An Adaptive Algorithm for the Determination of the Onset and Offset of Muscle Contraction by EMG Signal Processing
An Adaptive Algorithm for the Determination of the Onset and Offset of Muscle Contraction by EMG Signal Processing
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DOI：
10.1109/tnsre.2012.2226916
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发表时间：
2013-01-01
影响因子：
4.9
通讯作者：
He, Jiping
中科院分区：
文献类型：
--
作者：
Xu, Qi;Quan, Yazhi;He, Jiping
运动过程中人体骨骼肌开关时间的估计是相关临床应用表面肌电图 (sEMG) 信号处理中持续存在的问题。广泛使用的单一阈值方法仍然依赖操作员的经验来手动建立阈值水平。在本文中，提出了一种解决该问题的新方法。在广义似然比测试的基础上，利用基于准确表面肌电分析之前初始时间的信噪比（SNR）估计的自适应阈值技术改进了最大似然（ML）方法。最佳阈值对 SNR 的依赖性是通过最小化具有众所周知的信号参数的大量模拟信号的起始/偏移估计误差来确定的。通过使用一组模拟信号和真实的 sEMG 信号来评估算法的准确性和精确性，这些信号是从两名健康受试者在有或没有工作负荷的肘部屈曲-伸展运动中记录的。与传统算法的比较表明，随着计算量的适度增加，ML 算法即使对于低水平的 EMG 活动也能表现良好，而所提出的自适应方法对于 SNR 的变化来说是最稳健的。此外，我们还讨论了分析两名偏瘫受试者上肢近端肌肉 sEMG 记录的结果。该检测算法是自动且独立于用户的，管理起始激活和偏移激活的检测，并且适用于存在噪声的情况，允许熟练和不熟练的操作员都可以使用。
Estimation of on-off timing of human skeletal muscles during movement is an ongoing issue in surface electromyography (sEMG) signal processing for relevant clinical applications. Widely used single threshold methods still rely on the experience of the operator to manually establish a threshold level. In this paper, a novel approach to address this issue is presented. Based on the generalized likelihood ratio test, the maximum likelihood (ML) method is improved with an adaptive threshold technique based on the signal-to-noise ratio (SNR) estimate in the initial time before accurate sEMG analyses. The dependence of optimal threshold on SNR is determined by minimizing the onset/offset estimate error on a large set of simulated signals with well-known signal parameters. Accuracy and precision of the algorithm were assessed by using a set of simulated signals and real sEMG signals recorded from two healthy subjects during elbow flexion-extension movements with and without workload. Comparison with traditional algorithms shows that with amoderate increase in the computational effort the ML algorithm performs well even for low levels of EMG activity, while the proposed adaptive method is most robust with respect to variations in SNRs. Also, we discuss the results of analyzing the sEMG recordings from the selected proximal muscles of the upper limb in two hemiparetic subjects. The detection algorithm is automatic and user-independent, managing the detection of both onset and offset activation, and is applicable in presence of noise allowing use by skilled and unskilled operators alike.