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CLC number: TN911.72; R318.04

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Received: 2006-10-11

Revision Accepted: 2006-12-18

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.6 P.910~915

http://doi.org/10.1631/jzus.2007.A0910


Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions


Author(s):  WANG Gang, REN Xiao-mei, LI Lei, WANG Zhi-zhong

Affiliation(s):  Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):   wgnick@gmail.com, zzwang@sjtu.edu.cn

Key Words:  Muscle fatigue, Surface electromyographic (SEMG) signals, Multifractal, Static contraction


WANG Gang, REN Xiao-mei, LI Lei, WANG Zhi-zhong. Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions[J]. Journal of Zhejiang University Science A, 2007, 8(6): 910~915.

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author="WANG Gang, REN Xiao-mei, LI Lei, WANG Zhi-zhong",
journal="Journal of Zhejiang University Science A",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0910"
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0910

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T1 - Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions
A1 - WANG Gang
A1 - REN Xiao-mei
A1 - LI Lei
A1 - WANG Zhi-zhong
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 6
SP - 910
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%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A0910


Abstract: 
This study is aimed at assessing muscle fatigue during a static contraction using multifractal analysis and found that the surface electromyographic (SEMG) signals characterized multifractality during a static contraction. By applying the method of direct determination of the f(α) singularity spectrum, the area of the multifractal spectrum of the SEMG signals was computed. The results showed that the spectrum area significantly increased during muscle fatigue. Therefore the area could be used as an assessor of muscle fatigue. Compared with the median frequency (MDF)―the most popular indicator of muscle fatigue, the spectrum area presented here showed higher sensitivity during a static contraction. So the singularity spectrum area is considered to be a more effective indicator than the MDF for estimating muscle fatigue.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Abel, E.W., Zacharia, P.C., Forster, A., Farrow, T.L., 1996. Neural network analysis of the EMG interference pattern. Med. Eng. Phys., 18(1):12-17.

[2] Castillo-Valdivieso, P.A., Merelo, J.J., Prieto, A., Rojas, I., Romero, G., 2002. Statistical analysis of the parameters of a neuro-genetic algorithm. IEEE Trans. on Neural Networks, 13(6):1374-1394.

[3] Chhabra, A., Jensen, R.V., 1989. Direct determination of the f(α) singularity spectrum. Phys. Rev. Lett., 62(12):1327-1330.

[4] Chhabra, A.B., Meneveau, C., Jensen, R.V., Sreenivasan, K.R., 1989. Direct determination of the f(α) singularity spectrum and its application to fully developed turbulence. Phys. Rev. A, 40(9):5284-5294.

[5] Cuevas, E., 2003. f(α) multifractal spectrum at strong and weak disorder. Phys. Rev. B, 68(2):024206.

[6] Deangelis, G.C., Gilmore, L.D., de Luca, C.J., 1990. Standardized evaluation of techniques for measuring the spectral compression of the myoelectric signal. IEEE Trans. on Biomed. Eng., 37(9):844-849.

[7] de Luca, C.J., 1979. Physiology and mathematics of myoelectric signal. IEEE Trans. on Biomed. Eng., 26:313-325.

[8] Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M., 1999. Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys., 21:431-438.

[9] Frigo, C., Ferrarin, M., Frasson, W., Pavan, E., Thorsen, R., 2000. EMG signals detection and processing for on-line control of functional electrical stimulation. J. Electrom. Kinesiol., 10(5):351-360.

[10] Gandevia, S.C., 2001. Spinal and supraspinal factors in human muscle fatigue. Physiol. Rev., 81(4):1725-1789.

[11] Garland, S.J., Enoka, R.M., Serrano, L.P., Robinson, G.A., 1994. Behavior of motor units in human biceps brachii during a submaximal fatiguing contraction. J. Appl. Physiol., 76(6):2411-2419.

[12] Georgakis, A., Stergioulas, L.K., Giakas, G., 2003. Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency. IEEE Trans. on Biomed. Eng., 50(2):262-265.

[13] Gitter, J.A., Czerniecki, M.J., 1995. Fractal analysis of the electromyographic interference pattern. J. Neurosci. Methods, 58(1-2):103-108.

[14] Gupta, V., Suryanarayanan, S., Reddy, N.P., 1997. Fractal analysis of surface EMG signals from the biceps. Int. J. Med. Inf., 45(3):185-192.

