Full Text:   <2754>

CLC number: R318.04

On-line Access: 

Received: 2006-01-13

Revision Accepted: 2006-04-10

Crosschecked: 0000-00-00

Cited: 20

Clicked: 5975

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE B 2006 Vol.7 No.10 P.844-848


Characterization of surface EMG signals using improved approximate entropy

Author(s):  CHEN Wei-ting, WANG Zhi-zhong, REN Xiao-mei

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

Corresponding email(s):   zzwang@sjtu.edu.cn

Key Words:  Surface EMG (sEMG) signal, Nonlinear analysis, Approximate entropy (ApEn), Fractal dimension

Share this article to: More <<< Previous Article|

CHEN Wei-ting, WANG Zhi-zhong, REN Xiao-mei. Characterization of surface EMG signals using improved approximate entropy[J]. Journal of Zhejiang University Science B, 2006, 7(10): 844-848.

@article{title="Characterization of surface EMG signals using improved approximate entropy",
author="CHEN Wei-ting, WANG Zhi-zhong, REN Xiao-mei",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Characterization of surface EMG signals using improved approximate entropy
%A CHEN Wei-ting
%A WANG Zhi-zhong
%A REN Xiao-mei
%J Journal of Zhejiang University SCIENCE B
%V 7
%N 10
%P 844-848
%@ 1673-1581
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.B0844

T1 - Characterization of surface EMG signals using improved approximate entropy
A1 - CHEN Wei-ting
A1 - WANG Zhi-zhong
A1 - REN Xiao-mei
J0 - Journal of Zhejiang University Science B
VL - 7
IS - 10
SP - 844
EP - 848
%@ 1673-1581
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.B0844

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.

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


[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] Chang, G.C., Kang, W.J., Luh, J.J., Cheng, C.K., Lai, J.S., Chen, 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] Crawford, B., Miller, K., Shenoy, P., Rao, R., 2005. Real-Time Classification of Electromyographic Signals for Robotic Control. Proceedings of the National Conference on Artificial Intelligence. Pittsburgh, Pennsylvania, USA, 2:523-528.

[4] Doorenbosch, C.A.M., Harlaar, J., 2004. Accuracy of a practicable EMG to force model for knee muscles. Neuroscience Letters, 368(1):78-81.

[5] Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M., 1999. Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys., 21(6-7):431-438.

[6] 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. Biomed. Eng., 50(2):262-265.

[7] Gupta, V., Suryanarayanan, S., Reddy, N.P., 1997. Fractal analysis of surface EMG signals from the biceps. International Journal of Medical Informatics, 45(3):185-192.

[8] 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.

[9] Huang, Y., Englehart, K.B., Hudgins, B., Chan, A.D.C., 2005. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans. Biomed. Eng., 52(11):1801-1811.

[10] Jang, G.C., Cheng, C.K., Lai, J.S., Kuo, T.S., 1994. Using Time-Frequency Analysis Technique in the Classification of Surface EMG Signals. Proceeding of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Baltimore, Maryland, 16:1242-1243.

[11] Kang, W.J., Shiu, J.R., Cheng, C.K., Lai, J.S., Tsao, H.W., Kuo, T.S., 1995. The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition. IEEE Trans. Biomed. Eng., 42(8):777-785.

[12] 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.

[13] Kim, K.H., Kim, H.K., Kim, J.S., Son, W., Lee, S.Y., 2006. A biosignal-based human interface controlling a power-wheelchair for people with motor disabilities. ETRI Journal, 28(1):111-114.

[14] Park, J.H., Stelmach, G.E., 2006. Effect of combined variation of force amplitude and rate of force development on the modulation characteristics of muscle activation during rapid isometric aiming force production. Exp. Brain Res., 168(3):337-347.

[15] Pincus, S.M., 1991. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA, 88(6):2297-2301.

[16] Pincus, S.M., 1995. Approximate entropy (ApEn) as a complexity measure. Chaos, 5(1):110-117.

[17] Pincus, S.M., Goldberger, A.L., 1994. Physiological time-series analysis: what does regularity quantify? Am. J. Physiol., 266(4 35-4):H1643-H1656.

[18] 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. Rehabil. Eng., 8(3):433-436.

[19] Wolf, A., Swift, J.B., Swinney, H.L., Vastano, J.A., 1985. Determining Lyapunov exponents from a time-series. Physica D, 16(3):285-317.

[20] Zardoshti-Kermani, M., Wheeler, B.C., Badie, K., Hashemi, R.M., 1995. EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans. Rehabil. Eng., 3(4):324-333.

[21] Zijdewind, I., de Groot, M.C.H., Kernell, D., 1998. Task-related variations in motoneuronal drive to a human intrinsic hand muscle. Neuroscience Letters, 242(3):139-142.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE