| 基于非线性特征提取的EEG信号支持向量分类器 |
| |
| 引用本文： | 柳平,赵岩,王军.基于非线性特征提取的EEG信号支持向量分类器[J].汕头大学学报(自然科学版),2009,24(1):69-74. |
| |
| 作者姓名： | 柳平 赵岩 王军 |
| |
| 作者单位： | 汕头大学工学院电子系,广东,汕头,515063 |
| |
| 摘 要： | 为提高癫痫脑电(EEG)信号的正确识别率,设计了一种基于非线性特征提取的EEG信号支持向量分类器.分类器首先将EEG信号通过四层小波包变换分解到不同频段,然后计算各频段小波系数的近似熵(ApEn)值,作为特征向量,最后使用支持向量机(SVM)进行分类.实验结果显示该分类器能有效提高正确识别率.
|
| 关 键 词： | 癫痫脑电 小波包 近似熵 支持向量机 |
Nonlinear Feature Extraction Based SVM Classifier for EEG Signals |
| |
| LIU Ping,ZHAO Yan,WANG Jun.Nonlinear Feature Extraction Based SVM Classifier for EEG Signals[J].Journal of Shantou University(Natural Science Edition),2009,24(1):69-74. |
| |
| Authors: | LIU Ping ZHAO Yan WANG Jun |
| |
| Institution: | (Department of Electronics, Shantou University, Shantou 515063, Guangdong, China) |
| |
| Abstract: | A nonlinear feature extraction based SVMclassifier for EEG signals was proposed to improve the correct classification rates of epileptic EEG. Firstly, EEG signals were decomposed into various frequency bands through fourth-level wavelet packet decomposition. Secondly, approximate entropy（ApEn） values of the wavelet coefficients were used as feature vectors. Lastly, the SVM was used in classification.Experimental results showed that the correct classification rates of the classifier proposed was improved. |
| |
| Keywords: | EEG wavelet packet approximate entropy SVM |
| 本文献已被 维普 万方数据 等数据库收录！ |
|
