Detection of Epileptic Spikes with Empirical Mode Decomposition and Nonlinear Energy Operator

Abstract

Epileptic seizure is a serious brain disease. The characteristic signature of epileptic seizure is interictal spikes and sharp waves. Development of a reliable method to detect spikes from EEG data is of major clinical and theoretical importance. In this paper, a new detection algorithm that combines the Empirical Mode Decomposition (EMD), Hilbert Transformation (HT) and Smoothed Nonlinear Energy Operator (SNEO) is proposed to detect spikes hidden in human EEG data. Finally, the EEG data generated by a nonlinear lumped-parameter cerebral cortex model and real EEG data from human are applied to test the performance of the new detection method. The results show that this method can efficiently detect the spikes hidden in EEG signals.