1 , step (b) including die step of selecting said first filter from a filter bank including a plurality of filters configured for extracting and enhancing said ictal components 15 The mediod as set forth in claim 14, step (b) including the step of selecting said first filter as the filter from said filter bank providing die greatest differentiation of ictal components 16 The method as set forth in claim 15, said signal processor including memory means for storing said filter bank, step (b) including die step of retrieving a plurality of filters from said filter bank and applymg said plurality of filters to said input signals in said signal processor and therein selecting said first filter as the filter from said plurality providing die greatest differentiation ot ictal components 17 The mediod as set forth in claim 1 , further including die step of configuring said first filter m order to increase the differentiation of ictal components for the subject 18 The mediod as set forth in claim 1 , step (b) including die steps of using one of a finite impulse response filter and an infinite impulse response filter as said digital filter, and squaπng die results ot die application of said first filtei to enhance said ictal components, step (c) including the steps of measuπng the ictal activity in a background epoch of said input signals bv applying said order-statistic to said ictal components corresponding to said background epoch to produce a background measure of ictal activity and continuously updating said foreground and background measures, said background epoch occurring before said foreground epoch and said foreground and background epochs presenting a time delav dierebetween, step (d) including the step of determining whether die ratio ol said foreground measure to said background measure reaches said direshold level, said threshold level being a first threshold level, step (c) including the step ol suspending updating said background measure if said ratio reaches a second direshold level 19 The mediod as set forth in claim 1 , step (a) including die step of receiving said signals from at least one electrode operable for detecting die subject's bram activity and for producing said signals indicative thereof, said at least one electrode be g selected from the group consistmg of a scalp electrode and an implanted electrode 20 The mediod as set forth in claim 1 , step (a) including die step of receiving said signals from a memory device 21 The mediod as set forth in claim 1 , step (b) including die step ol analyzing said signals in said signal processor selected from the group consisting of a mici oprocessor and a computer 22 The mediod as set forth in claim 1 , step (b) including die step of analyzing said signals m a signal processor implanted within die subject 23 The method as set forth in claim 1 , step (b) including the steps of analyzing said signals by usmg a wavelet filter as said first filter to determine corresponding wavelet coefficients of said signals, using said wavelet coefficients to determme a power density distribution, and compaπng said power densitv distribution widi said threshold level wherein die crossing of said direshold level by said distnbution indicates die occurrence of die seizure 24 The method as set forth in claim 1 , step (b) including die step of analyzing said signals usmg windowed Founer and inverse Founer transforms as said first filter 25 The mediod as set forth in claim 1 including the step of producing an output in response lo the indication of die occurrence of a seizure as said abnormal activity with said output taken Irom the group consisting of admimstenng a medicament, electncally stimulating a portion of the subject's bram. magnetically stimulating a portion of the subject's brain, inhibiting acuvitv in a portion of the subject ' s brain, electπcalK stimulating a nerve of the subject, recording said signals, activating an alert, stimulating physiological receptors of die patient, heating at least a portion of die subject's brain, cooling at least a portion of die subject's bram, facilitating activity in a portion of a subject's bram, disfacihtating activity m a portion of a subject's bram, and ablating a portion of die subject's brain 26 The mediod as set forth in claim 25, further including die step ot using a de\ ice implanted with the subiect for producing said output 27 The mediod as set forth in claim 1 , said first filter including a geneticallv designed filter 28 A method of predicting die occurrence of a seizure in die bram of a subject said mediod compnsmg die steps of (a) receiving to a signal processor input signals indicative of die subject's brain acttwty, (b) analyzing said signals for at least one precursor predictive of die occurrence of a seizure in die subject, and (c) upon occurrence of said at least one precursor, producing an output in response before the occurrence of die seizure 29 The method as set forth in claim 28, step (b) including die step of detecting epilepuform discharges in said signals, determining die sharpness of each of said discharges by determining a parabola of optimal fit for each of said spikes, determining an index of relative shaφness of each of said discharges by companng each sharpness to the shaφness of odier discharges in a time window, determining whediei said index reaches said predetermined level, said spike presenting a polanty, and determining whedier said spikes reaching said predetermined level fit a predetermined pattern, such being predictive of a seizure 30 The method as set forth in claim 29, step (b) including die step of determining said precursor by detecting epilepuform spikes by using a signal analysis filter to extract spike shape coefficients, determining a ratio of spike shape coefficients squared to background signal information, determining whether said ratio exceeds said predetermined level and if so, determining whedier spikes exceeding said ratio fit a pattern determined as being predictive of a seizure 31 The method as set forth in claim 28, step (b) including the step of determining said precursor bv determmmg a ratio of current signal energy compared to background energy and determmmg whedier said ratio exceeds said predetermined level, such being predictive of a seizure 32 The method as set forth in claim 28, step (b) including die step of determining said precursor by determining a ratio of median frequency to background median frequency and determining whether said ratio exceeds said predetermined level, such being predictive of a seizure 33 A mediod of analyzing die activity state of the brain of a subject, said mediod compnsmg the steps of (a) receiving into a signal processor signals representative of the subject's brain actn ιt\ , (b) analyzing said signals in said signal processor for presence of information indicating the current activity state of die subject's brain, (c) accomplishing step (b) while said activity state is occumng, and (d) producing an output in response to said presence in said information 34 The method as set forth in claim 1. step (b) including die step of using a filter as said first filter taken from the group consisting of a digital filter, a nonlinear filter, and adaptive filter, a correlation integral, an arc length differential and a temperature filter 35 The method as set forth in claim 1 , said method including the mediod of detecting die occurrence of an epileptic seizure as said seizure 36 The mediod as set forth in claim 1 further including die step of receiving odier biological signals concemmg die subject and usmg said biological signals in detecting die occurrence of a seizure, said biological signals bemg representative of biological functions of the subject selected from die group consisting of respiratory activity, concentrations of glucose, free radicals, neurotransmitters, blood, body tissues, brain temperature, heart activity , muscle activity, ocular activity, magnetic fields, skin resistance, and electπcal fields 37 The mediod as set forth in claim 1 further including die step of providing biofeedback to die subject indicative of a seizure or epileptiform discharge 38 The mediod as set forth in claim 1 , including die step of measunng said ictal activity in said foreground epoch against a background signal generated using time and state-weighted averaging 39 The mediod as set forth in claim 38, including die step of using an exponentially forgetting tune averagmg as said time and state-weighted averaging 40 A seizure detection, prediction and treatment apparatus for detecting die occurrence of a seizure in die bram of a subject, said mediod compnsmg receiving means for receiving input signals indicative of the subject's bra activity, signal processing means coupled with said receiving means for determining ictal components in said input signals by applying to said input signals a first filter configured for extracting and enhancing ictal components from said input signals, measunng die ictal activity in a foreground epoch of said input signals by applymg an order- statistic filter to said ictal components corresponding to said epoch to produce a foreground measure of ictal activity, and determining whedier said foreground measure exceeds a predetermined level, such being indicative of die occurrence of a seizure, and output means for producing an output indicating die occurrence of the seizure while die seizure is occurring

Inventors: DORFMEISTER JOSEF, FREI MARK, LERNER DAVID, OSORIO IVAN, RALSTON JOHN, DORFMEISTER, JOSEF, FREI, MARK, LERNER, DAVID, OSORIO, IVAN, RALSTON, JOHN
Applicants: UNIV KANSAS, UNIVERSITY OF KANSAS
Application year: 1997
Main classification: A61B5
IPC codes: A61B5, A61N1