Automatic Detection of Seizure ONSET in Pediatric EEG

This paper proposes a method for automatic detection of seizure onset. Two statistical features: skewness and kurtosis with a wavelet based feature: normalized coefficient of variation (NCOV) were extracted from the data. The classification between normal and seizure EEGs was performed using simple linear classifier. The performance of the algorithm was tested on the 10 patient's data of CHB-MIT scalp EEG database. The data consisted of 55 seizures of 10646 seconds duration. The results show a mean latency of 3.2 seconds, a mean false detection rate of 1.1 false detections per hour and 100% sensitivity.