Advertisement

Time Varying Delay Estimators for Measuring Muscle Fiber Conduction Veloity: Effects of Non-stationarity of the Data

  • Gia-Thien Luu
  • Trung Duy Tran
  • Hanh Tan
  • Thanh Tung Ngo
  • Philippe Ravier
  • Olivier Buttelli
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 63)

Abstract

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the nonstationarity during the dynamic contraction (the most daily life situation) of the data, the proposed methods have to consider that the MFCV changes over the time, which induces non-constant time delays. In this study, the effect of the nonstationarity (change of Power Spectral Density) of the sEMG signals on the performance of the time-varying delay estimators recently developed by our group is investigated. This study presents a set of approaches for instantaneous delay estimation from two-channels EMG signals. The performance of the estimators is evaluated and compared through Monte-Carlo simulations in order to determine if their performance statistics are sufficient for practical applications.

Keywords

Electromyography Time-varying delay estimators Muscle fiber conduction velocity Non-stationarity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allen DC, Arunachalam R, Mills KR (2008) Critical illness myopathy: further evidence from muscle-fiber excitability studies of an acquired channelopathy. Muscle Nerve 37:14–22Google Scholar
  2. 2.
    Bjrn G, Nils O, Christer G, Karin R, Stefan KJ (2008) Firing rate and conduction velocity of single motor units in the trapezius muscle in fibromyalgia patients and healthy controls. J Electromyogr Kinesiol 18:707–716Google Scholar
  3. 3.
    Merletti R, Lo Conte LR (1997) Surface EMG signal processing during isometric contractions. J Electromyogr Kinesiol 7:241–250CrossRefGoogle Scholar
  4. 4.
    Tellakula AK (2007) Acoustic source localization using time delay estimation. Degree thesis, Supercomputer Education and Research Centre Indian Institute of Science, Bangalore, IndiaGoogle Scholar
  5. 5.
    Clifford Carter G (1981) Time delay estimation for passive sonar signal processing 29:463–470Google Scholar
  6. 6.
    Carter GC, Robinson ER (1993) Ocean effects on time delay estimation requiring adaptation 18:367–378Google Scholar
  7. 7.
    Farina D, Pozzo M, Merlo E, Bottin A, Merletti R (2004) Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions 51:1383–1393Google Scholar
  8. 8.
    Leclerc F, Ravier P, Buttelli O, Jouanin J-C (2007) Comparison of three time-varying delay estimators with application to electromyography. In: Proceeding of EUSIPCOGoogle Scholar
  9. 9.
    Leclerc F, Ravier P, Farina D, Jouanin J-C, Buttelli O (2008) Time-varying delay estimation with application to electromyography. In: Proceeding of EUSIPCOGoogle Scholar
  10. 10.
    Luu G-T, Ravier P, Buttelli O (2012) Comparison of maximum likelihood and time frequency approaches for time varying delay estimation in the case of electromyography signals. In: Biomedical engineering international conference (BMEiCON), pp 1–5Google Scholar
  11. 11.
    Luu G-T, Ravier P, Buttelli O (2014) The non-parametric approach for time-varying delay estimation with application to the electromyographics signals. In: Proceedings of international conference on green and human information technology (ICGHIT), Ho Chi Minh city, VietNamGoogle Scholar
  12. 12.
    Abdelbassit B, Meryem J, Philippe R, Olivier B (2015) Legendre polynomial modeling of time-varying delay applied to surface EMG signalsDerivation of the appropriate time-dependent CRBs. Sig Process 114:34–44Google Scholar
  13. 13.
    Philippe R, Dario F, Olivier B (2015) Time-varying delay estimators for measuring muscle fiber conduction velocity from the surface electromyogram. Biomed sig process control 22:126–134Google Scholar
