Seizure detection in clinical EEG based on entropies and EMD

Authors:
Qingfang Meng;Shanshan Chen;Weidong Zhou;Xinghai Yang
Affiliations:
School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, Shandong University, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

Considering the EEG signals are nonlinear and nonstationary, the nonlinear dynamical methods have been widely applied to analyze the EEG signals. Directly extracted the approximate entropy and sample entropy as features are efficient methods to analysis the EEG signals of epileptic parents. To detect the epilepsy seizure signals from epileptic EEG, choose an appropriate threshold value as the discrimination criteria is simplest. The experiment indicated the approximate entropy provide a higher accuracy in distinguishing the epileptic seizure signals from the EEG than sample entropy. To improve the accuracy of sample entropy, empirical mode decomposition (EMD) is used to decompose EEG into multiple frequency subbands, and then calculate sample entropy for each component. The results show that the accuracy is up to 91%, which could be used to discriminate epileptic seizure signals from epileptic EEG.