Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization
Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization
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DOI：
10.1016/j.bspc.2007.09.002
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发表时间：
2008-04-01
影响因子：
5.1
通讯作者：
Farina, Dario
中科院分区：
文献类型：
--
作者：
Lucas, Marie-Francoise;Gaufriau, Adrien;Farina, Dario
该研究提出了一种多通道表面肌电信号的监督分类方法，其目的是控制肌电假肢。表示空间是基于离散小波变换（DWT）的每个记录的肌电信号使用无约束的参数化的母小波。在多通道表示空间中使用支持向量机（SVM）方法进行分类。根据学习信号集的估计，以最小分类误差为准则对母小波进行优化。该方法被施加到六个手的动作与记录的表面肌电从八个位置在前臂的分类。使用8个通道的6名受试者的错误分类率为（平均S.D.）4.7+/- 3.7%，而没有小波优化（Daubechies小波）时为11.1 +/- 10.0%。小波变换和支持向量机都可以用快速算法实现，因此，该方法适合于实时实现。(C)2007爱思唯尔有限公司版权所有。
The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approach in a multi-channel representation space. The mother wavelet is optimized with the criterion of minimum classification error, as estimated from the learning signal set. The method was applied to the classification of six hand movements with recording of the surface EMG from eight locations over the forearm. Misclassification rate in six subjects using the eight channels was (mean S.D.) 4.7 +/- 3.7% with the proposed approach while it was 11.1 +/- 10.0% without wavelet optimization (Daubechies wavelet). The DWT and SVM can be implemented with fast algorithms, thus, the method is suitable for real-time implementation. (C) 2007 Elsevier Ltd. All rights reserved.