Djuwari D, Kumar D K, Naik G R, Arjunan S P, Palaniswami M
Department of Electrical Engineering, Faculty of Engineering, Universitas Surabaya, Jl. Raya Kalirungkut, TB.2.3 Building, Surabaya 60293, Indonesia.
Electromyogr Clin Neurophysiol. 2006 Sep;46(5):295-309.
Surface electromyogram (SEMG) has numerous applications, but the presence of artefacts and noise, especially at low level of muscle activity make the recordings unreliable. Spectral and temporal overlap can make the removal of artefacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, numbers of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artefacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. The paper also reports tests using Zibulevsky's technique of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results identify the unsuitability of ICA when the number of sources is greater than the number of recording channels. The results also demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. The paper determines the suitability of the use of error measure using simulated mixing matrix and the estimated unmixing matrix as a means identifying the quality of separation of the output. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevs.ky's technique.
表面肌电图（SEMG）有众多应用，但伪迹和噪声的存在，尤其是在肌肉活动水平较低时，会使记录变得不可靠。频谱和时间上的重叠会使去除伪迹和噪声，或从其他生物电信号中分离出相关信号变得极其困难。在局部层面，单个肌肉可被视为独立的，这为使用独立成分分析（ICA）来分离信号提供了依据。最近，由于ICA工具易于获取，许多研究人员已尝试将ICA用于此应用。本文报告了为评估ICA在分离肌肉活动和去除SEMG伪迹方面的应用而开展的研究。它讨论了一些可能影响分离可靠性的条件，并评估了与信号特性和源数量相关的问题。本文还指出了缺乏适用于生物电信号分离质量的合适度量标准，并推荐并测试了一种更稳健的分离度量标准。本文还报告了使用齐布列夫斯基（Zibulevsky）的时间绘图技术来确定SEMG记录中独立源数量的测试。理论分析和实验结果表明，ICA适用于SEMG信号。结果表明，当源数量大于记录通道数量时，ICA不适用。结果还证明了此类应用的局限性，因为系统无法识别信号的正确顺序和幅度。本文确定了使用模拟混合矩阵和估计的解混矩阵作为识别输出分离质量的误差度量方法的适用性。这项工作表明，即使在肌肉收缩水平极低的情况下，以及使用小波和带通滤波器进行滤波时，也无法使数据稀疏到足以使用齐布列夫斯基技术识别独立源的数量。
