TY - JOUR
T1 - Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue
T2 - A review
AU - Rampichini, Susanna
AU - Vieira, Taian Martins
AU - Castiglioni, Paolo
AU - Merati, Giampiero
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Approximate entropy
KW - Correlation dimension
KW - Detrended fluctuation analysis
KW - Fractal dimension
KW - Fuzzy entropy
KW - Largest lyapunov exponent
KW - Recurrence quantification analysis
KW - Sample entropy
KW - SEMG
UR - http://www.scopus.com/inward/record.url?scp=85085621080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085621080&partnerID=8YFLogxK
U2 - 10.3390/E22050529
DO - 10.3390/E22050529
M3 - Review article
AN - SCOPUS:85085621080
VL - 22
JO - Entropy
JF - Entropy
SN - 1099-4300
IS - 5
M1 - 529
ER -