The Utility of Wavelet Transform in Surface Electromyography Feature Extraction -A Comparative Study of Different Mother Wavelets
Electromyography (EMG) signal processing has been investigated remarkably regarding various applications such as in rehabilitation systems. Specifically, wavelet transform has served as a powerful technique to scrutinize EMG signals since wavelet transform is consistent with the nature of EMG as a non-stationary signal. In this paper, the efficiency of wavelet transform in surface EMG feature extraction is investigated from four levels of wavelet decomposition and a comparative study between different mother wavelets had been done. To recognize the best function and level of wavelet analysis, two evaluation criteria, scatter plot and RES index are recruited. Hereupon, four wavelet families, namely, Daubechies, Coiflets, Symlets and Biorthogonal are studied in wavelet decomposition stage. Consequently, the results show that only features from first and second level of wavelet decomposition yields good performance and some functions of various wavelet families can lead to an improvement in separability class of different hand movements.
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[2] De Luca, C.J., Physiology and mathematics of myoelectric signals.
Biomedical Engineering, IEEE Transactions on, 1979(6): p. 313-325.
[3] Criswell, E., Cram's introduction to surface electromyography. 2010:
Jones & Bartlett Learning.
[4] Asghari Oskoei, M. and H. Hu, Myoelectric control systemsÔÇöA survey.
Biomedical Signal Processing and Control, 2007. 2(4): p. 275-294.
[5] Ahmad, S.A.I., Asnor J.; Ali, Sawal H.; Chappell, Paul H., Review of
Electromyography Control Systems Based on Pattern Recognition for
Prosthesis Control Application. Australian Journal of Basic & Applied
Sciences, 2011. Vol. 5 (Issue 8): p. p1512.
[6] Polikar, R., The wavelet tutorial. 1996.
[7] Karlsson, S., J. Yu, and M. Akay, Time-frequency analysis of
myoelectric signals during dynamic contractions: a comparative study.
Biomedical Engineering, IEEE Transactions on, 2000. 47(2): p. 228-
238.
[8] Englehart, K., et al., Classification of the myoelectric signal using timefrequency
based representations. Medical engineering & physics, 1999.
21(6): p. 431-438.
[9] Khezri, M. and M. Jahed. Introducing a new multi-wavelet function
suitable for sEMG signal to identify hand motion commands. in
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th
Annual International Conference of the IEEE. 2007: IEEE.
[10] M. S. Hussain, M.M., Effectiveness of the Wavelet Transform on the
Surface EMG a Understand the Muscle Fatigue During Walk.
Measurement Science Review, 2012. 12.
[11] Phinyomark, A., C. Limsakul, and P. Phukpattaranont, Application of
wavelet analysis in EMG feature extraction for pattern classification.
Measurement Science Review, 2011. 11(2): p. 45-52.
[12] Phinyomark, A., et al. Evaluation of EMG feature extraction for hand
movement recognition based on Euclidean distance and standard
deviation. in Electrical Engineering/Electronics Computer
Telecommunications and Information Technology (ECTI-CON), 2010
International Conference on. 2010: IEEE.
[13] Ahmad, S.A. and P.H. Chappell, Moving approximate entropy applied to
surface electromyographic signals. Biomedical Signal Processing and
Control, 2008. 3(1): p. 88-93.
[14] Chui, C.K., Wavelet Analysis and Its Applications. 1995, DTIC
Document.
[15] Fugal, D.L., Conceptual Wavelets in Digital Signal Processing. Space
& Signals Technical Publishing, 2009.
[16] Guang-ying, Y. and L. Zhi-zeng. Surface electromyography disposal
based on the method of wavelet de-noising and power spectrum. in
Intelligent Mechatronics and Automation, 2004. Proceedings. 2004
International Conference on. 2004: IEEE.
[17] Phinyomark, A., C. Limsakul, and P. Phukpattaranont, Optimal wavelet
functions in wavelet denoising for multifunction myoelectric control.
ECTI Transactions on Electrical Eng., Electronics, and
Communications.-ECTI, 2010. 8(1): p. 43-52
[1] Merletti, R. and P. Parker, Physiology, engineering, and noninvasive
applications. 2004: IEEE Press Series on Biomedical Engineering).-
Wiley-IEEE Press.
[2] De Luca, C.J., Physiology and mathematics of myoelectric signals.
