VMD-based denoising methods for surface electromyography signals

Abstract

Objective. 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. Approach. 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. Main results. 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. Significance. 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.