Beta Band as a Biomarker for Classification between Interictal and Ictal States of Epileptical Patients

This work presents beta subband (12-30 Hz) as a biomarker to distinguish between interictal and ictal states using Haralick features. Previous works has showed whole frequency spectrum for this analysis. Significance of this work is it has used only beta subband of electroencephalogram (EEG) for classification using image descriptors. One dimensional EEG data has been converted into image using Short-time Fourier Transform (STFT). Beta subband is cut from the time frequency (t-f) plane and Haralick features is fed in the decision tree classifier. The results have been evaluated using K-fold cross validation and classification accuracy of 92.5% has been calculated. Receiver operating characteristic (ROC) analysis has also been performed which shows maximum area under curve (AUC) of 0.94 to distinguish between interictal and ictal.