Fuzzy Approximate Entropy Analysis of Chaotic and Natural Complex Systems: Detecting Muscle Fatigue Using Electromyography Signals

Abstract

Abstract:In the present contribution, a complexity measure is proposed to assess surface 1 electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle 2 contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the 3 complexity of experimental data that is often corrupted with noise, short data-length, and in many cases, 4 has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an 5 improved ApEn measure, i.e., fuzziness approximate entropy (fApEn), which utilizes the fuzzy 6 membership function to define the vectors' similarity. Tests were conducted on independent, identically 7 distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, and Rossler, and Henon 8 maps. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, 9 and more robustness to noise when characterizing signals with different complexities. Performance 10 analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the 11 development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the 12 EMG signal, while the standard ApEn failed to detect this change. Moreover, the fApEn is more 13 sensitive to muscle fatigue than MNF with a larger linear regression slope (significant value p=0.0213). 14 The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for 15 muscle fatigue assessment and be applicable to other short noisy physiological signal analysis. 16 17

Muscle Fatigue Analysis of the Deltoid during Three Head-Related Static Isometric Contraction Tasks

“…In this study, the performance of five SEMG parameters (RMS, MPF, ARC1, HFD and SE) in depicting fatiguing characteristics of heads was evaluated, under different tasks and loads. Being consistent with previous studies [20,27,30], RMS and ARC1 (normalized) parameters showed increasing trends with fatigue in most cases. MPF, HFD and SE parameters showed decreasing trends with fatigue almost in all cases.…”

“…During isometric fatiguing contraction, the entropy of SEMG was found to decrease with time [27]. SE was derived from approximate entropy and the calculation procedure was described below.…”

Effect of increasing difficulty in standing balance tasks with visual feedback on postural sway and EMG: Complexity and performance

“…Complexity was calculated by the Fuzzy Entropy (FuzzyEn) method (Chen et al, 2007(Chen et al, , 2009Xie et al, 2010) 1 . Higher values of FuzzyEn thus represent lower repeatability of vectors X i of length m to that of m + 1, marking lower predictability of future data points, and greater irregularity within the time-series.…”

“…It is important to examine whether the properties of the EMG for individual muscles will be altered in a similar fashion to that of the COP (Morrison et al, 2007). Although only a limited number of papers have characterized surface EMG with measures of complexity, these measures provided useful information about neuromuscular activation (e.g., index of fatigue) and motor control (Chen, Wang, Xie, & Yu, 2007;Chen, Zhuang, Yu, & Wang, 2009;Farina, Fattorini, Felici, & Filligoi, 2002;Morrison et al, 2007;Rodrick & Karwowski, 2006;Xie, Guo, & Zheng, 2010). For example, Morrison et al (2007) have demonstrated that timedependent structure of the EMG signal changes significantly as a function of the postural task being performed.…”

Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy

“…Entropy was first introduced by Shannon [12], and different entropy-based measures such as approximate entropy (ApEn) [13], sample entropy (SampEn) [14], and fuzzy entropy (FuzzyEn) [15] have been widely applied to analyze physiological signals [16][17][18]. A decline in entropy value usually indicates an increase in signal regularity and a decrease in signal complexity [3].…”