Chaos, Fractal and Recurrence Quantification Analysis of Surface Electromyography in Muscular Dystrophy

Abstract

We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component. We have estimated the correlation dimension (fractal measure), the largest Lyapunov exponent, the LZ complexity and the %Rec and %Det of the RQA demonstrating that such indexes are able to detect the presence of repetitive hidden patterns in sEMG which, in turn, senses the level of MU synchronization within the muscle. The results give also an interesting methodological indication in the sense that it evidences the manner in which nonlinear methods and RQA must be arranged and applied in clinical routine in order to obtain results of clinical interest. We have studied the muscular dystrophy and evidence that the continuous regime of chaotic transitions that we have in muscular mechanisms may benefit in this pathology by the use of the NPT treatment that we have considered in detail in our previous publications.

Control technologies of lower limb rehabilitation exoskeleton robots based on surface electromyography: A review

“…Chaos theory is a kind of stochastic process that appears in a deterministic system and has the characteristics of nonlinearity, uncertainty, non-balance, and sensitivity. Chaos theory can be used for the analysis and processing of sEMG and is helpful to extract more information from sEMG [28,29]. The clustering characteristics of chaotic features can be expressed through Lyapunov index, phase planar graph, power spectrum and other parameters.…”

Analysis of Brain-Neuromuscular Synchronization and Coupling Strength in Muscular Dystrophy after NPT Treatment

“…In the third paper [3] we discussed muscular dystrophy with performed HRV (Hear Rate Variability) analysis. The results were focalized on a subject evidencing a net ANS dysfunction before the NPT treatment.…”