Exercise-induced muscle damage is a condition which results in the loss of muscle function due to overexertion. Muscle fatigue is a precursor of this phenomenon. The characterization of muscle fatigue plays a crucial role in preventing muscle damage. In this work, an attempt is made to develop signal processing methods to understand the dynamics of the muscle's electrical properties. Surface electromyography signals are recorded from 50 healthy adult volunteers under dynamic curl exercise. The signals are preprocessed, and the first difference signal is computed. Furthermore, ascending and descending slopes are used to generate a binary sequence. The binary sequence of various motif lengths is analyzed using features such as the average symbolic occurrence, modified Shannon entropy, chi-square value, time irreversibility, maximum probability of pattern and forbidden pattern ratio. The progression of muscle fatigue is assessed using trend analysis techniques. The motif length is optimized to maximize the rho value of features. In addition, the first and the last zones of the signal are compared with standard statistical tests. The results indicate that the recorded signals differ in both frequency and amplitude in both inter- and intra-subjects along the period of the experiment. The binary sequence generated has information related to the complexity of the signal. The presence of more repetitive patterns across the motif lengths in the case of fatigue indicates that the signal has lower complexity. In most cases, larger motif length resulted in better rho values. In a comparison of the first and the last zones, most of the extracted features are statistically significant with p < 0.05. It is observed that at the motif length of 13 all the extracted features are significant. This analysis method can be extended to diagnose other neuromuscular conditions.

中文翻译：

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肱二头肌肱肌的表面肌电信号表征的疲劳使用象征性的图案特征。

运动引起的肌肉损伤是一种由于过度运动导致肌肉功能丧失的状况。肌肉疲劳是这种现象的先兆。肌肉疲劳的特征在防止肌肉损伤中起着至关重要的作用。在这项工作中，尝试开发信号处理方法以了解肌肉电特性的动态。在动态卷曲运动下，记录了来自50名健康成人志愿者的表面肌电信号。对信号进行预处理，并计算出第一差分信号。此外，使用上升和下降斜率生成二进制序列。使用平均符号出现率，修正的Shannon熵，卡方值，时间不可逆性，模式的最大概率和禁止的模式比率。使用趋势分析技术评估肌肉疲劳的进展。优化了图案长度，以最大化特征的rho值。此外，将信号的第一个和最后一个区域与标准统计测试进行比较。结果表明，在整个实验过程中，被摄物体之间和物体内部的频率和幅度都不同。产生的二进制序列具有与信号复杂度有关的信息。在疲劳的情况下，整个图案长度上存在更多重复的图案，这表明信号具有较低的复杂度。在大多数情况下，较大的主题长度会导致更好的rho值。在比较第一个和最后一个区域时，大多数提取的特征具有统计学意义，p <0.05。可以看出，在13个基序长度处，所有提取的特征都很重要。这种分析方法可以扩展到诊断其他神经肌肉疾病。