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 non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over the time, which induces time varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In the present paper, the problem of time varying delay (TVD) estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a stochastic optimization technique, called simulated annealing (SA). The Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the Crammer-Rao Lower Bound (CRLB). We also compared the performance of the proposed method with non-parametric approaches. The results indicate that the proposed method outperform the non-parametric one.
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References
Allen, D.C., Arunachalam, R., Mills, K.R.: Critical illness myopathy: further evidence from muscle-fiber excitability studies of an acquired channelopathy. Muscle Nerve 37(1), 14–22 (2008)
Athier, G., Floquet, P., Pibouleau, L., Domenech, S.: Synthesis of heat-exchanger network by simulated annealing and NLP procedures. AIChE J. 43(11), 3007–3020 (1997)
Chan, Y., Riley, J., Plant, J.: Modeling of time delay and its application to estimation of nonstationary delays. IEEE Trans. Acoust. Speech Signal Process. 29(3), 577–581 (1981)
Dréo, J., Siarry, P.: Métaheuristiques d’optimisation vues sous l’angle de l’échantillonnage de distribution. J. Européen des Systèmes Automatisés 42(1), 9 (2008)
Farina, D., Merletti, R.: Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions. J. Electromyogr. Kinesiol. 10(5), 337–349 (2000)
Farina, D., Pozzo, M., Merlo, E., Bottin, A., Merletti, R.: Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions. IEEE Trans. Biomed. Eng. 51(8), 1383–1393 (2004)
Gerdle, B., Ostlund, N., Grnlund, C., Roeleveld, K., Karlsson, J.S.: Firing rate and conduction velocity of single motor units in the trapezius muscle in fibromyalgia patients and healthy controls. J. Electromyogr. Kinesiol. 18(5), 707–716 (2008)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Leclerc, F., Ravier, P., Buttelli, O., Jouanin, J.C.: Comparison of three time-varying delay estimators with application to electromyography. In: 2007 15th European Signal Processing Conference, pp. 2499–2503, September 2007
Leclerc, F., Ravier, P., Farina, D., Jouanin, J.C., Buttelli, O.: Time-varying delay estimation with application to electromyography. In: 2008 16th European Signal Processing Conference, pp. 1–5, August 2008
Luu, G.T.: Contributions à lestimation de la vitesse de conduction des signaux électromyographiques. Ph.D. dissertation, Orleans University (2013)
Luu, G.-T., Ravier, P., Buttelli, O.: 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. 1–5 (2012)
Merletti, R., Knaflitz, M., De Luca, C.J.: Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. J. Appl. Physiol. 69(5), 1810–1820 (1990). http://jap.physiology.org/content/69/5/1810
Merletti, R., Conte, L.R.L.: Surface EMG signal processing during isometric contractions. J. Electromyogr. Kinesiol. 7(4), 241–250 (1997)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)
Pibouleau, L., Domenech, S., Davin, A., Azzaro-Pantel, C.: Expérimentations numériques sur les variantes et paramètres de la méthode du recuit simulé. Chem. Eng. J. 105(3), 117–130 (2005)
Ravier, P., Farina, D., Buttelli, O.: Time-varying delay estimators for measuring muscle fiber conduction velocity from the surface electromyogram. Biomed. Signal Process. Control 22, 126–134 (2015)
Shwedyk, E., Balasubramanian, R., Scott, R.N.: A nonstationary model for the electromyogram. IEEE Trans. Biomed. Eng. BME–24(5), 417–424 (1977)
Yaar, I., Niles, L.: Muscle fiber conduction velocity and mean power spectrum frequency in neuromuscular disorders and in fatigue. Muscle Nerve 15(7), 780–787 (1992). https://doi.org/10.1002/mus.880150706
Acknowledgment
The authors would like to thank the Posts and Telecommunications Institute of Technology, Ho Chi Minh City Branch for funding this research in project 03-HV-2017-RD-CB2.
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Luu, G.T., Boualem, A., Ravier, P., Buttelli, O. (2018). An Effective Time Varying Delay Estimator Applied to Surface Electromyographic Signals. In: Cong Vinh, P., Ha Huy Cuong, N., Vassev, E. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICTCC ICCASA 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-77818-1_21
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