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Propagation velocity measurement: Autocorrelation technique applied to the electromyogram

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Abstract

Muscle fibre conduction velocity is an important measurement in electrophysiology, both in the research laboratory and in clinical practice. It is usually measured by placing electrodes spaced at known distances and estimating the transit time of the action potential. The problem, common to all methods, is the estimation of this time delay. Several measurement procedures, in the time and frequency domains, have been proposed. Time-domain strategies usually require two acquisition channels, whereas some frequency-domain methods can be implemented using a single one. The method described operates in the time domain, making use of the autocorrelation function of the difference signal obtained from two needle electrodes and only one acquisition channel. Experimental results were obtained from the electromyogram of two biceps muscles (two adult male subjects, nine records each) under voluntary contraction, yielding an average of 3.58 m s−1 (SD=0.04 m s−1) and 3.37 m s−1 (SD=0.03 m s−1), respectively. Several tests showed that the proposed method works properly with electromyogram records as short as 0.3 s.

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References

  • Erlanger, J. andGasser, H. S. (1937): ‘Electrical signs of nervous activity’ (University of Pennsylvania Press, Philadelphia, USA, 1937)

    Google Scholar 

  • Geddes, L. A., andHoff, H. E. (1968): ‘Who was the first biomedical engineer?’,Biomed. Eng.,3, pp. 551–558

    Google Scholar 

  • Harba, M. I., andTeng, L. Y. (1999): ‘Reliability of measurement of muscle fiber conduction velocity using surface EMG’,Frontiers Med. Biol. Eng.,9, pp. 31–47

    Google Scholar 

  • Lindström, L., andMagnusson, R. (1977): ‘Interpretation of myoelectric power spectra: A model and its applications’,Proc. IEEE,65, pp. 653–662

    Google Scholar 

  • Parker, P., andScott, R. (1973): ‘Statistics of the myoelectric signal from monopolar and bipolar electrodes’,Med. Biol. Eng.,11, pp. 591–596

    Google Scholar 

  • Parker, P., Stuller, J., andScott, R. (1977): ‘Signal processing for the multistate myoelectric channel’,Proc. IEEE,65, pp. 662–674

    Google Scholar 

  • Pattichis, C. S., Schofield, I., Merletti, R., Parker, P. A., andMiddleton, L. T. (Eds) (1999): ‘Intelligent data in electromyography and electroneurology’,Med. Eng. Phys.,21, (special issue), pp. 379–523

    Google Scholar 

  • Schwartz, M., andShaw, L. (1975): ‘Signal processing: discrete spectral analysis, detection and estimation’ (McGraw-Hill, Tokyo, 1975), pp. 148–215

    Google Scholar 

  • Vicar, G., andParker, P. (1988): ‘Spectrum dip estimator of nerve conduction velocity’,IEEE Trans. Biomed. Eng.,35, pp. 1069–1076

    Google Scholar 

  • Zorn, H., andNaeije, H. (1983): ‘On-line muscle fibre action potential conduction velocity measurement using the surface EMG cross-correlation technique’,Med. Biol. Eng. Comput.,21, pp. 239–240

    Google Scholar 

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Correspondence to M. E. Valentinuzzi.

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Spinelli, E., Felice, C.J., Mayosky, M. et al. Propagation velocity measurement: Autocorrelation technique applied to the electromyogram. Med. Biol. Eng. Comput. 39, 590–593 (2001). https://doi.org/10.1007/BF02345151

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  • DOI: https://doi.org/10.1007/BF02345151

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