The analysis of surface EMG signals with the wavelet-based correlation dimension method.
The analysis of surface EMG signals with the wavelet-based correlation dimension method.
复制标题
基于小波的相关维数法分析表面肌电信号
DOI：
10.1155/2014/284308
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发表时间：
2014
影响因子：
--
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Wang G;Zhang Y;Wang J
为了有效地改进由表面肌电信号分类控制的假肢系统，人们进行了许多尝试。近年来，有效特征提取方法的发展仍然是一个主要的挑战。以往的研究表明，表面肌电信号具有非线性特征。本研究将非线性时间序列分析与时频域方法相结合，提出了基于小波的相关维数提取表面肌电信号有效特征的方法。首先对表面肌电信号进行小波变换分析，计算相关维数，得到表面肌电信号的特征。然后，将这些特征作为Gustafson-Kessel聚类分类器的输入向量来区分四种类型的前臂运动。我们的研究结果表明，当使用两个通道的表面肌电信号时，在第三分辨率水平上有四个独立的簇对应不同的前臂运动，所得分类准确率为100%。这表明所提出的方法可以对表面肌电信号的非线性特征和时频域特征提供重要的见解，并且适用于对不同类型的前臂运动进行分类。通过与已有方法的比较，该方法具有更强的鲁棒性和更高的分类精度。
Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.