Variational mode decomposition based on refined composite multiscale dispersion entropy and its application in sEMG...

Surface electromyography (sEMG) signals are characterized by low amplitude, nonlinearity, non-stationarity, and susceptibility to various types of noise during acquisition. Although signal decomposition-based preprocessing methods are widely used, the performance of existing algorithms heavily depends on mode selection strategies, leading to suboptimal denoising outcomes under low signal-to-noise ratio(SNR) conditions. These limitations significantly restrict the broader application of sEMG. In this paper, a hybrid denoising framework integrating variational mode decomposition (VMD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. Based on the variational mode functions(VMFs) derived from VMD, an RCMDE-weighted VMF screening mechanism is established to effectively identify noise-dominant components and suppress spectral leakage. Subsequently, local mean decomposition(LMD) and wavelet thresholding(WT) are combined to achieve superior denoising performance. Simulations on synthetic sEMG and real sEMG signals, along with gesture classification experiments, demonstrate that the proposed algorithm outperforms conventional VMD and other mode selection methods, such as those based on correlation coefficient or multiscale dispersion entropy, in terms of SNR and root mean square error(RMSE), particularly under low-SNR scenarios. This method contributes to improving gesture classification accuracy and provides an efficient preprocessing solution for sEMG signals, which lays a foundation for its applications in medical diagnosis and human-computer interaction.