  14. 14.
    Nishizono H, Saito Y, Miyashita M (1979) The estimation of conduction velocity in human skeletal muscle in situ with surface electrodes. Electroencephalogr Clin Neurophysiol 46:659–664CrossRefGoogle Scholar
  15. 15.
    Naeije M, Zorn H (1983) Estimation of the action potential conduction velocity in human skeletal muscle using the surface EMG crosscorrelation technique. Electromyogr Clin Neurophysiol 23:73–80Google Scholar
  16. 16.
    Yaar I, Shapiro MB, Mitz AR, Pottala EW (1984) A new technique for measuring muscle fiber conduction velocities in full interference patterns. Electroencephalogr Clin Neurophysiol 57:427–434CrossRefGoogle Scholar
  17. 17.
    Zwarts MJ, van Weerden TW, Links TP, Haenen HT, Oosterhuis HJ (1988) The muscle fiber conduction velocity and power spectra in familial hypokalemic periodic paralysis. Muscle Nerve 11:166–173CrossRefGoogle Scholar
  18. 18.
    Matsunaga S, Sadoyama T, Onomitu S, Masuda T, Katsuta S (1993) Muscle fiber conduction velocity in right and left biceps brachii for badminton players. Ann Physiol Anthropol 12:251–257CrossRefGoogle Scholar
  19. 19.
    Arabadzhiev TI, Dimitrov GV, Dimitrova NA (2004) The crosscorrelation and phase-difference methods are not equivalent under non- invasive estimation of the motor unit propagation velocity. J Elec tromyogr Kinesiol 14:295–305CrossRefGoogle Scholar
  20. 20.
    Lars Arendt-Nielsen, Forster A, Mills KR (1984) The relationship between muscle-fibre conduction velocity and force in the human vastus lateralis. J Physiol 353:6PGoogle Scholar
  21. 21.
    Ravier P, Luu G-T, Jabloun M, Buttelli O (2011) Do the generalized correlation methods improve time delay estimation of the muscle fiber conduction velocity? In: Proceedings of the 4th international symposium on applied sciences in biomedical and communication technologies (ISABEL’11), New York, NY, USA. ACM, 181:1–181:5Google Scholar
  22. 22.
    Masuda K, Masuda T, Sadoyama T, Inaki M, Katsuta S (1999) Changes in surface EMG parameters during static and dynamic fatiguing contractions. J Electromyogr Kinesiol 9:39–46CrossRefGoogle Scholar
  23. 23.
    Lindstrom L, Magnusson R, Petersén I (1970) Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals. Electromyography 10:341–356Google Scholar
  24. 24.
    Shwedyk E, Balasubramanian R, Scott RN (1977) A nonstationary model for the electromyogram. Biomed Eng IEEE Trans BME-24:417–424Google Scholar
  25. 25.
    Farina D, Merletti R (2000) Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions. J Electromyogr Kinesiol 10:337–349CrossRefGoogle Scholar
  26. 26.
    Chan YT, Riley J, Plant JB (1981) Modeling of time delay and its application to estimation of nonstationary delays 29:577–581Google Scholar
  27. 27.
    Knapp C, Carter GC (1976) The generalized correlation method for estimation of time delay 24:320–327Google Scholar
  28. 28.
    Blok E (2002) Classification and evaluation of discrete subsample time delay estimation algorithms. In: 14th international conference on microwaves, radar and wireless communications (MIKON-2002), vol 3, pp 764–767Google Scholar
  29. 29.
    Luu G-T, Ravier P, Buttelli O (2013) Comparison of maximum likelihood and time frequency approaches for time varying delay estimation in the case of electromyography signals. Int J Appl Biomed Eng 6:6–11Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Gia-Thien Luu
    • 1
  • Trung Duy Tran
    • 1
  • Hanh Tan
    • 1
  • Thanh Tung Ngo
    • 2
  • Philippe Ravier
    • 3
  • Olivier Buttelli
    • 3
  1. 1.Posts and Telecommunications Institute of TechnologyHo Chi Minh CityVietnam
  2. 2.Faculty of Telecommunications ProfessionalTelecommunications UniversityNha TrangVietnam
  3. 3.PRISME LaboratoireOrlansFrance

Personalised recommendations