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Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

  • D. Farina
  • W. Muhammad
  • E. Fortunato
  • O. Meste
  • R. Merletti
  • H. Rix
Article

Abstract

This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 ms−1; the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 ms−1 and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

Keywords

Electromyography Linear electrode arrays Beamforming Maximum likelihood estimation Motor unit action potentials Muscle fibre conduction velocity estimation 

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References

  1. Bergamo, R., Gazzoni, M., Farina, D., Lantermo, A., Merletti, R., Sanfilippo, C., andTitolo, M. (1999): ‘Multichannel surface EMG of muscles of the hand and forearm’. Proc. PROCID Symp. Muscular disorders in computer users, Copenhagen, Denmark, pp. 198–202Google Scholar
  2. Bonato, P., Balestra, G., Knaflitz, M., andMerletti, R. (1990): ‘Comparison between muscle fibre conduction velocity estimation techniques: spectral matching versus crosscorrelation’. Proc. 8th Int. ISEK Congress, pp. 19–22Google Scholar
  3. Brody, L., Pollock, M., Roy, S., De Luca, C. J., andCelli, B. (1991): ‘pH induced effects on median frequency and conduction velocity of the myoelectric signal’,J. Appl. Physiol.,71, pp. 1878–1885Google Scholar
  4. Davies, S., andParker, P. (1987): ‘Estimation of myoelectric conduction velocity distribution’,IEEE Trans.,BME-34, pp. 98–105Google Scholar
  5. Farina, D., Merletti, R., andDimanico, U. (1998): ‘Non-invasive identification of motor units with linear electrode arrays’. Proc. XII ISEK Congress, Montreal, pp. 50–51Google Scholar
  6. Farina, D., andRainoldi, A. (1999): ‘Compensation of the effect of subcutaneous tissue layers on surface EMG: a simulation study’,Med. Eng. Phys.,21, pp. 487–496Google Scholar
  7. Farina, D., Fortunato, E., andMerletti, R. (2000): ‘Non invasive estimation of motor unit conduction velocity distribution using linear electrode arrays’,IEEE Trans.,BME-47, pp. 380–388Google Scholar
  8. Farina, D., andMerletti, R. (2000): ‘Comparison of algorithms for estimation of EMG variables estimation during isometric constant force contractions’,J. Electromyogr. Kinesiol.,10, pp. 337–350CrossRefGoogle Scholar
  9. Farina, D., andMerletti, R. (2001): ‘A novel approach for precise simulation of the EMG signal detected by surface electrodes’ to be published inIEEE Trans. Biomed. Eng. Google Scholar
  10. Gazzoni, M., Farina, D., Rainoldi, A., andMerletti, R. (2000): ‘Surface EMG signal decomposition’. Proc. XIII ISEK Congress, Sapporo, pp. 403–404Google Scholar
  11. Hakansson, C. H. (1956): ‘Conduction velocity and amplitude of the action potential as related to circumference in the isolated fiber of frog muscle’,Acta Physiol. Scand.,37, pp. 14–22Google Scholar
  12. Johnson, D. H., andDuolgeon, D. E. (1993): ‘Array signal processing-Concepts and techniques’, inOppenheim, A. V. (Ed): (Prentice Hall Signal Processing Series)Google Scholar
  13. Lo Conte, L., andMerletti, R. (1995): ‘Advances in processing of surface myoelectric signals: Part 2’,Med. Biol. Eng. Comput.,33, pp. 362–372Google Scholar
  14. Masuda, T., Miyano, H., andSadoyama, T. (1985a): ‘The position of innervation zones in the biceps brachii investigated by surface electromyography’,IEEE Trans.,BME-32, pp 36–42Google Scholar
  15. Masuda, T., Miyano, H., andSadoyama, T. (1985b): ‘A surface electrode array for detecting action potential trains of single motor units’,Electroencephalogr. Clin. Neurophysiol.,60, pp. 435–443Google Scholar
  16. Masuda, T., andSadoyama, T. (1986): ‘The propagation of single motor unit action potentials detected by a surface electrode array’,Electroencephalogr. Clin. Neurophysiol.,63, pp. 590–598Google Scholar
  17. McGill, K. C., andDorfman, L. J. (1984): ‘High resolution alignment of sampled waveforms’,IEEE Trans.,BME-31, pp. 462–470Google Scholar
  18. Merletti, R., andDe Luca, C. J. (1989): ‘New techniques in surface electromyography’, inDesmedt, J. E. (Ed.): ‘Computer aided electromyography and expert systems’ (Elsevier Science Publisher)Google Scholar
  19. Merletti, R., Knaflitz, M., andDe Luca, C. J. (1990): ‘Myoelectric manifestations of muscle fatigue during voluntary and electrically elicited contractions’,J. Appl. Physiol.,68, pp. 1657–1667Google Scholar
