ROC Analysis of EEG Subbands for Epileptic Seizure Detection using Naïve Bayes Classifier

Abstract

This paper presents analysis of Electroencephalograms (EEGs) and subbands (delta, theta, alpha, beta, gamma) using image descriptors for epileptic seizure detection. Short-time Fourier transform (STFT) has been utilized to convert 1-D EEG data into image. All subbands are separated from the time-frequency (t-f) matrix and Haralick features of each subband is fed in the Naïve Bayes (NB) classifier. Receiver operating characteristic (ROC) analysis has been used for performance evaluation of classifier. Among all subbands, gamma band alone shows a maximum AUC of 0.98 to classify between ictal and healthy class, while beta band shows a maximum AUC of 0.96 to differentiate between ictal and interictal class. Significance of this work is it shows the medical advantage of different subbands for the detection process.