Automatic detection of Epilepsy based on EMD-VMD feature components and ReliefF algorithm

EEG signal records the nerve activity in the brain, which is of great significance for the diagnosis and treatment of epilepsy. Effective automatic diagnosis method for epilepsy interictal period and ictal period can predict epilepsy and prevent the hurt to the body. In this paper, an automatic epilepsy detection method is proposed based on support vector machine classifier which use the sample entropy and standard deviation features selected by the reliefF algorithm from the components of EEG signals using empirical mode decomposition and variational mode decomposition. The epilepsy EEG database of Bonn University is used to evaluate the method. The experimental results show that proposed method can distinguish the epilepsy EEG signal between interictal period and ictal period in terms of sensitivity, specificity, precision, and accuracy. The best classification accuracy is up to 97.00% using support vector machine classifier with fine gaussian kernel function based on selected features.