Fatigue-Mediated Loss of Complexity is Contraction-Type Dependent in Vastus Lateralis Electromyographic Signals

Hernandez, Luis Radford; Camic, Clayton L.

doi:10.3390/sports7040078

Cited by 13 publications

(35 citation statements)

References 40 publications

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Section: Resultsmentioning

confidence: 99%

“…DFA and muscle activation : Different studies demonstrated an increase of the DFA scaling exponent with muscle effort [ 91 , 92 , 93 ]. In addition, concentric contractions result in lower α values compared to isometric and eccentric contractions, with scale exponents close to one (the characteristic value of 1/ f power law phenomena) suggesting a higher level of complexity [ 93 ]. Such difference were explained by the different levels of motor unit recruitment which occur during concentric versus eccentric contraction [ 94 , 95 ] and possibly by the different motor control strategy which regulates concentric and isometric contraction [ 96 , 97 ].…”

Section: Resultsmentioning

confidence: 99%

“…The MMF as assessed by the DFA scale exponent results in a significant loss of signal complexity. Interestingly, Hernandez et al [ 93 ] recently found a significant multivariate effect from fatigue status and muscle contraction type. They found that α DFA was significantly lower during non-fatigued compared to fatigued conditions and during concentric compared to isometric contractions.…”

Section: Resultsmentioning

confidence: 99%

“…The use of entropy algorithms to study MMF has been recently evaluated [ 121 , 129 , 132 , 142 ]. Hernandez et al [ 93 ] recently studied the individual influence of fatiguing contractions and of different contraction types on the complexity of sEMG signal by SampEn and DFA. The effect of the combination of both factors were also evaluated.…”

Section: Resultsmentioning

confidence: 99%

“…The effect of the combination of both factors were also evaluated. Given that SampEn values decreased in fatigued conditions and different values were found among the contraction types, the Authors concluded that “sEMG complexity is affected by fatigue status and contraction type, with the degree of fatigue-mediated loss of complexity dependent on the type of contraction used to elicit fatigue” [ 93 ] ( Figure 6 ).…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

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

Rampichini

Vieira

Castiglioni

et al. 2020

Entropy

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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

Rampichini

Vieira

Castiglioni

et al. 2020

Entropy

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Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

Beretta-Piccoli

Cescon

D’Antona

2020

Arab Journal of Basic and Applied Sciences

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In literature, it is commonly reported that the progress of performance fatigability may be indirectly assessed through the changes in the features of the surface electromyogram (sEMG) signal. In particular, during isometric constant force contractions, changes in the sEMG signal are caused by several physiological factors, such as a decay in muscle fibers conduction velocity (CV), an increase of the degree of synchronization between the firing times of simultaneously active motor units (MUs), by the central nervous system, and a reduction of the recruitment threshold and a modulation of MUs firing rate. Amplitude and spectral parameters may be used to characterize the global contributions to performance fatigability, such as MU control properties and fiber membrane properties, or central and peripheral factors, respectively. In addition, being CV a physiological parameter, its estimation is of marked interest to the study of fatigue both in physiological and in presence of neuromuscular diseases.

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Upper and lower limb performance fatigability in people with multiple sclerosis investigated through surface electromyography: a pilot study

et al. 2020

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Objective: Fatigue experienced by people with multiple sclerosis (pwMS) is multidimensional, consisting of different components, such as perceived, physical and cognitive fatigue and performance fatigability. At present, there is no gold standard to assess performance fatigability in pwMS; therefore, we aimed to determine whether, during a fatiguing task, average rectified value (ARV), mean frequency of the power spectrum (MNF), muscle fiber conduction velocity (CV) and fractal dimension (FD) of surface electromyography (sEMG) may be used as indirect indices of performance fatigability. Moreover, we analyzed whether a three-week rehabilitation program impacts on performance fatigability in pwMS, and whether a relationship between sEMG parameters and trait levels of perceived fatigability, before and after rehabilitation, does exist. Approach: Twenty-one pwMS performed a 20% maximal voluntary contraction (MVC) of 1 min, and afterwards a 60% MVC held until exhaustion. sEMG signals were detected from the biceps brachii, vastus medialis and vastus lateralis. Performance fatigability was determined at entry to (t 0) and discharge from (t 1) rehabilitation. Perceived fatigability was measured at t 0 and t 2, one month after rehabilitation. Main results: ARV, MNF, CV and FD rates of change showed significant changes at t 0 and t 1 (p < 0.05) during the high-level contraction in the BB, but rather limited in the vastii muscles. Moreover, rehabilitation did not induce any reductions in either perceived or performance fatigability. No significant correlations between ARV, MNF, CV and FD rates of change during the 60% MVC and perceived fatigability, at t 0 and t 2, were found. Significance: Our findings suggest that the sEMG parameters are useful for indirectly assessing performance fatigability in pwMS during sub-maximal fatiguing contractions, particularly in the biceps brachii.

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Fatigue-Mediated Loss of Complexity is Contraction-Type Dependent in Vastus Lateralis Electromyographic Signals

Cited by 13 publications

References 40 publications

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

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

Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

Upper and lower limb performance fatigability in people with multiple sclerosis investigated through surface electromyography: a pilot study

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