Detection of epileptic seizure and seizure‐free EEG signals employing generalised S ‐transform

Abstract

In this contribution, a novel technique for classification of electroencephalogram (EEG) signals has been presented employing generalised Stockwell ( S )-transform technique. S -transform is a technique for analysis of any non-stationary time series in joint time–frequency frame. In this work, epileptic seizure and seizure-free EEG signals have been taken from an available existing database and generalised S -transform is applied individually on different sets of EEG signals. Selective features like standard deviation and energy are evaluated from the joint time–frequency S -transform contour of the transformed signals and are eventually being classified using support vector machines (SVMs) and k-nearest neighbour (kNN) classifier. In this work, two different classification problems are addressed, namely (i) seizure and healthy (ii) seizure and inter-ictal, where both EEG signals of healthy and inter-ictal zone are considered to be in seizure-free class. For different cases investigated in this study, the highest overall classification accuracy of 98.44% is achieved using SVM classifier where as 100% accuracy is obtained using kNN classifier, which are comparable and even better than the results obtained in the existing literatures, analysed on the same dataset.