Document Type: Original Articles


1 Department of Physiotherapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Physical Therapy, School of Rehabilitation, Tehran University of Medical Sciences,Tehran, Iran


Background: Surface electromyography (sEMG) of muscles is a non-invasive tool that can be helpful in the assessment of muscle function and some motor control evaluations. A loss of force, known as muscle fatigue is accompanied by changes in muscle electrical activity. One of the most commonly used surface EMG parameters which reflects paraspinal muscle fatigue during different tasks and positions is median frequency. Although it is widely known that the electromyography power spectrum shifts to lower frequencies during fatiguing contraction, an opinion exists that the validity of spectral shifts in assessment of fatigue is questionable. Some researchers have examined whether other quantities derived from sEMG signals are better indicators for muscle fatigue. Following cyclic flexion/extension and consequence fatigue, variation in sEMG signals may be complex for study. The aim of this study was to determine which of the median frequency (MF) or entropic (ENTR) is more sensitive for measuring muscular fatigue in erector spinae muscles during cyclic flexion/extension.Methods: Surface electromyography of erector spine muscles was recorded in 25 healthy subjects during cyclic dynamic contractions. The experimental session consisted of two parts: measurement of Maximal Voluntary Contraction (MVC), and performing the fatigue test. All subjects performed rhythmic flexion/extension with 50% MVC loading against B-200 Isostation, about 4-6 minutes. The MF and ENTR of the muscle activities were computed to assess muscular fatigue.Results: Paired sample t-tests showed that MF and ENTR changes after fatigue test were significant (P <0.001). Percentage changes of both MF and ENTR were reduced, this reduction for ENTR was more than 40% (P <0.001).Conclusion: It seems that the changes of ENTR in muscle activities have the ability to measure muscular fatigue and is more sensitive in comparison to MF.


  1. Ritvanen T, Zaproudina N, Nissen M, Leinonen V, Hänninen O. Dynamic surface electromyographic responses in chronic low back pain treated by traditional bone setting and conventional physical therapy. Journal of Manipulative and Physiological Therapeutics. 2007;30(1):31-7.
  2. Ettema GJ, Taylor E, North JD, Kippers V. Muscle synergies at the elbow in static and oscillating isometric torque tasks with dual degrees of freedom. Motor control. 2005;9(1):59-74.
  3. Farina D, Leclerc F, Arendt-Nielsen L, Buttelli O, Madeleine P. The change in spatial distribution of upper trapezius muscle activity is correlated to contraction duration. Journal of Electromyography and Kinesiology. 2008;18(1):16-25.
  4. Cashaback JG, Cluff T, Potvin JR. Muscle fatigue and contraction intensity modulates the complexity of surface electromyography. Journal of Electromyography and Kinesiology. 2013;23(1):78-83.
  5. Dimitrova N, Hogrel J-Y, Arabadzhiev T, Dimitrov G. Estimate of M-wave changes in human biceps brachii during continuous stimulation. Journal of Electromyography and Kinesiology. 2005;15(4):341-8.
  6. Ravier P, Buttelli O, Jennane R, Couratier P. An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions. Journal of Electromyography and Kinesiology. 2005;15(2):210-21.
  7. Sung PS, Zurcher U, Kaufman M. Reliability difference between spectral and entropic measures of erector spinae muscle fatigability. Journal of Electromyography and Kinesiology. 2010;20(1):25-30.
  8. De Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. Critical reviews in biomedical engineering. 1983;11(4):251-79.
  9. Beck TW, Stock MS, Defreitas JM. Shifts in EMG spectral power during fatiguing dynamic contractions. Muscle & Nerve. 2014;50(1):95-102.
  10. Dimitrova N, Arabadzhiev T, Hogrel J-Y, Dimitrov G. Fatigue analysis of interference EMG signals obtained from biceps brachii during isometric voluntary contraction at various force levels. Journal of Electromyography and Kinesiology. 2009;19(2):252-8.
  11. Slack PS, Ma X, editors. Determination of muscle fatigue using dynamically embedded signals. From: ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; American Society of Mechanical Engineers.2007;5:1565-1574.
  12. Coorevits P, Danneels L, Cambier D, Ramon H, Druyts H, Karlsson JS, et al. Correlations between short-time fourier-and continuous wavelet transforms in the analysis of localized back and hip muscle fatigue during isometric contractions. Journal of Electromyography and Kinesiology. 2008;18(4):637-44.
  13. Talebinejad M, Chan AD, Miri A, Dansereau RM. Fractal analysis of surface electromyography signals: a novel power spectrum-based method. Journal of Electromyography and Kinesiology. 2009;19(5):840-50.
  14. Kouzaki M, Fukunaga T. Frequency features of mechanomyographic signals of human soleus muscle during quiet standing. Journal of Neuroscience Methods. 2008;173(2):241-8.
  15. Süüden E, Ereline J, Gapeyeva H, Pääsuke M. Low back muscle fatigue during Sorensen endurance test in patients with chronic low back pain: relationship between electromyographic spectral compression and anthropometric characteristics. Electromyography and Clinical Neurophysiology. 2007;48(3-4):185-92.
  16. Dimitrov GV, Arabadzhiev TI, Mileva KN, Bowtell JL, Crichton N, Dimitrova NA. Muscle fatigue during dynamic contractions assessed by new spectral indices. Medicine and Science in sports and exercise. 2006;38(11):1971.
  17. Webber C, Schmidt M, Walsh J. Influence of isometric loading on biceps EMG dynamics as assessed by linear and nonlinear tools. Journal of Applied Physiology. 1995;78(3):814-22.
  18. Talebinejad M, Chan AD, Miri A. Multiplicative multi-fractal modeling of electromyography signals for discerning neuropathic conditions. Journal of Electromyography and Kinesiology. 2010;20(6):1244-8.
  19. Eckmann J-P, Kamphorst SO, Ruelle D. Recurrence plots of dynamical systems. Europhys Lett. 1987;4(9):973-7.
  20. Sung PS, Zurcher U, Kaufman M. Comparison of spectral and entropic measures for surface electromyography time series: a pilot study. Journal of Rehabilitation Research and Development. 2007;44(4):599.
  21. Costa M, Goldberger AL, Peng C-K. Multiscale entropy analysis of biological signals. Physical review E. 2005;71(2):021906.
  22. Allen PA, Murphy MD, Kaufman M, Groth KE, Begovic A. Age differences in central (semantic) and peripheral processing: The importance of considering both response times and errors. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2004;59(5):P210-P9.
  23. Caplin A, Dean M. Behavioral implications of rational inattention with shannon entropy. National Bureau of Economic Research, 2013.