Wavelet-based denoising algorithm for robust EMG pattern recognition

Fluctuation and Noise Letters

Volumn 10, Issue 2, 2011, Pages 157-167

  Phinyomark, Angkoon ^a   Phukpattaranont, Pornchai ^a   Limsakul, Chusak ^a  

^a PRINCE OF SONGKLA UNIVERSITY   (Thailand)

Author keywords

Electromyography (EMG) signal; robust recognition; wavelet denoising; wavelet transform; white Gaussian noise

Indexed keywords

EID: 79955831275     PISSN: 02194775     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219477511000466     Document Type: Article

References (35)

1. M. H. Silber, Clinical utility of surface EMG, Neurology 55 (2000) 171-177.
2. S. Koçer, Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases, J. Med. Syst. 34 (2010) 321-329.
3. Y.-C. Dua, C.-H. Linb, L.-Y. Shyuc and T. Chena, Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis, Expert Syst. Appl. 37 (2010) 4283-4291.
4. D. K. Kumar, N. D. Pah and A. Bradley, Wavelet analysis of surface electromyogra-phy to determine muscle fatigue, IEEE Trans. Neural Syst. Rehabil. Eng. 11 (2003) 400-406. (Pubitemid 38134113)
5. M. Zecca, S. Micera, M. C. Carrozza and P. Dario, Control of multifunctional prosthetic hands by processing the electromyographic signal, Crit. Rev. Biomed. Eng. 30 (2002) 459-485. (Pubitemid 36528604)
6. E. A. Clancy, E. L. Morin and R. Merletti, Sampling, noise-reduction and amplitude estimation issues in surface electromyography, J. Electromyography Kinesiol. 12 (2002) 1-16. (Pubitemid 34132825)
7. M. B. I. Reaz, M. S. Hussain and F. Mohd.-Yasin, Techniques of EMG signal analysis: Detection, processing, classification and applications, Biol. Proced. O nline 8 (2006) 11-35.
8. M. S. Hussain, M. B. I. Reaz, F. Mohd-Yasin and M. I. Ibrahimy, Electromyogra-phy signal analysis using wavelet transform and higher order statistics to determine muscle contraction, Expert Syst. 26 (2009) 35-48.
9. X. Ren, X. Hu, Z. Wang and Z. Yan, MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition, Med. Biol. Eng. Comput. 44 (2006) 371-382. (Pubitemid 43949694)
10. A. Phinyomark, C. Limsakul and P. Phukpattaranont, A novel feature extraction for robust EMG pattern recognition, J. Comput. 1 (2009) 71-80.
11. S. N. Kale and S. V. Dudul, Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network, Appl. Comput. Intell. Soft Comput. 7 (2009) 1-12.
12. C. J. De Luca, Surface Electromyography: Detection and Recording (DelSys Incorporated, 2002).
13. C. J. De Luca, L. D. Gilmore, M. Kuznetsov and S.H. Roy, Filtering the surface EMG signal: Movement artifact and baseline noise contamination, J. Biomech. 43 (2010) 1573-1579.
14. M. Zardoshti-Kermani, B. C. Wheeler, K. Badie and R. M. Hashemi, EMG feature evaluation for movement control of upper extremity prostheses, IEEE Trans. Rehabil. Eng. 3 (1995) 324-333.
15. R. Boostani and M. H. Moradi, Evaluation of the forearm EMG signal features for the control of a prosthetic hand, Physiol. Meas. 24 (2003) 309-319. (Pubitemid 36648398)
16. F. Laterza and G. Olmo, Analysis of EMG signals by means of the matched wavelet transform, Electron. Lett. 33 (1997) 357-359. (Pubitemid 127546378)
17. P. Wellig and G. S. Moschytz, Analysis of wavelet features for myoelectric signal classification, Proc. IEEE Int. Conf. Electronics, Circuits and System, Lisbon, Portugal (1998), pp. 109-112.
18. A. Phinyomark, C. Limsakul and P. Phukpattaranont, A comparative study of wavelet denoising for multifunction myoelectric control, Proc. Int. Conf. on Computer and Automation Engineering, Bangkok, Thailand (2009), pp. 21-25.
19. M. S. Hussain, M. B. I. Reaz, M. I. Ibrahimy, A. F. Ismail and F. Mohd-Yasin, Wavelet based noise removal from EMG signals, Inform. MIDEM 37 (2007) 94-97.
20. M. Khezri and M. Jahed, Surface Electromyogram signal estimation based on wavelet thresholding technique, Proc. 30th Ann. Int. IEEE EMBS Conf., Vancouver, British Columbia, Canada (2008), pp. 4752-4755.
21. A. Phinyomark, C. Limsakul and P. Phukpattaranont, EMG denoising estimation based on adaptive wavelet thresholding for multifunction myoelectric control, Proc. Conf. Innovative Technologies in Intelligent Systems and Industrial Applications, Monash University, Sunway campus, Malaysia (2009), pp. 171-176.
22. Z. Liu and Z. Luo, EMG de-noising by multi-scale product coefficient hard thresholding, J. Huazhong Univ. Sci. Nat. Sci. 36 (2008) 134-136.
23. Z.-Z. Luo, Q.-J. Zhang and J.-P. Jiang, Improving method of surface electromyog-raphy denoising based on wavelet transform, J. Zhejiang Univ. (Eng. Sci.) 41 (2007), pp. 213-216, 220.
24. D. L. Donoho, De-noising by soft-thresholding, IEEE Trans. Inf. Theory 41 (1995) 613-627.
25. D. L. Donoho, Wavelet analysis and WVD: A ten minute tour, Proc. Int. Conf. Wavelets and Applications, France (1992).
26. S. Zhong and V. Cherkassky, Image denoising using wavelet thresholding and model selection, Proc. Int. Conf. Image Processing, Vancouver, British Columbia, Canada (2000), pp. 262-265.
27. G. Song and R. Zhao, Three novel models of threshold estimator for wavelet coefficients, Proc. 2nd Int. Conf. Wavelet Analysis and Its Applications, Hong Kong (2001), pp. 145-150.
28. Z. Qingju and L. Zhizeng, Wavelet de-noising of electromyography, Proc. IEEE Int. Conf. Mechatronics and Automation, Luoyang, China (2006), pp. 1553-1558.
29. Y. Ying and W. Yusen, New threshold and shrinkage function for ECG signal denoising based on wavelet transform, Proc. 3rd Int. Conf. Bioinformatics and Biomedical Engineering, Beijing, China (2009), pp. 1-4.
30. A. D. C. Chan and G. C. Green, Myoelectric control development toolbox, Proc. 30th Conf. Canadian Medical and Biological Engineering Society, Toronto, Canada (2007), p. M0100.
31. K. Englehart, B. Hudgin and P. A. Parker, A wavelet-based continuous classification scheme for multifunction myoelectric control, IEEE Trans. Biomed. Eng. 48 (2001) 302-311. (Pubitemid 32249578)
32. O. Farooq and S. Datta, Wavelet-based denoising for robust feature extraction for speech recognition, Electron. Lett. 39 (2003) 163-165.
33. M. A. Oskoei and H. Hu, Myoelectric control systems - a survey, Biomed. Signal Process. Control 2 (2007) 275-294.
34. A. Phinyomark, C. Limsakul and P. Phukpattaranont, Optimal wavelet functions in wavelet denoising for multifunction myoelectric control, ECTI Trans. Elec. Eng. Electron. Commun. 8 (2010) 43-52.
35. S. Poornachandra, Wavelet-based denoising using subband dependent threshold for ECG signals, Digital Signal Process. 18 (2008) 49-55. (Pubitemid 350184353)