[15] Halsey, T.C., Jensen, M.H., Kadanoff, L.P., Procaccia, I., Shraiman, B.I., 1986. Fractal measures and their singularities: the characterization of strange sets. Phys. Rev. A, 33(2):1141-1151.

[16] Hu, X., Wang, Z.Z., Ren, X.M., 2005. Classification of surface EMG signal with fractal dimension. J. Zhejiang Univ. Sci., 6B(8):844-848.

[17] Hudgins, B., Parker, P., Scott, R.N., 1993. A new strategy for multifunction myoelectric control. IEEE Trans. on Biomed. Eng., 40(1):82-94.

[18] Inbar, G.F., Allin, J., Paiss, O., Kranz, H., 1986. Monitoring surface EMG spectral changes by the zero crossing rate. Med. Biol. Eng. Comput., 24(1):10-18.

[19] Ivanov, P.C., Amaral, L.A.N., Goldberger, A.L., Havlin, S., Rosenblum, M.G., Struzik, Z.R., Stanley, H.E., 1999. Multifractality in human heartbeat dynamics. Nature, 399(6735):461-465.

[20] Jensen, M.H., Kadanoff, L.P., Procaccia, I., 1987. Scaling structure and thermodynamics of strange sets. Phys. Rev. A, 36(3):1409-1420.

[21] Karlsson, S., Yu, J., Akay, M., 1999. Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods. IEEE Trans. on Biomed. Eng., 46(6):670-684.

[22] Kim, J.Y., Jung, M.C., Haight, J.M., 2005. The sensitivity of autoregressive model coefficient in quantification of trunk muscle fatigue during a sustained isometric contraction. Int. J. Ind. Ergon., 35(4):321-330.

[23] Lindstrom, L., Kadefors, R., Petersen, I., 1977. An electromyographic index for localized muscle fatigue. J. Appl. Physiol. Resp. Environ. Exerc. Physiol., 43(4):750-754.

[24] Masuda, K., Masuda, T., Sadoyama, T., Mitsuharu, I., Katsuta, S., 1999. Changes in surface EMG parameters during static and dynamic fatiguing contractions. J. Electrom. Kinesiol., 9:39-46.

[25] Merletti, R., Lo Conte, L.R., 1997. Surface EMG signal processing during isometric contractions. J. Electrom. Kinesiol., 7(4):241-250.

[26] Muthuswamy, J., Thakor, N.V., 1998. Spectral analysis methods for neurological signals. J. Neurosci. Methods, 83(1):1-14.

[27] Ravier, P., Buttelli, O., Jennane, R., Couratier, P., 2005. An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions. J. Electrom. Kinesiol., 15(2):210-221.

[28] Sbriccoli, P., Felici, F., Rosponi, A., Aliotta, A., Castellano, V., Mazza, C., Bernardi, M., Marchetti, M., 2001. Exercise induced muscle damage and recovery assessed by means of linear and non-linear SEMG analysis and ultrasonography. J. Electrom. Kinesiol., 11(2):73-83.

[29] Sparto, P.J., Parnianpour, M., Barria, E.A., Jagadeesh, J.M., 2000. Wavelet and short-time Fourier transform analysis of electromyography for detection of back muscle fatigue. IEEE Trans. on Rehabil. Eng., 8(3):433-436.

[30] Stulen, F.B., de Luca, C.J., 1981. Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans. on Biomed. Eng., 28(7):515-523.

[31] Wang, J., Ning, X., Chen, Y., 2003a. Multifractal analysis of electronic cardiogram taken from healthy and unhealthy adult subjects. Physica A, 323:561-568.

[32] Wang, J., Ning, X., Chen, Y., 2003b. Modulation of heart disease information to the 12-lead ECG multifractal distribution. Physica A, 325(3-4):485-491.

[33] Wang, J., Ning, X., Ma, Q., Bian, C., Xu, Y., Chen, Y., 2005. Multiscale multifractality analysis of a 12-lead electrocardiogram. Phys. Rev. E, 71(6):062902.

[34] Webber, C.L.Jr, Schmidt, M.A., Walsh, J.M., 1995. Influence of isometric loading on biceps EMG dynamics as assessed by linear and nonlinear tools. J. Appl. Physiol., 78(3):814-822.

[35] Xie, H., Wang, Z., 2006. Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis. Computer Methods & Programs in Biomed., 82(2):114-120.

[36] Yamaguti, M., Prado, C.P.C., 1995. A direct calculation of the spectrum of singularities f(α) of multifractals. Phys. Lett. A, 206(5-6):318-322.

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