Xie Hong-Bo, Zheng Yong-Ping, Jing-Yi Guo
Jiangsu University, Jiangsu, 212013 PR of China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2971-4. doi: 10.1109/IEMBS.2009.5332503.
A new method, namely Cross fuzzy entropy (C-FuzzyEn) analysis, that could enable the measurement of the synchrony or similarity of patterns between two distinct signals, was presented in this study. Tests on simulated data sets showed that C-FuzzyEn was superior to the conventional cross sample entropy (C-SampEn) in several aspects, including giving entropy definition in case of small parameters, better relative consistency, and less dependence on record length. The proposed C-FuzzyEn was then applied for the analysis of simultaneously recorded electromyography (EMG) and mechanomyography (MMG) signals during sustained isometric contraction for monitoring local muscle fatigue. The results showed that the C-FuzzyEn of EMG-MMG decreased significantly during the development of muscle fatigue. The time-decrease trend of C-FuzzyEn is similar to the mean frequency (MNF) of EMG, the commonly used muscle fatigue indicator.
本研究提出了一种新方法，即交叉模糊熵（C-FuzzyEn）分析，该方法能够测量两个不同信号之间模式的同步性或相似性。对模拟数据集的测试表明，C-FuzzyEn在几个方面优于传统的交叉样本熵（C-SampEn），包括在参数较小时给出熵定义、具有更好的相对一致性以及对记录长度的依赖性较小。然后，将提出的C-FuzzyEn应用于持续等长收缩期间同时记录的肌电图（EMG）和肌机械图（MMG）信号的分析，以监测局部肌肉疲劳。结果表明，在肌肉疲劳发展过程中，EMG-MMG的C-FuzzyEn显著降低。C-FuzzyEn的时间下降趋势与常用的肌肉疲劳指标EMG的平均频率（MNF）相似。
