Detection of epileptic spikes with empirical mode decomposition and nonlinear energy operator

Authors:
Suyuan Cui;Xiaoli Li;Gaoxiang Ouyang;Xinping Guan
Affiliations:
Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 1
Cited 1

De-noising by soft-thresholding

IEEE Transactions on Information Theory

Comparison of different EEG features in estimation of hypnosis susceptibility level

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

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.