Noninvasive Imaging and Diagnostics Lab, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
J Med Syst. 2016 Jan;40(1):28. doi: 10.1007/s10916-015-0394-0. Epub 2015 Nov 7.
Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.
肌电疲劳分析在临床研究和生物力学等领域都有广泛的应用。表面肌电（sEMG）信号由于其非侵入性而被广泛用于这些研究。在周期性动态收缩过程中，这些信号是非平稳和循环平稳的。近年来，已经采用了几种非平稳方法来进行肌肉疲劳分析。然而，基于循环平稳性的方法还没有很好地建立起来用于评估肌肉疲劳。在这项工作中，使用 sEMG 信号和谱相关密度（SCD）函数来分析二头肌肌疲劳进展的循环平稳性。信号是从五十名健康成年人在规定的协议下进行动态收缩时记录的。这些信号经过预处理，并分为三个部分，即非疲劳、第一肌肉不适和疲劳区。然后使用快速傅里叶变换积累法估计 SCD。进一步，从 SCD 谱中计算循环频率谱密度（CFSD）。提出了两个特征，即循环频率谱面积（CFSA）和循环频率谱熵（CFSE），以研究肌肉疲劳的进展。此外，计算了循环平稳度（DCS）以量化信号中存在的循环平稳度的数量。结果表明，在肌肉疲劳进展过程中循环平稳度逐渐增加。CFSA 在肌肉疲劳收缩中呈增加趋势。然而，CFSE 呈下降趋势。观察到当肌肉从非疲劳状态进展到疲劳状态时，五十个受试者的平均 DCS 从 0.016 增加到 0.99。在肌肉收缩的三个区域（p<0.05）中，所有提取的特征都表现出明显的差异和统计学意义。似乎这些 SCD 特征可用于不同神经肌肉条件下的 sEMG 信号的自动分析。
