EEG analysis based on wavelet-spectral entropy for epileptic seizures detection

Abstract

The electroencephalogram (EEG) is the brain signal containing valuable information about the normal or epileptic state of the brain. In this paper a discrete wavelet-spectral entropy (SEN) method is presented for epileptic seizures detection through the analysis of EEGs and EEG sub-bands. The EEG signal is decomposed by discrete wavelet transform into its sub-bands and is characterized by spectral entropy approach. This method is applied to three different groups of EEG signals: 1) healthy states, 2) epileptic states during a seizure-free interval (interictal EEG), 3) epileptic states during a seizure (ictal EEG). Spectral entropy differentiates between these three states and their sub-bands. At the end, t-student statistical distribution is applied to determine the measure of distinguishing between different subjects. This method can discriminate between ictal and healthy subject of alpha sub-band (8–15 Hz) with 98.5% p-value.