[EEG signal classification based on EMD and SVM].

Shufang Li; Weidong Zhou; Dongmei Cai; Kai Liu; Jianlin Zhao

[EEG signal classification based on EMD and SVM].

Shufang Li, Weidong Zhou, Dongmei Cai, Kai Liu, Jianlin Zhao

School of Physical Science and Technology

Shandong University

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

The automatic detection and classification of EEG epileptic wave have great clinical significance. This paper proposes an empirical mode decomposition (EMD) and support vector machine (SVM) based classification method for non-stationary EEG. Firstly, EMD was used to decompose EEG into multiple empirical mode components. Secondly, effective features were extracted from the scales. Finally, the EEG was classified with SVM. The experiment indicated that this method could achieve good classification result with accuracy of 99 % for interictal and ictal EEGs.

Original language	English
Pages (from-to)	891-894
Number of pages	4
Journal	Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering
Volume	28
Issue number	5
State	Published - Oct 2011

Cite this

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title = "[EEG signal classification based on EMD and SVM].",

abstract = "The automatic detection and classification of EEG epileptic wave have great clinical significance. This paper proposes an empirical mode decomposition (EMD) and support vector machine (SVM) based classification method for non-stationary EEG. Firstly, EMD was used to decompose EEG into multiple empirical mode components. Secondly, effective features were extracted from the scales. Finally, the EEG was classified with SVM. The experiment indicated that this method could achieve good classification result with accuracy of 99 % for interictal and ictal EEGs.",

author = "Shufang Li and Weidong Zhou and Dongmei Cai and Kai Liu and Jianlin Zhao",

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AU - Zhou, Weidong

AU - Cai, Dongmei

AU - Liu, Kai

AU - Zhao, Jianlin

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AB - The automatic detection and classification of EEG epileptic wave have great clinical significance. This paper proposes an empirical mode decomposition (EMD) and support vector machine (SVM) based classification method for non-stationary EEG. Firstly, EMD was used to decompose EEG into multiple empirical mode components. Secondly, effective features were extracted from the scales. Finally, the EEG was classified with SVM. The experiment indicated that this method could achieve good classification result with accuracy of 99 % for interictal and ictal EEGs.

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JF - Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering

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ER -

[EEG signal classification based on EMD and SVM].

Abstract

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