Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
J Biomech. 2010 Apr 19;43(6):1224-6. doi: 10.1016/j.jbiomech.2009.11.035. Epub 2009 Dec 22.
The mechanomyography (MMG) signal reflects mechanical properties of limb muscles that undergo complex phenomena in different functional states. We undertook the study of the chaotic nature of MMG signals by referring to recent developments in the field of nonlinear dynamics. MMG signals were measured from the 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, a power indicator of the chaos of fatigue MMG signals, was 22.20+/-8.73. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D(2), which was 3.35+/-0.36 across subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for analysis and modeling of fatigue MMG signals.
肌动描记术（MMG）信号反映了肢体肌肉的力学特性，这些肌肉在不同的功能状态下会经历复杂的现象。我们通过参考非线性动力学领域的最新发展，研究了 MMG 信号的混沌性质。在 80%最大自主收缩（MVC）水平的等长收缩疲劳期间，我们从 5 名受试者的肱二头肌测量了 MMG 信号。使用 Volterra-Wiener-Korenberg 模型和噪声滴定法在所有数据中检测到确定性混沌特征。疲劳 MMG 信号混沌的噪声极限为 22.20+/-8.73。此外，我们通过计算它们的关联维数 D(2)来研究 MMG 信号的非线性动力学特征，跨受试者的 D(2)为 3.35+/-0.36。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来分析和建模疲劳 MMG 信号。
