基于边际谱熵的肌肉疲劳实时评估方法研究

Real-time assessment of muscle fatigue based on marginal spectrum entropy

肌肉疲劳是一种复杂的生理现象。针对利用表面肌电信号实时评估肌肉疲劳,要求疲劳指标兼具快速、可靠、抗噪的问题,提出基于边际谱熵的肌肉疲劳实时评估方法。首先,利用不同数据长度的确定性周期信号和高斯白噪声分析了边际谱熵快速性与数据长度稳健性;其次,利用10名受试者握力持续静态收缩状态下从100%MVC下降到50%MVC时桡侧腕长伸肌的肌肉疲劳信号,分析了边际谱熵评估肌肉疲劳的可靠性与应用于不同个体的稳定性;最后,在某一受试者肌肉疲劳信号中加入高斯白噪声和心电噪声考察了边际谱熵的抗噪性。实验结果表明,边际谱熵与近似熵和中值频率相比计算快速,数据长度稳健性更优;线性拟合优度较佳(0.46±0.14),能可靠地评估肌肉疲劳;斜率变异系数较低(30.30%),对不同个体稳定性高;加入高斯白噪声和心电噪声后边际谱熵拟合优度变化率较低(分别为34.39%和3.78%),具有良好的抗噪性。因此边际谱熵兼具快速、能可靠评估肌肉疲劳以及抗噪等优点,为实时评估肌肉疲劳提供一种新方法。

Muscle fatigue is a complex physiological phenomenon. The real-time assessment of muscle fatigue with surface electromyography requires that fatigue indices are rapid, noise immunity, and reliable. The real-time assessment of muscle fatigue is proposed based on the marginal spectrum entropy. First, deterministic periodic signals with different data lengths and Gauss white noise are used to analyze the rapidity of marginal spectrum entropy and the robustness of data length. When 10 subjects grip under continuous static contraction state decreases from 100% maximal voluntary contraction to 50% maximal voluntary contraction, the muscle fatigue signals of long extensor carpi are used to analyze the reliability of the assessment method and the stability for different individuals. Finally, the noise immunity of marginal spectral entropy is investigated by adding Gauss white noise and electrocardiogram noise into the muscle fatigue signal of a subject. Experimental results show that the calculation based on marginal spectrum entropy is more rapid and data length robustness is more obvious, compared with approximate entropy and median frequency. Goodness of fit based on marginal spectrum entropy is better（0.46 ＋ 0.14） , and the marginal spectrum entropy can reliably assess muscle fatigue. Slope of the coefficient of variation is lower（ 30.30% ）, and the marginal spectrum entropy is highly stable for different individuals. Rate of change of the goodness of fit of the marginal spectral entropy is lower after Gaussian white noise and electrocardiogram noise are added （ additive Gaussian white noise and additive electrocardiogram noise are 34.39% and 3.78% respectively）. It can be concluded that the marginal spectrum entropy is suitable for real-time assessment of muscle fatigue, which provides a new method for muscle fatigue assessment.