Electromyography signal segmentation method based on spectral subtraction backtracking  

作　　者：CAI Huihui ZHANG Yakun XIE Liang YIN Erwei YAN Ye MING Dong 

机构地区：[1]Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin,300072,China [2]Tianjin Artificial Intelligence Innovation Center(TAIIC),Tianjin,300450,China [3]Defense Innovation Institute,Academy of Military Sciences(AMS),Beijing,100071,China

出　　处：《Optoelectronics Letters》2022年第10期623-627,共5页光电子快报（英文版）

摘　　要：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.

关 键 词：SIGNAL SPECTRAL METHOD 

分 类 号：TN911.7[电子电信—通信与信息系统] R318[电子电信—信息与通信工程]