利用整体经验模态分解和随机森林的脑电信号分类研究

doi:10.3969/j.issn.0258-8021.2018.06.004

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Abstract It is of great importance to automaticaly monitor and classify epileptic EEG in clinical medicine. In view of the non-stationary characteristics of EEG signals, a new method for feature extraction and recognition of EEG based on ensemble empirical mode decomposition (EEMD) and random forest (RF) was proposed in this paper. In this study 200 single-channel signals of epileptic ictal and interictal EEG were selected from EEG data of Bonn University, and 819400 data were used as samples. Firstly, the EEG signals were decomposed into several intrinsic mode functions (IMF) by EEMD, and then the effective features were extracted from each IMF component. Finally, the features of each IMF component were classified by RF and least squares support vector machine (LSSVM). We compared the classification results of RF and LSSVM. The results showed that the classification effect of RF algorithm on epileptic EEG signals in ictal and interictal periods was effective. The recognition accuracy was 99.60%, which was higher than the accuracy of LSSVM. The proposed method could effectively improve the efficiency of clinical EEG signal analysis.

Key words： EEG ensemble empirical mode decomposition(EEMD) random forest(RF) feature extraction and recognition

Received: 17 June 2017

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R318

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	Articles by authors
	Qin Xiwen
	Lv Siqi
	Li Qiaoling

Cite this article:

Qin Xiwen,Lv Siqi,Li Qiaoling. Recognition of EEG Based on Ensemble Empirical Mode Decomposition and Random Forest[J]. Chinese Journal of Biomedical Engineering, 2018, 37(6): 665-672.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.06.004 OR http://cjbme.csbme.org/EN/Y2018/V37/I6/665

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