Independent component analysis based algorithms for high-density electromyogram decomposition: Experimental evaluation of upper extremity muscles
Independent component analysis based algorithms for high-density electromyogram decomposition: Experimental evaluation of upper extremity muscles
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DOI：
10.1016/j.compbiomed.2019.03.009
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发表时间：
2019-05-01
影响因子：
7.7
通讯作者：
Hu, Xiaogang
中科院分区：
文献类型：
--
作者：
Dai, Chenyun;Hu, Xiaogang
运动单元放电活动可以提供有关骨骼肌神经控制的重要信息。从表面肌电图（EMG）中可靠地提取运动单元活动仍然是信号处理中的一个挑战。我们量化了三种不同的基于独立分量分析（ICA）的分解算法（Infomax、FastICA和RobustlCA）对不同收缩水平下手臂肌肉（肱二头肌和指间伸肌）高密度肌电信号的性能。基于不同算法在放电时序上的一致程度，以及两种算法同时识别的常见运动单元的数量，对源分离结果进行评估。两个指标，分离指数（剪影距离或SIL）和一致性，被用来评估分解的准确性。我们的结果显示，不同算法之间的一致性很高（80%-90%），这在不同的收缩水平上是一致的。对于其他两种算法（尤其是Infomax）， RobustlCA倾向于显示更高的RoA，而FastICA和Infomax倾向于产生更多的公共mu。总的来说，通过对三种算法的实验评估，结果提供了有关这些算法的效用和涉及上肢肌肉肌电图信号的运动单元过滤标准的信息。
Motor unit firing activities can provide critical information regarding neural control of skeletal muscles. Extracting motor unit activities reliably from surface electromyogram (EMG) is still a challenge in signal processing. We quantified the performance of three different independent component analysis (ICA)-based decomposition algorithms (Infomax, FastICA and RobustlCA) on high-density EMG signals, obtained from arm muscles (biceps brachii and extensor digitorum communis) at different contraction levels. The source separation outcomes were evaluated based on the degree of agreement in the discharge timings between different algorithms, and based on the number of common motor units identified concurrently by two algorithms. Two metrics, the separation index (silhouette distance or SIL) and the rate of agreement, were used to evaluate the decomposition accuracy. Our results revealed a high rate of agreement (80%-90%) between different algorithms, which was consistent across different contraction levels. The RobustlCA tended to show a higher RoA with the other two algorithms (especially with Infomax), whereas FastICA and Infomax tended to yield a greater number of common MUs. Overall, through an experimental evaluation of the three algorithms, the outcomes provide information regarding the utility of these algorithms and the motor unit filter criteria involving EMG signals of upper extremity muscles.