Optoelectronics Letters, Volume. 18, Issue 10, 623(2022)

Electromyography signal segmentation method based on spectral subtraction backtracking

Huihui CAI1,2,3, Yakun ZHANG2,3、*, Liang XIE2,3, Erwei YIN1,2,3, Ye YAN2,3, and Dong and MING1
Author Affiliations
  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
  • 2Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
  • 3Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
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    Surface electromyography (EMG) is a bioelectrical signal that recognizes speech contents in a non-acoustic form. Activity detection is an important research direction in EMG research. However, in the low signal-to-noise ratio (SNR) environment, it is difficult for traditional methods to obtain accurate active signals. This paper proposes a new energy-based spectral subtraction backtracking (E-SSB) method to segment EMG active signal in the low SNR environment. Compared with traditional energy detection, the algorithm in this paper adds spectral subtraction (SS) to filter out the clutter, and raises a retrospective idea to improve the classification performance. The experiment results show the proposed activity detection method is more effective than other methods in the low SNR environment.

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    CAI Huihui, ZHANG Yakun, XIE Liang, YIN Erwei, YAN Ye, and MING Dong. Electromyography signal segmentation method based on spectral subtraction backtracking[J]. Optoelectronics Letters, 2022, 18(10): 623

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    Paper Information

    Received: Apr. 13, 2022

    Accepted: Jun. 11, 2022

    Published Online: Jan. 20, 2023

    The Author Email: Yakun ZHANG (ykzhang1222@126.com)

    DOI:10.1007/s11801-022-2058-x

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