Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

Here, a technique for automated detection of epilepsy is proposed, based on a novel set of features derived from the multifractal spectrum of electroencephalogram (EEG) signals. In fractal geometry, multifractal detrended fluctuation analysis (MDFA) is a technique to examine the self-similarity of a non-linear, chaotic and noisy time series. EEG signals which are representatives of complex human brain dynamics can be effectively characterised by MDFA. Here, EEG signals representing healthy, interictal and seizure activities are acquired from an available dataset. The acquired signals are at first analysed using MDFA. Based on the multifractal analysis, 14 novel features are proposed in this study, to distinguish between different types of EEG signals. The statistical significance of the selected features is evaluated using Kruskal-Wallis test and is finally served as input feature vector to a support vector machines classifier for the classification of EEG signals. Four classification problems are presented in this work and it is observed that 100% classification accuracy is obtained for three problems which validate the efficacy of the proposed model for computer-aided diagnosis of epilepsy.