Comparison Study of Electromyography Using Wavelet and Neural Network

Authors

  • Nebras Hussain Gheab Baghdad University/ Al Khwarizmy College of Engineering/ Biomedical Engineering Department
  • Sadeem Nabeel Saleem Baghdad University/ Al Khwarizmy College of Engineering/ Biomedical Engineering Department

Abstract

In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

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References

[1] John G. Webster, Encyclopedia of Medical Devices and Instrumentation, 2nd edition, 2006, vol. 3, p 99 – 109.
[2] M. B. I. Reaz, M. S. Hussain and F. Mohd-Yasin, Techniques of EMG signal analysis: detection, processing, classification and applications, Biol. Proced. Online 2006 8(1): 11-35.
[3] Peter Konrad, The ABC of EMG - A Practical Introduction to Kinesiological EMG, power by Noraxon INC. USA, Version 1.0 April 2005, page 5.
[4] D. Moshou, I. Hostens, G. Papaioannou, H. Ramon, Wavelets and self-organizing maps in EMG analysis, ESIT 2000, 14-15 September 2000, Aachen, Germany.
[5] K. Englehart, B. Hudgins, P. A. Parker and V. Stevenson (1999). Classification of the Myoelectric Signal Using Time-Frequency Based Representations, Medical Eng. and Physics, volume 21, pages 431-438.

[6] R. Carreño and M. I. Vuskovic: “Wavelet Transform Moments for Feature Extraction from temporal Signals’” 2nd Internat. Conference in Control, Automation and Robotics (ICINCO 2005), 14-17 September, Barcelona, Spain, 2005
[7] Daubechies, I., 1988, “Orthonormal bases of compactly supported wavelets”, Commune. Pure Applied Mathematics 41, pp. 909-996.
[8] Meyer, Y., 1989, “Orthonormal Wavelets, Wavelets, time-frequency methods and phase-space”, J. M. Combes, A. Grossman, P. Tchamitchian, (eds.), Springer-Verlag, pp. 21-37.
[9] C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelet and Wavelet Transforms, Prentice – Hall, Inc., 1998.
[10] P. Wellig, C. Zhenlan, M. Semling, and G. S. Moschytz, “Electromyogram data compression using single-tree and modified zero-tree wavelet encoding,” in Engineering in Medicine and Biology Society. Proceedings of the 20th Annual International Conference of the IEEE, Hong Kong, China, Oct. 1998, vol. 3, pp. 1303–1306.
[11] M. S. Hussain, M. B.I. Reaz, M. I. Ibrahimy, and F. Mohd-Yasin, An Efficient Technique of Analyzing Surface EMG Signals, ISBME 2006, paper No. 107.
[12] J. U. Chu, I. Moon, and M Mun, A Real-Time EMG Pattern Recognition based on Linear-Nonlinear Feature Projection for Multifunction Myoelectric Hand, Proceedings of the 2005 IEEE, 9th International Conference on Rehabilitation Robotics, June 28 - July 1, 2005, Chicago, IL, USA.
[13] J. Laakso, M. Juhola, V. Surakka, A. Aula and T. Partala, Neural Network and Wavelet Recognition of Facial Electromyographic Signals, MEDINFO, V. Patel et al. (Eds), Amsterdam: IOS Press, 2001.
[14] Wavelet Toolbox help, MATLAB 7.a.
[15] G. D. Luca, Fundamental Concepts in EMG Signal Acquisition, Delsys Inc., 2003.

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Published

2019-03-01

How to Cite

Comparison Study of Electromyography Using Wavelet and Neural Network. (2019). Al-Khwarizmi Engineering Journal, 4(3), 108-119. https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/600

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