Epileptic seizure detection using wavelet transform based sample entropy and support vector machine

Electroencephalogram is the recording of brain electrical activity and it contains valuable information related to the different physiological states of the brain. In this study, we present a new approach to detect epileptic seizure. The new scheme was based on discrete wavelet transform and sample entropy analysis of EEG signals. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the sample entropy and detection by using support vector machine. The analysis results depicted that during seizure activity EEG had lower sample entropy values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG.