Classification of ictal and seizure-free EEG signals using fractional linear prediction

Abstract

In this paper, we present a new method for electroencephalogram (EEG) signal classification based on fractional-order calculus. The method, termed fractional linear prediction (FLP), is used to model ictal and seizure-free EEG signals. It is found that the modeling error energy is substantially higher for ictal EEG signals compared to seizure-free EEG signals. Moreover, it is known that ictal EEG signals have higher energy than seizure-free EEG signals. These two parameters are then given as inputs to train a support vector machine (SVM). The trained SVM is then used to classify a set of EEG signals into ictal and seizure-free categories. It is found that the proposed method gives a classification accuracy of 95.33% when the SVM is trained with the radial basis function (RBF) kernel.