Epileptic seizure detection in EEG using mutual information-based best individual feature selection

Epilepsy is a group of neurological disorders that affect normal brain activities and human behavior. Electroencephalogram based automatic epileptic seizure detection has significant applications in epilepsy treatment and medical diagnosis. In this study, a novel epileptic seizure detection method is proposed with a combination of empirical mode decomposition, mutual information-based best individual feature (MIBIF) selection algorithm and multi-layer perceptron neural network. Initially, fixed length EEG epochs are decomposed into amplitude and frequency-modulated components called intrinsic mode functions (IMFs). Three features named ellipse area of second-order difference plot, variance and fluctuation index are calculated from first few IMFs. The most significant features are then selected from the calculated features using the MIBIF algorithm to produce a final feature set. Later, the generated feature set is fed into the multi-layer perceptron neural network (MLPNN) classifier. Two well-known benchmark epileptic EEG datasets are used in this study for experimental evaluations. The result of proposed approach shows a significant performance improvement compared to the recent state-of-the-art methods.