Biomedical Engineering Program, University of Science and Technology of China, Hefei, Anhui, People's Republic of China. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA. Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA.
Physiol Meas. 2014 Jan;35(1):45-54. doi: 10.1088/0967-3334/35/1/45. Epub 2013 Dec 17.
Surface electromyography (EMG) signal from trunk muscles is often contaminated by electrocardiography (ECG) artifacts. This study presents a novel method for muscle activity onset detection by processing surface EMG against ECG artifacts. The method does not require removal of ECG artifacts from raw surface EMG signals. Instead, it applies the sample entropy (SampEn) analysis to highlight EMG activity and suppress ECG artifacts in the signal complexity domain. A SampEn threshold can then be determined for detection of muscle activity. The performance of the proposed method was examined with different SampEn analysis window lengths, using a series of combinations of 'clean' experimental EMG and ECG recordings over a wide range of signal to noise ratios (SNRs) from -10 to 10 dB. For all the examined SNRs, the window length of 128 ms yielded the best performance among all the tested lengths. Compared with the conventional amplitude thresholding and integrated profile methods, the SampEn analysis based method achieved significantly better performance, demonstrated as the shortest average latency or error among the three methods (p < 0.001 for any of the examined SNRs except 10 dB).
表面肌电图（EMG）信号常受到心电图（ECG）伪迹的干扰。本研究提出了一种新的方法，通过处理表面 EMG 对抗 ECG 伪迹来检测肌肉活动的起始。该方法不需要从原始表面 EMG 信号中去除 ECG 伪迹。相反，它在信号复杂度域中应用样本熵（SampEn）分析来突出 EMG 活动并抑制 ECG 伪迹。然后可以确定 SampEn 阈值以检测肌肉活动。使用一系列在宽 SNR 范围（-10 至 10 dB）下具有“干净”实验 EMG 和 ECG 记录的组合，检查了所提出方法的性能，针对不同的 SampEn 分析窗口长度进行了检查。对于所有检查的 SNR，128 ms 的窗口长度在所有测试长度中表现出最佳性能。与传统的幅度阈值和积分轮廓方法相比，基于 SampEn 分析的方法表现出显著更好的性能，表现为三种方法中最短的平均潜伏期或误差（除 10 dB 以外，任何检查的 SNR 下均为 p < 0.001）。
