Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: Fuzzy approximate entropy analysis of chaotic and natural complex systems : detecting muscle fatigue using electromyography signals
Authors: Xie, HB
Guo, JY
Zheng, YP 
Issue Date: Apr-2010
Source: Annals of biomedical engineering, Apr. 2010, v. 38, no. 4, p. 1483-1496
Abstract: In the present contribution, a complexity measure is proposed to assess surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the complexity of experimental data that is often corrupted with noise, short data length, and in many cases, has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an improved ApEn measure, i.e., fuzzy approximate entropy (fApEn), which utilizes the fuzzy membership function to define the vectors’ similarity. Tests were conducted on independent, identically distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, Rossler equation, and Henon map. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, and more robustness to noise when characterizing signals with different complexities. Performance analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the EMG signal, while the standard ApEn failed to detect this change. Moreover, fApEn of EMG demonstrated a better robustness to the length of the analysis window in comparison with the MNF of EMG. The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for muscle fatigue assessment and be applicable to other short noisy physiological signal analysis.
Keywords: Fuzzy approximate entropy
Muscle fatigue
Time series analysis
Publisher: Springer Netherlands
Journal: Annals of biomedical engineering 
ISSN: 0090-6964
DOI: 10.1007/s10439-010-9933-5
Rights: © 2010 Biomedical Engineering Society. The original publication is available at
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Xie et al ABME 2010 Fuzzy approximate entropy for chaotic and natural complex.pdfPre-published version331.11 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of May 28, 2023


Citations as of May 28, 2023


Last Week
Last month
Citations as of Jun 1, 2023


Last Week
Last month
Citations as of Jun 1, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.