  20. Merletti, R., andLo Conte, L. (1995): ‘Advances in processing of surface myoelectric signals: Part 1’,Med. Biol. Eng. Comput.,33, pp. 373–384Google Scholar
  21. Merletti, R., Lo Conte, L., Avignone, E., andGuglielminotti, P. (1999a): ‘Modelling of surface EMG signals. Part I: model implementation’,IEEE Trans.,BME-46, pp. 810–820Google Scholar
  22. Merletti, R., Roy, S., Kupa, E., Roatta, S., andGranata, A. (1999b): ‘Modelling of surface EMG signals. Part II: model based interpretation of surface EMG signals’,IEEE Trans.,BME-46, pp. 821–829Google Scholar
  23. Merletti, R., Farina, D., andGranata, A. (1999c): ‘Non-invasive assessment of motor unit properties with linear electrode arrays’ inComi, G.,et al. (Eds): ‘Clinical neurophysiology: from receptors to perception’ (Elsevier Science Publisher)Google Scholar
  24. Merletti, R., Schieroni, M. P., Farina, D., Brero, M., andGazzoni, M. (1999d): ‘Non-invasive assessment of the skeletal musculature in the elderly’. Proc. XI International Congress of EMG and Clinical Neurophysiology, PragueGoogle Scholar
  25. Morimoto, S., andMasuda, T. (1984): ‘Dependence of conduction velocity on spike interval during voluntary muscular contraction in human motor units’,Eur. J. Appl. Physiol.,53, pp. 191–195CrossRefGoogle Scholar
  26. Muhammad, W., Meste, O., andRix, H. (2000): ‘Comparison of single and multiple time delay estimators: application to muscle fiber conduction velocity estimation’. Research report RR-0015, Lab. 135, University of Nice, Sophia Antipolis, FranceGoogle Scholar
  27. Nishizono, H., Kurata, H., andMiyashita, M. (1989): ‘Muscle fiber conduction velocity related to stimulation rate’,Electroenceph. Clin. Neurophysiol.,72, pp. 529–534CrossRefGoogle Scholar
  28. Nishizono, H., Fujimoto, T., Ohtake, H., andMiyashita, M. (1990): ‘Muscle fiber conduction velocity and contractile properties estimated from surface electrode arrays’,Electroenceph. Clin. Neurophysiol.,75, pp. 75–81CrossRefGoogle Scholar
  29. Press, H., Vetterling, T., Teukolsky, S. A., andFlannery, B. P. (1996): ‘Numerical recipes in C′ (Cambridge University Press)Google Scholar
  30. Rainoldi, A., Galardi, G., Maderna, L., Comi, G., Lo Conte, L., andMerletti, R. (1999): ‘Repeatability of surface EMG variables during voluntary isometric contractions of the biceps brachii muscle’,J. Electromyogr. Kinesiol.,9, pp. 105–119CrossRefGoogle Scholar
  31. Rix, H., andMalengé, J. P. (1980): ‘Detecting small variation in shape’,IEEE Trans. Syst. Man. Cybern.,10, pp. 90–96Google Scholar
  32. Rix, H., andMeste, O. (1997): ‘Fine structure of ECG signal using wavelet transform’, inD'Attellis, C. E., andFernandez-Berdaguer, E. M. (Eds): ‘Wavelet theory and harmonic analysis in applied sciences’ (Birkhauser Publisher)Google Scholar
  33. Roy, S. H., De Luca, C. J., andSchneider, J. (1986): ‘Effects of electrode location on myoelectric conduction velocity and median frequency estimates’,J. Appl. Physiol.,61, pp. 1510–1517Google Scholar
  34. Sadoyama, T., Masuda, T., andMiyano, T. (1985): ‘Optimal conditions for the measurement of muscle fiber conduction velocity using surface electrode arrays’,Med. Biol. Eng. Comput.,23, pp. 339–342Google Scholar
  35. Schneider, J., Silny, J., andRau, G. (1991): ‘Influence of tissue inhomogeneities on noninvasive muscle fiber conduction velocity measurements investigated by physical and numerical modeling’,IEEE Trans.,BME-38, pp. 851–860Google Scholar
  36. Stalberg, E., Nandekar, S., Sanders, D., andFalck, B. (1996): ‘Quantitative motor unit action potential analysis’,J. Clin. Neurophysiol.,13, pp. 401–422Google Scholar
  37. Theodoridis, S., andKoutroumbas, K. (1999): ‘Pattern recognition’ (Academic Press)Google Scholar
  38. Troni, W., Cantello, R., andRainero, I. (1983): ‘Conduction velocity along human muscle fibersin situNeurology,33, pp. 1453–1459Google Scholar

Copyright information

© IFMBE 2001

Authors and Affiliations

  • D. Farina
    • 1
    • 2
  • W. Muhammad
    • 3
  • E. Fortunato
    • 3
  • O. Meste
    • 3
  • R. Merletti
    • 1
  • H. Rix
    • 3
  1. 1.Department of ElectronicsPolitecnico di TorinoTorinoItaly
  2. 2.Department d'Automatique et Informatique AppliquéeEcole Centrale de NantesNantesFrance
  3. 3.Laboratoire I3SSophia AntipolisFrance

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