An improved wavelet threshold denoising approach for surface electromyography signal

Abstract

Background The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion. Purpose This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality. Results After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength. Conclusion The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.

Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition

“…During denoising, it will lose a large amount of effective signal, resulting in poor processing results for nonstationary signals [7] . Wavelet threshold denoising transforms micro-motion modulated signals into the timescale domain, but it requires the selection of appropriate wavelet functions and thresholds to achieve a better denoising effect [8] . However, these methods are all based on fixed transform basis functions and cannot adaptively process structurally complex modulated echo data.…”