BECT Spike Detection Based on Novel Multichannel Data Weighted Fusion Algorithm

Abstract

Benign epilepsy with spinous waves in the central temporal region (BECT) is the most common epilepsy syndromes in children. Spike discharges in the Rolandic area are important biomarkers for diagnosis evaluation. Conventional single-channel electroencephalogram (EEG) based spike detection methods are generally susceptible to artifact interference. To address this issue, a novel spike detection method based on multichannel EEG weighted fusion strategy is developed in this brief. The proposed algorithm mainly includes multichannel spike candidate sample screening, data weighted fusion, time-series feature extraction and long-short-time memory neural networks (LSTM) detection. Studies on 15 BECT children show that the proposed algorithm can obtain an average of 95.74% F1 scores, 93.94% sensitivity, 97.73% precision for all subjects.