Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA.
J Electromyogr Kinesiol. 2012 Dec;22(6):901-7. doi: 10.1016/j.jelekin.2012.06.005. Epub 2012 Jul 15.
Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.
自愿表面肌电图（EMG）信号有时会受到生理和外在或意外来源的虚假背景尖峰的干扰。本研究提出了一种针对这种虚假尖峰的肌肉活动起始检测的新方法，主要基于表面 EMG 的样本熵（SampEn）分析。该方法利用 SampEn 分析的非线性特性，在复杂度域中区分自愿表面 EMG 信号和虚假背景尖峰。为了便于肌肉活动起始检测，首先对表面 EMG 进行 SampEn 分析，以突出自愿 EMG 活动，同时抑制虚假背景尖峰。然后，优化 SampEn 阈值以进行肌肉活动起始检测。使用半合成和实验表面 EMG 信号检验了所提出方法的性能。基于 SampEn 的方法有效地降低了虚假背景尖峰引起的检测误差，并在 Teager Kaiser 能量域中基于常规幅度阈值或其扩展版本的方法的性能得到了提高。
