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CLC number: R318.04
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Received: 2005-01-04
Revision Accepted: 2005-05-30
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Cited: 28
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Classification of surface EMG signal with fractal dimension
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HU Xiao, WANG Zhi-zhong, REN Xiao-mei. Classification of surface EMG signal with fractal dimension[J]. Journal of Zhejiang University Science B, 2005, 6(8): 844-848.
@article{title="Classification of surface EMG signal with fractal dimension",
author="HU Xiao, WANG Zhi-zhong, REN Xiao-mei",
journal="Journal of Zhejiang University Science B",
volume="6",
number="8",
pages="844-848",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.B0844"
}
%0 Journal Article
%T Classification of surface EMG signal with fractal dimension
%A HU Xiao
%A WANG Zhi-zhong
%A REN Xiao-mei
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 8
%P 844-848
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0844
TY - JOUR
T1 - Classification of surface EMG signal with fractal dimension
A1 - HU Xiao
A1 - WANG Zhi-zhong
A1 - REN Xiao-mei
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 8
SP - 844
EP - 848
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B0844
Abstract: Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.
[1] Buczkowski, S., Hildgen, P., Cartilier, L., 1998. Measurements of fractal dimension by box-counting: A critical analysis of data scatter. Physica A, 252:23-34.
[2] Chang, G.C., Kang, W.J., Luh, J.J., Cheng, C.K., Lai, J.S., Chen, J.J.J., Kuo, T.S., 1996. Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface. Med. Eng. Phys., 18(7):529-537.
[3] Eke, A., Herman, P., Kocsis, L., Kozak, L.R., 2002. Fractal characterization of complexity in temporal physiological signals. Physiol. Meas., 23:R1-R38.
[4] 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.
[5] Grassberger, P., Procaccia, I., 1983. Measuring the strangeness of strange attractors. Physica D, 9:189-208.
[6] Gupta, V., Suryanarayanan, S., Reddy, N.P., 1997. Fractal analysis of surface EMG signals from the biceps. International Journal of Medical Informatics, 45:185-192.
[7] Hu, X., Wang, Z.Z., Ren, X.M., 2005. Classification of surface EMG signal using relative wavelet packet energy. Computer Methods and Programs in Biomedicine (in press).
[8] Hudgins, B., Parker, P., Scott, R.N., 1993. A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng., 40(1):82-94.
[9] Kang, W.J., Cheng, C.K., Lai, J.S., Shiu, J.R., Kuo, T.S., 1996. A comparative analysis of various EMG pattern recognition methods. Med. Eng. Phys., 18(5):390-395.
[10] Kim, H.S., Eykholt, R., Salas, J.D., 1999. Nonlinear dynamics, delay times, and embedding windows. Physica D, 127:48-60.
[11] Lei, M., Wang, Z.Z., Feng, Z.J., 2001. Detecting nonlinearity of action surface EMG signal. Physics Letters A, 290:297-303.
[12] Parker, T.S., Chua, L.O., 1989. Practical Numerical Algorithms for Chaotic Systems. Springer-Verlag, New York, p:193-194.
[13] Sarkar, M., Leong, T.Y., 2003. Characterization of medical time series using fuzzy similarity-based fractal dimensions. Artificial Intelligence in Medicine, 27:201-222.
[14] Xu, Z.Q., Xiao, S.J., 2000. Digital filter design for peak detection of surface EMG. Journal of Electromyography and Kinesiology, 10:275-281.
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