Comparison of classification methods on EEG signals based on wavelet packet decomposition

被引:39
作者
Zhang, Yong [1 ]
Zhang, Yuting [1 ]
Wang, Jianying [2 ]
Zheng, Xiaowei [1 ]
机构
[1] Liaoning Normal Univ, Sch Comp & Informat Technol, Dalian 116081, Liaoning Provin, Peoples R China
[2] Liaoning Normal Univ, Sch Psychol, Dalian 116029, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
EEG; Wavelet packet; Classification; Approximate entropy; Extreme learning machine; EPILEPTIC SEIZURE DETECTION; APPROXIMATE ENTROPY; FEATURE-EXTRACTION; TRANSFORM;
D O I
10.1007/s00521-014-1786-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
EEG signals play an important role in both the diagnosis of neurological diseases and understanding the psychophysiological processes. Classification of EEG signals includes feature extraction and feature classification. This paper uses approximate entropy and sample entropy based on wavelet package decomposition as the feature exaction methods and employs support vector machine and extreme learning machine as the classifiers. Experiments are performed in epileptic EEG data and five mental tasks, respectively. Experimental results show that the combination strategy of sample entropy and extreme learning machine has shown great performance, which obtains good classification accuracy and low training time.
引用
收藏
页码:1217 / 1225
页数:9
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