School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, People's Republic of China.
J Neural Eng. 2019 Aug 21;16(5):056017. doi: 10.1088/1741-2552/ab33e4.
Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising.
The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and analyzed. In this paper, three methods are used for quantitative evaluation of the filtering performance: the signal-to-noise ratio (SNR), root mean square error and R-squared value. The proposed two methods (VMD-WST, VMD-SIT) are compared with the empirical mode decomposition (EMD) method and the wavelet method.
The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effects were better than the EMD method and the wavelet method. The VMD-SIT method has the best performance.
This study provides a new means of eliminating the noise of sEMG signals based on the VMD method, and it can be applied in the fields of limb movement classification, disease diagnosis, human-machine interaction and so on.
由于表面肌电（sEMG）信号的测量过程中不可避免地会引入噪声，因此本文提出了两种基于变分模态分解（VMD）方法的去噪新方法。在此之前，还没有文献涉及 VMD 在 sEMG 去噪中的应用。
第一种提出的方法使用 VMD 方法将信号分解为多个变分模态函数（VMF），每个 VMF 都有其自己的中心频率和窄带，然后对每个 VMF 应用小波软阈值（WST）方法。这种方法称为 VMD-WST。第二种提出的方法使用 VMD 方法将信号分解为多个 VMF，然后对每个 VMF 应用软间隔阈值（SIT）方法，简称 VMD-SIT。本实验招募了 10 名健康受试者和 10 名脑卒中患者，测量和分析肱二头肌的 sEMG 信号。本文使用三种方法对滤波性能进行定量评估：信噪比（SNR）、均方根误差和 R 平方值。将提出的两种方法（VMD-WST、VMD-SIT）与经验模态分解（EMD）方法和小波方法进行了比较。
实验结果表明，VMD-WST 和 VMD-SIT 方法可以有效地滤除噪声效应，且去噪效果优于 EMD 方法和小波方法。VMD-SIT 方法的性能最佳。
本研究为基于 VMD 方法消除 sEMG 信号噪声提供了一种新手段，可应用于肢体运动分类、疾病诊断、人机交互等领域。
