Surface electromyography signal denoising via EEMD and improved wavelet thresholds

Ziyang Sun; Xugang Xi; Changmin Yuan; Yong Yang; Xian Hua; Ziyang Sun; Xugang Xi; Changmin Yuan; Yong Yang; Xian Hua

doi:10.3934/mbe.2020359

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 6945-6962. doi: 10.3934/mbe.2020359

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Research article

Surface electromyography signal denoising via EEMD and improved wavelet thresholds

1.
School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
3.
Jinhua People's Hospital, Jinhua 321000, China

Received: 02 August 2020 Accepted: 08 October 2020 Published: 16 October 2020

The acquisition of good surface electromyography (sEMG) is an important prerequisite for correct and timely control of prosthetic limb movements. sEMG is nonlinear, nonstationary, and vulnerable against noise and a new sEMG denoising method using ensemble empirical mode decomposition (EEMD) and wavelet threshold is hence proposed to remove the random noise from the sEMG signal. With this method, the noised sEMG signal is first decomposed into several intrinsic mode functions (IMFs) by EEMD. The first IMF is mostly noise, coupled with a small useful component which is extracted using a wavelet transform based method by defining a peak-to-sum ratio and a noise-independent extracting threshold function. Other IMFs are processed using an improved wavelet threshold denoising method, where a noise variance estimation algorithm and an improved wavelet threshold function are combined. Key to the threshold denoising method, a threshold function is used to retain the required wavelet coefficients. Our denoising algorithm is tested for different sEMG signals produced by different muscles and motions. Experimental results show that the proposed new method performs better than other methods including the conventional wavelet threshold method and the EMD method, which guaranteed its usability in prosthetic limb control.
- electromyography,
- denoising,
- wavelet transform,
- ensemble empirical mode decomposition,
- empirical mode decomposition,
- prosthetic control
Citation: Ziyang Sun, Xugang Xi, Changmin Yuan, Yong Yang, Xian Hua. Surface electromyography signal denoising via EEMD and improved wavelet thresholds[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6945-6962. doi: 10.3934/mbe.2020359

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Abstract

The acquisition of good surface electromyography (sEMG) is an important prerequisite for correct and timely control of prosthetic limb movements. sEMG is nonlinear, nonstationary, and vulnerable against noise and a new sEMG denoising method using ensemble empirical mode decomposition (EEMD) and wavelet threshold is hence proposed to remove the random noise from the sEMG signal. With this method, the noised sEMG signal is first decomposed into several intrinsic mode functions (IMFs) by EEMD. The first IMF is mostly noise, coupled with a small useful component which is extracted using a wavelet transform based method by defining a peak-to-sum ratio and a noise-independent extracting threshold function. Other IMFs are processed using an improved wavelet threshold denoising method, where a noise variance estimation algorithm and an improved wavelet threshold function are combined. Key to the threshold denoising method, a threshold function is used to retain the required wavelet coefficients. Our denoising algorithm is tested for different sEMG signals produced by different muscles and motions. Experimental results show that the proposed new method performs better than other methods including the conventional wavelet threshold method and the EMD method, which guaranteed its usability in prosthetic limb control.

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