Denoising Surface Electromyography Signals Based on Complementary Ensemble Empirical-Mode Decomposition and Multi-Scale Entropy

Surface electromyography (sEMG) can capture muscle activity and motor function, which is considered an effective tool for assessing local muscle fatigue. Due to the complex environment and individual differences, the collected sEMG signals contain lots of noising information. A novel method is devised to effectively solve the problem of sEMG denoising based on complementary ensemble empirical mode decomposition (CEEMD) and multi-scale entropy (MSEn). The method uses the concept of MSEn and analyzes the feature trend of MSEn of intrinsic mode functions (IMFs) obtained by CEEMD. The method adaptively determines the main components of IMFs according to the complexity of sEMG on the time scale, avoiding insufficient selection based on experience. Actual experiments verified the effectiveness of the method.