Xie Hong-Bo, Zheng Yong-Ping, Jing-Yi Guo
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, P.R.China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4379-82. doi: 10.1109/IEMBS.2009.5333485.
We undertake the study of the chaotic nature of mechanomygraphy (MMG) signal by recourse to the recent developments in the field of nonlinear dynamics. The MMG signals were measured from biceps brachii muscle of 5 subjects during fatigue of isometric contraction at 80% maximal voluntary contraction (MVC) level. Deterministic chaotic character was detected in all data by using the Volterra-Wiener-Korenberg model and noise titration approach. The noise limit (NL), which is a power indicator of chaos of fatigue MMG signals, is 22.2000 + or - 8.7293. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D(2), which is 3.3524 + or - 0.3645 across all the subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for the analysis and modeling the MMG signals.
我们借助非线性动力学领域的最新进展，对肌动图（MMG）信号的混沌特性进行了研究。在5名受试者的肱二头肌进行80%最大自主收缩（MVC）水平的等长收缩疲劳过程中，测量了MMG信号。通过使用Volterra-Wiener-Korenberg模型和噪声滴定法，在所有数据中检测到了确定性混沌特征。噪声极限（NL）是疲劳MMG信号混沌的功率指标，为22.2000±8.7293。此外，我们通过计算MMG信号的关联维数D(2)来研究其非线性动力学特征，所有受试者的该值为3.3524±0.3645。这些结果表明，MMG是一个高维混沌信号，并支持使用非线性动力学理论对MMG信号进行分析和建模。
