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Denoising Method for Surface Electromyography Signals Combining CEEMDAN and Interval Total Variation

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Abstract

The use of surface electromyography (sEMG) signals gains importance in rehabilitation and sports science because it provides a noninvasive and convenient way to analyze the activities of muscles. Since sEMG signals are weak, nonstationary electrical signals mixed with baseline noise, motion artifacts, power line interference, and many other types of noise, these signals need to be denoised for the extraction of useful information. This paper presents a method of denoising sEMG signals based on the combination of complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and interval total variation (ITV). First, we devised the CEEMDAN method to decompose an sEMG signal into several intrinsic mode functions (IMFs). Next, we categorized the IMFs into signal-dominant and noise-dominant IMFs according to energy entropy and frequency-domain transform. Then, we separately denoised these signal-dominant IMFs using the ITV method. Finally, we reconstructed the denoised signal-dominant IMFs to obtain a denoised sEMG signal. Extensive comparisons conducted on both synthetic noisy and real sEMG signals demonstrate the effectiveness of the CEEMDAN-ITV method.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61873348 and 62106240; the Natural Science Foundation of Hubei Province, China, under Grant 2020CFA031; Wuhan Applied Foundational Frontier Project under Grant 2020010601012175; the 111 Project under Grant B17040; and JSPS (Japan Society for the Promotion of Science) KAKENHI under Grants 20H04566 and 22H03998. The authors would like to thank Miss Yinghui Hu at China University of Geosciences (she is now with Jiangsu University of Technology) for her contribution to this study.

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Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Xiaofeng Zong, Feng Wang & Juan Zhao

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Xiaofeng Zong, Feng Wang & Juan Zhao

  3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China

    Xiaofeng Zong, Feng Wang & Juan Zhao

  4. School of Engineering, Tokyo University of Technology, Hachioji, Tokyo, 192-0982, Japan

    Jinhua She

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  1. Xiaofeng Zong
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Correspondence to Jinhua She.

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Zong, X., Wang, F., She, J. et al. Denoising Method for Surface Electromyography Signals Combining CEEMDAN and Interval Total Variation. Circuits Syst Signal Process 41, 6493–6512 (2022). https://doi.org/10.1007/s00034-022-02108-1

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