Health  Vol.1 No.2 , September 2009
Pattern recognition of surface electromyography signal based on wavelet coefficient entropy
ABSTRACT
This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.

Cite this paper
nullHu, X. , Gao, Y. and Liu, W. (2009) Pattern recognition of surface electromyography signal based on wavelet coefficient entropy. Health, 1, 121-126. doi: 10.4236/health.2009.12020.
References
[1]   Stokes, I.A.F. (2005) Relationships of EMG to effort in the trunk under isometric conditions force-increasing and decreasing effects and temporal delays. Clinical Biome-chanics, 20, 9-15.

[2]   Wakeling, J.M. (2009) Patterns of motor recruitment can be determined using surface EMG. Journal of Electro-myography and Kinesiology, 19, 199-207.

[3]   Kaplanis, P.A., Pattichis, C.S., Hadjileontiadis, L.J., Roberts, V.C. (2009) Surface EMG analysis on normal subjects based on isometric voluntary contraction. Jour-nal of Electromyography and Kinesiology, 19, 157-171.

[4]   Lei, M., Wang, Z.Z., Feng, Z.J. (2001) Detecting nonlin-earity of action surface EMG signal. Physics Letters A, 290, 297-303.

[5]   Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M. (1999) Classification of the myoelectric signal using time-frequency based representations. Medical Engi-neering & Physics, 21, 431-438.

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

[7]   Stashuk, D. (2001) EMG signal decomposition: how can it be accomplished and used? Journal of Electromyogra-phy and Kinesiolugy, 11, 151-173.

[8]   Hu, X. and Wang, Z.Z. (2004) Detecting the motor unit action potential from surface EMG signals based on wavelet transform, 2004 IEEE International workshop on Biomedical circuits & system, S2. 6-15.

[9]   Farina, D. and Merletti, R. (2001) A novel approach for presice simulation of the EMG signal detected by surface electrodes. IEEE Trans. Biomed. Eng., 48(6), 637-646.

[10]   Merletti, R. and Torino, P.D. (1999) Standards for Re-porting EMG Data. Journal of Electromyography and Kinesiolugy.

[11]   Hu, X., Yu, P., Yu Q., Liu, W.X., Qin, J. (2008) Classifi-cation of Surface EMG Signal Based on Energy Spectra Change, 2008 International Conference on BioMedical Engineering and Informatics, 375-379.

[12]   Mallat, S.G. (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell, 11, 674-693.

[13]   Daubechies, I., Mallat, S., Willsky, A.S. (1992) Introduc-tion to the special issue on wavelet transforms and mul-tiresolution signal analysis. IEEE Trans. Inform. Theory, 38, 529-531.

[14]   Coifman, R.R. and Wickerhauser, M.V. (1992) Entropy- based algorithms for best basis selection. IEEE Trans. Inform. Theom, 38, 713–718.

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

[16]   Flanders, M. (2002) Choosing a wavelet for single-trial EMG. Journal of Neuroscience Methods, 116, 165 -177.

[17]   Englehart, K. and Hudgins, B. (2003) A robust, real-time control scheme for multi-function myoelectric control. IEEE Trans. Biomed. Eng., 50(7), 848-854.

[18]   Flanders, M. (2002) Choosing a wavelet for single-trial EMG. Journal of Neuroscience Methods, 116, 165-177.

[19]   Sameer, S., Nabeel, M., Walter, K. (2001) Advances in pattern recognition. Springer.

 
 
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