Approximate Entropy in Electromyography during Muscle Fatigue

Authors: Payam Wali M. Hussein1 & Kadir Tufan2 & Serkan Dursun3
1Computer Engineering Department, Faculty of Engineering, Ishik University, Erbil, Iraq
2Faculty of Computer Science and IT, Tirana, Albania
3Computer Science Department at University of Houston, Houston, TX, USA

Abstract:  Muscle fatigue (MF) is a phenomenon that involves the decline of one’s ability to perform physical action. The early detection of MF is important in the field of ergonomics, sports, occupational work, and human-computer interaction, as MF affects performance and may cause injury. Since MF is not a quantitative value, existing researches in this field are mostly based on different measurable parameters. Electromyography is among the most commonly used signals in analysing MF. The main purpose of this paper is to analyse MF during isometric contractions. For this purpose Discrete Wavelet Transform (DWT) is used to divide each signal to get sub-band frequencies. Approximate Entropy (ApEn) is applied to each sub-band. In the next step, each band is segmented into three sections. Finally, a comparison between the first segment and last segment is performed to evaluate MF.

Keywords: Muscle Fatigue, Electromyography, Discrete Wavelet Transform, Approximate Entropy
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doi: 10.23918/eajse.v4i4p28


Ahmad, S. A., & Chappell, P. H. (2008). Moving approximate entropy applied to surface electromyographic signals. Biomed. Sig. Process. Contr., 3, 88–93.

Al Zaman, A. Sharmin, T., & Khan, A. (20070. Muscle fatigue analysis in young adults at different MVC levels using EMG metrics”, Southeast Con IEEE Proceedings, pp. 390-400.

Al-Mulla, M.R. Sepulveda, F., & Colley, M. (2011). An Autonomous Wearable System for Predicting and Detecting Localised Muscle Fatigue. Sensors, University of Essex, UK.

Bogdanis, G. C. (2012). Effects of physical activity and inactivity on muscle. Frontiers in Physiology, 18(3), 142.

Buranachai, C. Thanvarungkul, P. Kanatharanaa, P., & Meglinski, I. (2009). Application of wavelet analysis in optical coherence tomography for obscured pattern recognition. Laser Phys. Lett, 6(12), 892-895.

Caldirola, D. Bellodi, L. Caumo, A. Migliarese, G., & Perna, G. (2004). Approximate entropy of respiratory patterns in panic disorder. Am. J. Psychiatry, 161, 79–87.

Chaffin, D. B. Andersson, G. B. J., & Martin, B. J. (1999). Occupational Biomechanics. Wiley-Interscience.

Enoka, R.M., & Duchateau, J. (2008). Muscle fatigue: what, why and how it influences muscle function. The Journal of physiology, 11-23.

Gandevia, S.C. (1992).  Some central and peripheral factors affecting human motoneuronal output in neuromuscular fatigue. Sports Medicine, 13, 93-98.

James P. K. (1997). Research design in occupational education. Oklahama State University.

Janardan, M., & Babu, K. (2011). An efficient architecture for 3-D lifting-based discrete wavelet transform. M Janardan et al, Int. J. Comp. Tech. Appl., 2(5), 1439-1458.

Jones, J.R. Huxtable, C.S., & Hodgson, J.T. (2006). Self-reported work-related illness: Results from the labour force survey. Health and Safety Executive, National Statistics.

Jonsson B. (1988). The static load component in muscle work. European Journal of Applied Physiology and Occupational Physiology, 57, 305-310.

Kleissen, R. F. Buurke, J. H., & Harlaar, J. Zilvold, G. (1998). Electromyography in the biomechanical analysis of human movement and its clinical application. Gait Posture, 8, 143–158.

Kogi, K., & Hakamada, T. (1962). Frequency analysis of the surface electromyogram in muscle fatigue. J. Sci, Tokyo.

Mader, S.S., & Galliart, P. (2005). Understanding human anatomy and physiology. McGraw-Hill Higher Education.

Nieminen, H., & Takala E. (1996). Evidence of deterministic chaos in the myoelectric signal. Medical Engineering Laboratory, 36, 1, 49-58.

Okkesim, S., & Coskun, K. (2014). Analysis of muscle fatigue during isometric contraction using surface EMG signals. M.S Thesis, Fatih University, Turkey.

Pah, N.D., Kumar, D.K., & Burton, P. (2014). Adding wavelet decomposition to neural networks for the classification of fatigue SEMG. School of Electrical and Computer System Engineering, RMIT University, Melbourne 3000, Australia.

Pincus, S. M. (1991). Approximate entropy as a measure of system complexity.  Proceedings of the National Academy of Sciences, 88, 6, 2297-2301.

Raez, M.B.I., Hussain, M.S., & Yasin., F. (2006). Techniques of EMG signal analysis: Detection, processing, classification and applications. Biol Proced Online.

Soo, Y. Sugi, M., Arai, T., Kato, R., Ota, J. (2009). Quantitative estimation of muscle fatigue using surface electromyography during static muscle contraction. Engineering in Medicine and Biology Society, EMBC.

Tarata, M.T., (2003). Mechanomyography versus electromyography, in monitoring the muscular fatigue. Biomed Eng Online, 2(3).

Vedsted, P. (2006). Biofeedback and optimization of muscle contraction mode as intervention strategy in the prevention of work-related musculoskeletal disorders. Ph.D. Thesis, University College of Northern Denmark, Aalborg, Denmark.

Webber, C.L., Schmidt, M.A., & Walsh, J. M. (1995). Influence of isometric loading on biceps EMG dynamics as assessed by linear and non-linear tools. Journal of Applied Physiology, 78, 3, 814–822.

Xie, H. B. Guo, J.Y., & Zheng Y. P. (2010). Fuzzy approximate entropy analysis of chaotic and natural complex systems: Detecting muscle fatigue using electromyography signals. Annals of Biomedical Engineering, 38, 4, 1483-1496.