Analysis of non-seizure and seizure activity using intracranial EEG signals and empirical mode decomposition based approximate entropy

Abstract

Epilepsy is a series brain dynamical disorder, characterized by recurrent seizures. It is estimated that incidence rates (number of new cases) range from 24 to 53 per 100000 for every year. Approximately, 33% of epilepsy patients suffer from seizures that are not controlled by anti-convulsant medications. The patients with uncontrolled seizures experience several limitations in family, social and educational activities. Therefore, epilepsy research based on the diagnosis and treatment of seizure is considered to be greater importance. In this study, a method based on empirical mode decomposition and approximate entropy (ApEn) is proposed to analyze the intracranial electroencephalography (iEEG) recorded in non-seizure and seizure activity. For this purpose, a standard database based of University of Bonn, Germany is utilized. The iEEG signals of non-seizure and seizure class are subjected to empirical mode decomposition and Intrinsic Mode Functions (IMF) are obtained. Then the approximate entropy is computed from each IMF. The results show that IMF based ApEn is higher in non-seizure class. The ApEn extracted from the fourth IMF is found to perform better in terms of separating these signals. Highest percentage difference of 165% is obtained for this IMF. Further, the ApEn values extracted from all IMFs except IMF7 and IMF8 is found to have highly statistically significant (p<0.0001).