You are hereEpilepsy: Automatic Seizure Detection and Focus Localization for Drug-Resistant Patients

Epilepsy: Automatic Seizure Detection and Focus Localization for Drug-Resistant Patients


Epilepsy affects 50 million people worldwide [1]-[4], and 30% remain drug-resistant [5]-[7]. This has increased interest in both chronic and responsive neurostimulation [8]-[14], which is most effective when administered at or near the foci and immediately prior to or at the seizure onset. Precise focus localization and automatic online seizure detection (AOSD) from intracranial EEG (iEEG) recordings are therefore critical for closed-loop intervention, but remain challenging problems. Automated localization schemes have primarily been developed using univariate and bivariate features but lack consistency and accuracy [15]-[30]. Several AOSD algorithms has been proposed thus far ([31]-[38] to name only a few) and though they are highly sensitive (large number of true positives), these algorithms generally have low specificity (large number of false positives), which limits their clinical use. The lack of specificity presumably occurs because (i) they compute statistics from a few channels at a time which may not capture network dynamics of the brain, (ii) the channels used may be too far from the focus, and/or (iii) the detection thresholds are not explicitly optimized to maximize AOSD performance.

This program proposes a novel computational framework for seizure foci localization and AOSD that involves (i) constructing multivariate statistics from all electrodes to localize foci and distinguish between non-ictal vs. ictal states; (ii) modeling the evolution of these statistics in each state and the state-transition probabilities; and, (iii) developing an optimal model-based strategy to detect transitions to ictal states from sequential neural measurements. This strategy is formulated as the Bayesian “Quickest Detection” (QD) of the seizure onset [39][40], is solved via control optimization tools [41], and explicitly minimizes both the distance between detected and unequivocal onset times and the probability of false positives. Our team of clinicians and engineers will combine state of the art tools from matrix theory, system identification, and control theory to construct this framework using iEEG recordings from epilepsy patients.

We hypothesize that (i) foci localization accuracy will improve using multivariate statistics computed from all channels at a time, and that (ii) the QD algorithm combined with multivariate statistics will detect seizure onsets with fewer false positives while maintaining high true positive rates and smaller delays than current algorithms. We will test our hypotheses through the following research aims.

Aim 1: To identify robust multivariate statistics for seizure focus localization. Multi-site iEEG signals will be processed into generalized non-square connectivity matrices that describe the time-varying spectral dependencies between all the channels over multiple frequency bands. The singular value decomposition (SVD), a tool from matrix theory that highlights dependencies within a matrix, will be used to extract multivariate statistics (e.g., leading singular vector) that capture the nodes of the brain network with the strongest connections across multiple frequency bands. These nodes are an estimation of the seizure foci and will be compared to those annotated by the clinicians.

Aim 2: To construct models describing the evolution of multivariate statistics. The network-based statistics from Aim 1 evolve over time because of subclinical changes of the brain activity that affect iEEG data in time and frequency. To estimate these changes, we will model the evolution of each SVD statistic through a Hidden Markov Model (HMM). The HMMs will be estimated from training data for each patient and will characterize (i) neural dynamics in non-ictal and ictal states, and (ii) the probability distribution of the actual transition (T) from any non-ictal to ictal state.

Aim 3: To implement, compare, and validate QD. We will implement, test, and validate a QD-based strategy for AOSD on a rich clinical data set. For each patient and statistic, the QD strategy (i) will use the HMM to recursively estimate the a posteriori probability of being in ictal state conditioned on current and past iEEG measurements; and (ii) will compare this probability to a time-varying, state-dependent, unsupervised threshold to decide whether a seizure is occurred or not. This threshold will be obtained by solving a QD problem which explicitly minimizes the probability of false positives as well as the expected distance between actual seizure onset (T) and detected transition time (T_QD). The QD strategy will be compared to state-of-the-art AOSD schemes and critical aspects of the QD (e.g., computational and memory requirements, safety, etc.) will be validated to prove whether or not our control-theoretical approach performs significantly better than existing approaches.

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