A Patient Specific Seizure Prediction in Long Term EEG based on Adaptive Channel Selection and Preictal Period Selection

Abstract

A novel algorithm for seizure prediction based on patient specific manner was proposed to improve the accuracy of epilepsy prediction. Time-frequency features and spatial features were extracted from each channel by 4s windows with 2s overlap. A continuous 10-min sample was selected from 1h before seizure onset by preictal period selection, which achieved maximum linear separability compared with inter ictal period. The effective features selected by elastic net and effective channels selected adaptively in greedy manner were input into SVM. The algorithm is tested on MIT scalp EEG database and the database collected in Xuanwu Hospital Capital Medical University. The algorithm can achieve a sensitivity of 94.61% and a false positive rate of 0.1484/h in MIT database, and a sensitivity of 95.14% and a false positive rate of 0.1312/h in Xuanwu Hospital database. The results show that the algorithm in this paper has higher sensitivity and lower false positive rate.