Epileptic seizure identification using entropy of FBSE based EEG rhythms

Abstract

This paper has proposed a new method for classification of epileptic seizures based on weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier–Bessel series expansion (FBSE) of electroencephalogram (EEG) signals. In this method, each EEG signal is considered for the FBSE to determine the coefficients then the selected order values of these coefficients are used to obtain the rhythms (δ, θ, α, β, and γ). The feature used in this work is WMRPE which has been extracted from rhythms of EEG signal. These computed feature values are then used in distinct classifiers such as random forest (RF), least squares support vector machine (LS-SVM), and regression for classification of epileptic seizure EEG signals. The classification is validated using 10-fold cross-validation technique in order to verify the robustness of the proposed work and the optimization of classification performance has been implemented with feature ranking methods. The proposed method is also tested with additive white Gaussian noise (AWGN) at different signal to noise ratio (SNR) levels. In this present work, the proposed method provides better classification accuracy results for seven different classification problems in which six are two-class classification problems and the remaining one is three-class classification problem. In two-class classification problems, the EEG signals of epileptic patients at the time of seizure interval has been classified with six distinct classes of EEG signals i.e., healthy subjects with eyes open condition, healthy subjects with eyes closed condition, epileptic patients at the time of seizure-free interval captured from hippocampal formation zone, epileptic patients at the time of seizure-free interval captured from epileptogenic zone, epileptic patients at the time of seizure-free interval, and subjects at the time of non-seizure (healthy and seizure-free) interval. In three-class classification problem, the EEG signals corresponding to epileptic patients during seizure interval, healthy subjects with eyes open condition, and epileptic patients at the time of seizure-free interval captured from hippocampal formation zone have been classified with the help of proposed method. The obtained results show that the WMRPE with FBSE based rhythms provides better classification accuracies in comparison to the existing rhythms based method and other existing methods. The proposed method is also perform well in noisy environment at different SNR levels. © 2019 Elsevier Ltd