Evaluation of time domain features on detection of epileptic seizure from EEG signals

Abstract

In the study of detection of an epileptic seizure using Electroencephalogram (EEG), pattern recognition has been recognised as a valued tool. In this pattern recognition study, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived directly from filtered EEG data and from discrete wavelet transform (DWT) of filtered EEG data for the detection of an epileptic seizure. Further, the authors attempted to study the performance of other time domain features such as mean absolute value (MAV), standard deviation (SD), average power (AVP) which had been attempted by other researchers. The performance of the TD features is studied using naive Bayes (NB) and support vector machines (SVM) classifiers for University of Bonn database with fourteen different combinations of set E with set A to D and clinically inferred with Christian Medical College, Vellore database. The proposed scheme was also compared with other existing scheme in the literature. The implementation results showed that the proposed scheme could attain the highest accuracy of 100% for normal eyes open and epileptic data set with direct as well as DWT based TD features. For other data sets, the highest accuracy are obtained with DWT based TD features using SVM.