S-Transform-Based Electroencephalography Seizure Detection and Prediction

Abstract

Epilepsy is a fatal disease worldwide, which is considered as the second most common brain disease. It can be represented by an electroencephalogram (EEG) signal for epileptic seizure analysis. In this work, features are extracted from S-transform of EEG signals with fixed periods of time. These features are extracted from three states, namely normal, pre-ictal and ictal (seizure). The powers of the different EEG extracted features are calculated. Afterwards, the support vector machines (SVMs) are applied to distinguish between the different periods of the different states. The simulation results reveal the impact of using S-transform for seizure detection with average sensitivity and specificity of 94.481%, and 70.315%, respectively. Moreover, the seizure prediction is performed accurately with average sensitivity and specificity of 96%, and 72.944%, respectively.