Intra-Seizure Pattern Recognition for Personalized Treatment

Saeed Hashemi, Genchang Peng, Mehrdad Nourani, Omar Nofal, Jay Harvey

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Keywords: Epileptogenic zone localization, Intra-seizure pattern, Seizure stage discovery, spatial-temporal representation, Stereo-electroencephalograpy

TL;DR: We propose a patient-specific framework for SEEG-based seizure analysis that captures the spatial-temporal dynamics of individual seizure episodes for personalized treatment.

Abstract: Stereo-electroencephalography (SEEG) is a technique to monitor and evaluate the spatial and temporal properties of ictal EEG changes in patients with epilepsy during pre-surgery evaluation. When patients have different types of seizures, the intra-seizure patterns across different ictal episode provide extra and meaningful information for personalized treatment planning. This work introduces a patient-specific framework to capture the intra-seizure patterns in a seizure-specific way. After defining a Pre-seizure Plus Seizure (PPS) window as period of interest, SHapley Additive exPlanations (SHAP) is applied to quantify the contributing score of each SEEG channel (i.e. spatial correlation) based on XGBoost classifier. These SHAP scores are segmented and compared via Soft-Dynamic Time Warping (Soft-DTW) to characterize their dissimilarities (i.e. temporal pattern). Then, k-medoids clustering is exploited to divide seizure episodes into groups (episodes) based on Soft-DTW variations, and three clinically meaningful stages, namely trigger, transient, and steady stages, are consistently identified. Validated on SEEG data from eight patients, our results demonstrate high classification performance, reliable epileptogenic zone localization, and robust intra-seizure stage segmentation which are consistent with clinicians' annotations.

Track: 4. Clinical Informatics

Registration Id: QSNCCX4QXMS

Submission Number: 209