Performance Analysis of Classifiers for Seizure Diagnosis for Single Channel EEG Data

Abstract

The problem of diagnosis and treatment of epileptic seizures to aid neurophysiologists suggests the development of automated seizure onset detection systems. The purpose of the quantitative research is to determine the best classifier having highest rates of classification. This research work compares the classification results between seizure and non-seizure and inters ictal activity using Neural Network, Support Vector Machine and Radial Basis function machine learning techniques. It has been illustrated from results that the neural network classifier outperforms for the present research work. The differences between classification accuracy exhibited by the different classifiers are small, but the superiority of neural network as compared to support vector machine classifier and radial basis function was sustained by classification acuuracy, sensitivity, specificity and ROC curve.