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Epileptic focus localization based on bivariate empirical mode decomposition and entropy

Epilepsy is a neurological disorder which causes abnormal discharges in the brain. Epileptic focus localization is a important factor for successful epilepsy surgery. The intracranial electroencephalogram (iEEG) is the most used signal for detecting epileptic focus. The iEEG signals are obtained from a publicly available database that consists of 7,500 signal pairs. To this dataset, empirical mode decomposition (EMD) has been successfully applied to detect the epileptic focus. However, the EMD method is not suitable for iEEG signal pairs. In this paper, a method for the classification of focal and non-focal iEEG signals using bivariate EMD (BEMD) is presented. The bivariate iEEG signals are decomposed the into signal components of the same frequency band. Various entropy measures calculated from the IMFs of the iEEG signals. Then, some or all of the entropies are chosen as features, which are discriminated into focal or non-focal iEEG by using the support vector machine (SVM). Experimental results show that the proposed method is able to differentiate the focal from non-focal iEEG signals with an average classification accuracy of 86.89%.