【摘要】 目的：通过研究股外侧肌疲劳时的肌电信号，探讨骨骼肌疲劳过程中的肌电信号非线性动力学特征。方法：15名受试者首先在等速测力仪上进行最大随意收缩（MVC）测试，受试者以30%、50%、70%MVC运动至疲劳，同步进行高密度阵列式表面肌电测试。结果：（1）在梯度运动强度中，肌电平均整流值（ARV）和均方根振幅值（RMS）均随骨骼肌疲劳呈线性增加（P<0.05）,70%MVC下肌电ARV及RMS值最高，30%MVC下肌电ARV及RMS值最低（P<0.05）;(2)肌电平均功率频率（MPF）值随疲劳呈线性下降（P<0.05）,70%MVC下肌电MPF下降斜率绝对值最大，30%MVC下肌电MPF下降斜率绝对值最小（P<0.05）,50%和70%MVC肌肉收缩至疲劳过程中，中位频率呈下降趋势（P<0.05）,30%MVC肌肉收缩至疲劳过程中，中位频率呈先上升后下降趋势（P<0.05）;(3)非线性指标复杂度（LZc）、分形维数（FD）、多尺度样本熵、科尔莫哥洛夫熵均呈线性下降（P<0.05），非线性指标LLE值在30%和50%MVC下随疲劳呈上升趋势（P<0.05），在70%MVC下随疲劳呈先上升后下降趋势（P<0.05）;(4)空间域指标运动变异性随疲劳呈线性增长（P<0.05）。结论：肌电信号是一种介于周期信号和随机信号之间的混沌信号，其具有非线性特征，因此可以采用LZc、FD、LLE等非线性特征评估肌肉疲劳状态。更多还原

【Abstract】 Objective To investigate the nonlinear dynamic characteristics of electromyogram(EMG) signals during skeletal muscle fatigue by analyzing EMG signals during the lateral femoral muscle fatigue. Methods Fifteen subjects underwent a maximal voluntary contraction(MVC) test on an isokinetic dynamometer, and they performed contractions at intensities of 30%, 50%, and70% MVC until fatigue, with synchronous recording of high-density array surface EMG signals. Results(1) At graded exercise intensities, average rectified value(ARV) and root mean square(RMS)amplitude of EMG signals increased linearly with skeletal muscle fatigue(P<0.05), with the highest EMG ARV and RMS values at 70% MVC and the lowest EMG ARV and RMS values at 30% MVC(P<0.05).(2) EMG mean power frequency(MPF) value decreased linearly with fatigue(P<0.05). The absolute value of the MPF decline slope was the largest at 70% MVC, and the smallest at 30% MVC(P<0.05). The median frequency trended downward during 50% and 70% MVC muscles contraction to fatigue(P<0.05), while the median frequency firstly increased and then decreased during 30% MVC muscle contraction to fatigue(P<0.05).(3) Nonlinear indexes, including complexity(LZc), fractal dimension, multiscale sample entropy, and Kolmogorov’s entropy all showed linear decreases with fatigue(P<0.05). The largest Lyapunov exponent value at 30% and 50% MVC increased with fatigue(P<0.05), whereas it displayed a trend of initial increase followed by decrease at 70% MVC(P<0.05).(4) The spatial domain index, motor variability, increased linearly with fatigue(P<0.05). Conclusion EMG signal is a kind of chaotic signal between periodic and random signals, possessing nonlinear characteristics. Therefore, nonlinear characteristics such as LZc, fractal dimension, and largest Lyapunov exponent can be used to assess the state of muscle fatigue.更多还原