EMG spectral shift as an indicator of fatigability in an heterogeneous muscle group

  • J. Duchêne
  • F. Goubel


Changes in electromyogram (EMG) power spectra were investigated in the triceps surae musclesof two classes of individuals (untrained subjects and athletes) maintaining a plantarflexion torque of 80% of maximal voluntary contraction until exhaustion. A set of 23 parameters describing changes in the frequency content and power of EMG was defined. For most experiments, classical changes were found, indicating a shift of the EMG spectra towards lower frequencies and an increase in the total power of the signals. In 12% of the experiments, alternations in activity between synergistic muscles were found, leading to a large variability in the spectral parameters. After the expression of each experiment in terms of a reduced data matrix and matrix to vector transformations, three methods of discrimination were used to classify subjects with respect to changes in the EMG signal during sustained contraction: (1) evaluation of the most discriminating parameter, (2) principal components analysis, (3) transformation maximizing differences between classes. Method (3) was found to be preferable since it led to good separation of the two classes in a reference group of subjects and a satisfactory projection of each individual from a group of unknowns into the appropriate class. These results suggest using a method such as this for ergonomic or athletic training purposes rather than the usual method of monitoring the frequency shift of the EMG.

Key words:

Myoelectric signals Frequency spectrum Isometric contraction Discriminant analysis Muscle fatigue 


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  1. Bigland-Ritchie B, Donovan EF, Roussos CS (1981) Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts. J Appl Physiol Respir Environ Exerc Physiol 51:1300–1305Google Scholar
  2. Clamann HP, Broecker KT (1979) Relation between force and fatigability of red and pale skeletal muscles in man. Am J Phys Med 58:70–85Google Scholar
  3. DeLuca CJ (1984) Myoelectrical manifestations of localized muscular fatigue in humans. Crit Rev Biomed Eng 11:251–279Google Scholar
  4. Draper NR, Smith H (1981) Applied regression analysis. Wiley, New YorkGoogle Scholar
  5. Duchêne J (1983) Développement de méthodes de decision sur un ensemble de tableaux. Application au traitement d'électromyogrammes lors d'épreuves de fatigue. Thesis,Université de Technologie, CompiégneGoogle Scholar
  6. Duchêne J (1986) A significant plane for two-classes discrimination problems. IEEE PAMI 8:557–560Google Scholar
  7. Foley DH, Sammon JW (1975) An optimal set of discriminant vectors. IEEE Trans Comput C-24:281–289Google Scholar
  8. Gollnick PD, Sjodin B, Karlsson J, Jansson E, Saltin B (1974) Human soleus muscle: a comparison of fiber composition and enzyme activities with other leg muscles. Pflugers Arch 348:247–255Google Scholar
  9. Hagberg M (1981) Muscular endurance and surface electromyogram in isometric and dynamic exercise. J Appl Physiol Respir Environ Exerc Physiol 51:1–7Google Scholar
  10. Hagberg M, Ericson BE (1982) Myoelectric power spectrum dependence on muscular contraction level of elbow flexors. Eur J Appl Physiol 48:147–156Google Scholar
  11. Hagg G (1981) Electromyographic fatigue analysis based on the number of zero crossings. Pflugers Arch 391:78–80Google Scholar
  12. Hof AL, van den Berg JW (1977) Linearity between the weighted sum of the EMGs of the human triceps surae and the total torque. J Biomech 10:529–539Google Scholar
  13. Hujing PA, Adelerhof ASP, Giesbergen R, Woittiez RD, Rijnsburger WH (1986) Triceps surae EMG power spectrum changes during sustained submaximal isometric contractions at different muscle lengths. Electromyogr Clin Neurophysiol 26:181–192Google Scholar
  14. Humphreys PW, Lind AR (1963) The blood flow through active and inactive muscles of the forearm during sustained hanggrip contractions. J Physiol (Lond) 166:120–131Google Scholar
  15. IEEE (1971) Special issue on feature extraction and selection in pattern recognition. IEEE Trans Comput C-20:967–1117Google Scholar
  16. Kadefors R, Petersen I, Herberts P (1976) Muscular reaction to welding work: an electromyographic investigation. Ergonomics 19:543–558Google Scholar
  17. Komi PV, Tesch P (1979) EMG frequency spectrum, muscle structure and fatigue during dynamic contractions in man. Eur J Appl Physiol 42:41–50Google Scholar
  18. Lago P, Jones NB (1977) Effect of motor-unit firing time statistics on EMG spectra. Med Biol Eng Comput 15:648–655Google Scholar
  19. Lindstrom L, Magnusson R, Petersen I (1974) Muscle load influence on myoelectric signal characteristics. Scand J Rehabil Med [Suppl] 3:127–148Google Scholar
  20. Lindstrom L, Kadefors R, Petersen I (1977) An electromyographic index for localized muscle fatigue. J Appl Physiol Respir Environ Exerc Physiol 43:750–754Google Scholar
  21. Lippold OCJ, Redfearn JWT, Vuco J (1960) The electromyography of fatigue. Ergonomics 3:121–131Google Scholar
  22. Moritani T, Muro M, Nagata A (1986) Intransmucular and surface electromyogram changes during muscle fatigue. J Appl Physiol 60:1179–1185Google Scholar
  23. Mortimer JT, Magnusson R, Petersen I (1970) Conduction velocity in ischemic muscle: effect on EMG frequency spectrum. Am J Physiol 219:1324–1329Google Scholar
  24. Petrofsky JS, Glaser RM, Phillips CA (1982) Evaluation of the amplitude and frequency components of the surface EMG as an index of muscle fatigue. Ergonomics 25:213–223Google Scholar
  25. Roussos C, Fixley M, Gross D, Macklem PT (1979) Fatigue of inspiratory muscles and their synergic behavior. J Appl Physiol Respir Environ Exerc Physiol 46:897–904Google Scholar
  26. Spath H (1980) Cluster analysis algorithms. Wiley, New YorkGoogle Scholar
  27. Stulen FB, de Luca CJ (1981) Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans Biomed Eng 28:515–523Google Scholar
  28. van Boxtel A, Schomaker LRB (1984) Influence of motor unit firing statistics on the median frequency of the EMG power spectrum. Eur J Appl Physiol 52:207–213Google Scholar
  29. van Boxtel A, Goudswaard P, Vander Molen GM, Vandenbosch WEJ (1983) Changes in electromyogram power spectra of facial and jaw-elevator muscles during fatigue. J Appl Physiol 54:51–58Google Scholar
  30. Viitasalo JHT, Komi PV (1977) Signal characteristics of EMG during fatigue. Eur J Appl Physiol 37:111–121Google Scholar

Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • J. Duchêne
    • 1
  • F. Goubel
    • 1
  1. 1.Département de Genie BiologiqueUniversité de TechnologieCompiègne CedexFrance

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