Biomedical Engineering, IEEE Transactions on, 1979(6): p. 313-325.
[3] Criswell, E., Cram's introduction to surface electromyography. 2010:
Jones & Bartlett Learning.
[4] Asghari Oskoei, M. and H. Hu, Myoelectric control systemsÔÇöA survey.
Biomedical Signal Processing and Control, 2007. 2(4): p. 275-294.
[5] Ahmad, S.A.I., Asnor J.; Ali, Sawal H.; Chappell, Paul H., Review of
Electromyography Control Systems Based on Pattern Recognition for
Prosthesis Control Application. Australian Journal of Basic & Applied
Sciences, 2011. Vol. 5 (Issue 8): p. p1512.
[6] Polikar, R., The wavelet tutorial. 1996.
[7] Karlsson, S., J. Yu, and M. Akay, Time-frequency analysis of
myoelectric signals during dynamic contractions: a comparative study.
Biomedical Engineering, IEEE Transactions on, 2000. 47(2): p. 228-
238.
[8] Englehart, K., et al., Classification of the myoelectric signal using timefrequency
based representations. Medical engineering & physics, 1999.
21(6): p. 431-438.
[9] Khezri, M. and M. Jahed. Introducing a new multi-wavelet function
suitable for sEMG signal to identify hand motion commands. in
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th
Annual International Conference of the IEEE. 2007: IEEE.
[10] M. S. Hussain, M.M., Effectiveness of the Wavelet Transform on the
Surface EMG a Understand the Muscle Fatigue During Walk.
Measurement Science Review, 2012. 12.
[11] Phinyomark, A., C. Limsakul, and P. Phukpattaranont, Application of
wavelet analysis in EMG feature extraction for pattern classification.
Measurement Science Review, 2011. 11(2): p. 45-52.
[12] Phinyomark, A., et al. Evaluation of EMG feature extraction for hand
movement recognition based on Euclidean distance and standard
deviation. in Electrical Engineering/Electronics Computer
Telecommunications and Information Technology (ECTI-CON), 2010
International Conference on. 2010: IEEE.
[13] Ahmad, S.A. and P.H. Chappell, Moving approximate entropy applied to
surface electromyographic signals. Biomedical Signal Processing and
Control, 2008. 3(1): p. 88-93.
[14] Chui, C.K., Wavelet Analysis and Its Applications. 1995, DTIC
Document.
[15] Fugal, D.L., Conceptual Wavelets in Digital Signal Processing. Space
& Signals Technical Publishing, 2009.
[16] Guang-ying, Y. and L. Zhi-zeng. Surface electromyography disposal
based on the method of wavelet de-noising and power spectrum. in
Intelligent Mechatronics and Automation, 2004. Proceedings. 2004
International Conference on. 2004: IEEE.
[17] Phinyomark, A., C. Limsakul, and P. Phukpattaranont, Optimal wavelet
functions in wavelet denoising for multifunction myoelectric control.
ECTI Transactions on Electrical Eng., Electronics, and
Communications.-ECTI, 2010. 8(1): p. 43-52
@article{"International Journal of Electrical, Electronic and Communication Sciences:51637", author = "Farzaneh Akhavan Mahdavi and Siti Anom Ahmad and Mohd Hamiruce Marhaban and Mohammad-R. Akbarzadeh-T", title = "The Utility of Wavelet Transform in Surface Electromyography Feature Extraction -A Comparative Study of Different Mother Wavelets", abstract = "Electromyography (EMG) signal processing has been investigated remarkably regarding various applications such as in rehabilitation systems. Specifically, wavelet transform has served as a powerful technique to scrutinize EMG signals since wavelet transform is consistent with the nature of EMG as a non-stationary signal. In this paper, the efficiency of wavelet transform in surface EMG feature extraction is investigated from four levels of wavelet decomposition and a comparative study between different mother wavelets had been done. To recognize the best function and level of wavelet analysis, two evaluation criteria, scatter plot and RES index are recruited. Hereupon, four wavelet families, namely, Daubechies, Coiflets, Symlets and Biorthogonal are studied in wavelet decomposition stage. Consequently, the results show that only features from first and second level of wavelet decomposition yields good performance and some functions of various wavelet families can lead to an improvement in separability class of different hand movements.
", keywords = "Electromyography signal, feature extraction, wavelettransform, means absolute value.", volume = "7", number = "2", pages = "118-6", }
