CEEMDAN-Wavelet Threshold Denoising Method on sEMG

Abstract

In view of the fact that the collected sEMG signal contains a lot of noise, which makes it impossible to accurately identify and analyze the signal, this paper proposes a method that complete ensemble empirical mode decomposition with adaptive noise and wavelet layered threshold denoising to achieve accurate signal identification and analysis. The method is to first calculate the correlation coefficient after CEEMDAN(Cemplete Ensemple Empirical Mode Decomposition with Adaptive Noise) decomposition, and then denoise the first three IMFs after decomposition, and then reconstruct, and then perform wavelet layered threshold denoising after reconstruction. After experimental comparison, it is found that the denoising effect of designing such a denoising algorithm is better than other different global thresholds and separate layered threshold denoising.