Open AccessDetection of epileptic seizure and seizure‐free EEG signals employing generalised S ‐transform
Author(s) -
Chatterjee Soumya,
Ray Choudhury Niladri,
Bose Rohit
Publication year - 2017
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0443
Subject(s) - ictal , pattern recognition (psychology) , support vector machine , electroencephalography , epileptic seizure , artificial intelligence , computer science , time–frequency analysis , classifier (uml) , speech recognition , psychology , computer vision , neuroscience , filter (signal processing)
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.