Fractals, chaos and entropy analysis to obtain parametric features of surface electromyography signals during dynamic contraction of biceps muscles under varying load

Chakraborty, Monisha; Parbat, Debanjan

doi:10.1109/i2ct.2017.8226125

Cited by 19 publications

(20 citation statements)

References 15 publications

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“…The description of fractal structures goes through the determination of fractal dimension (FD), an index characterizing “the complexity and space filling propensity of a structure” [ 71 ]. Transposed to time series signals, FD has been demonstrated to describe the self-similarity of a pattern over multiple time-scale [ 71 , 72 ]. FD can be estimated with different algorithms and a popular one is the Katz’s method [ 73 ] which, however, provides FD estimates that may depend on the length of the time series [ 74 ].…”

Section: Resultsmentioning

confidence: 99%

“…Despite the different studies using ApEn on EMG signals, its consistency and reliability have recently been questioned [ 72 ]. Zhou et al employed SampEn and FuzzyEn to interpret sEMG collected at different intensity levels of contraction and found a very weak correlation between SampEn and muscle torque while FuzzyEn showed a direct positive correlation with the effort [ 134 ].…”

Section: Resultsmentioning

confidence: 99%

“…Muscle activation and λ LLE . The Rosenstein method for calculating λ LLE was applied on EMG signals by Chakraborty and Parbat [ 72 ] for the assessment of chaotic patterns during isotonic contractions of biceps brachii muscle (arm flexion with 1 kg load). Considering the stochastic nature of EMG, the authors used Cao’s method for determining the embedding dimension [ 146 ], whereas the time delay was determined through Kraskov’s mutual information function [ 147 ], the mean period was obtained as the reciprocal of the median frequency found by the average Welch periodogram technique and 100 iterations were used as the last input variable.…”

Section: Resultsmentioning

confidence: 99%

“…The estimation of the largest Lyapunov exponent had limited applications in the evaluation of muscle manifestation of fatigue. The λ LLE value did not change with the increase of the muscle load in [ 72 ], although it was unlikely that the load used in this work provoked a significant fatigue state in the tested muscle (biceps brachii). Significant reductions in the dynamic stability of low back EMG were found during a fatiguing task (30 repetitions of trunk extension) by means of the maximum Lyapunov exponent [ 148 ].…”

Section: Resultsmentioning

confidence: 99%

“…Several papers focusing on the complex behavior of EMG demonstrated so far that the EMG signal is non-linear in nature and expresses the features of a low dimension chaotic system [ 72 , 100 , 104 ]. Many complexity indices have been therefore used in characterizing the changes occurring in EMG with muscle activation and with fatigue.…”

Section: Discussionmentioning

confidence: 99%

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Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

Rampichini

Vieira

Castiglioni

et al. 2020

Entropy

103

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The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

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