BECT Spike Detection Algorithm Based on Optimal Template Matching and Morphological Feature Selection

Over 15% of children with epilepsy belong to benign childhood epilepsy with centro-temporal spikes (BECT), facing with educational difficulties. The accurate recognition of spikes in electroencephalogram (EEG) signals collected from BECT patients can help the doctor to effectively make diagnosis and give therapeutic schedule. Traditionally, template matching method can extract spike-like waves from EEG signals and is adopted by many researches. However, the patterns of the spikes appeared in different patients or different time in one patient varies greatly. The brief proposes a spike detection algorithm based on optimal template matching and morphological feature selection, which includes universal template matching, spike clustering, universal template optimization based on particle swarm optimization (PSO) algorithm and false positive spike (FPS) elimination based on spike morphological feature. Based on the testing EEG data set adopted in this brief, the sensitivity (Sen), specificity (Spe) and accuracy (AC) of the proposed algorithm reaches 98.2%, 95.1% and 96.5%, respectively.