Xu Weizhao, Mo Hongqiang, Tian Lianfang, Ou Demiao
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P.R.China.
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641,
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Dec 25;35(6):953-958. doi: 10.7507/1001-5515.201703084.
Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.
表面肌电图(sEMG)已广泛应用于临床医学、康复医学、体育等领域的研究,在进行分析之前应准确检测其端点。然而,当sEMG记录仪放置在心脏附近时,端点检测容易受到心电图(ECG)干扰。本文提出了一种对ECG干扰不敏感的端点检测算法。该算法基于sEMG的短时能量和短时过零率来检测sEMG的端点。根据sEMG与ECG之间短时过零率的统计差异以及sEMG与背景噪声之间短时能量的统计差异来设置短时能量和短时过零率的阈值。腹直肌sEMG的实验结果表明,该算法检测sEMG端点的准确率高达95.6%。