Recognition of Epileptic EEG Using Support Vector Machines

摘要：

A new approach based on support vector machine (SVM) is presented for the recognition of epileptic EEG. Firstly, the original signals of normal and epileptic EEG are decomposed with multi-resolution wavelet analysis. Secondly, their approximate entropy (ApEn) is estimated to extract features from raw EEG data. Finally, a SVM classifier with a Gaussian kernel function of SVM is used for the classification. Simulation results demonstrated that the SVM combined with wavelet transform and ApEn achieves high recognition accuracies.

关键词： Support vector machine Epileptic EEG Multi-resolution wavelet analysis Approximate entropy

作者: Chunmei Wang Jun-Zhong Zou Jian Zhang Lan-Lan Chen Min Wang

作者单位: Department of Automation, East China University of Science and Technology,200237 Shanghai, China Department of Electronic Engineering, Shanghai Normal University, 200234 Shanghai, China

会议类型: 国际会议

会议名称: The Second International Conference on Cognitive Neurodynamics--2009(第二届国际认知神经动力学会议)

会议地点: 杭州

会议语种:英文

页码: 653-658

在线出版日期: 2009-11-15（万方平台首次上网日期，不代表论文的发表时间）