Uncovering chaotic structure in mechanomyography signals of fatigue biceps brachii muscle

Abstract

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.