Wavelet Transform-Based Classification of Electromyogram Signals Using an Anova Technique

1. J. Kilby and H. G. Hosseini, “Extracting effective features of SEMG using continuous wavelet transform,” Conf. Proc. IEEE Eng. Med. Biol. Sci., 1, 1704-1707 (2006).

   Article  CAS  Google Scholar 

2. H. S. Ryait, A. S. Arora, and R. Agarwal, “Interpretations of wrist operations from surface-EMG signals at different locations on arm along with acupressure points,” IEEE Transact. Biomed. Circ. Syst., 4, No. 2, 101-111 (2010).

   Article  CAS  Google Scholar 

3. Q. Zhang and Zh. Luo, “Wavelet de-noising of electromyography,” in: Proc. IEEE Internat. Conf. Mechatron. Automat. (June 25 - 28, 2006), China (2006), pp. 1553-1558.

4. K. A. Wheeler, H. Shimada, D. K. Kumar, and S. P. Arjunan, “A sEMG model with experimentally based simulation parameters,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 4258-4261 (2010).

5. N. M. Sobahi, “Denoising of EMG signals based on wavelet transform,” Asian Transact. Eng., 1, No. 5, 17-23 (2011).

   Google Scholar 

6. A. Phinyomark, A. Nuidod, P. Phukpattaranont, and C. Limsakul, “Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification,” Electron. Electr. Eng. Signal Technol., 6, 27-32 (2012).

   Google Scholar 

7. A. Phinyomark, C. Llimsakul, and P. Phukpattaranont, “Optimal wavelet functions in wavelet denoising for multifunction myoelectric control,” ECTI Transact. Electr. Eng. Electron. Commun., 8, 43-52 (2010).

   Google Scholar 

8. A. Phinyomark, C. Llimsakul, and P. Phukpattaranont, “Application of wavelet analysis in EMG feature extraction for pattern classification,” Measur. Sci. Rev., 11, 45-52 (2011).

   Google Scholar 

9. K. Englehart, B. Hudgins, and P. A. Parker, “A waveletbased continuous classification scheme for multifunction myoelectric control,” IEEE Transact. Biomed. Eng., 48, No. 3, 302-311 (2001).

   Article  CAS  Google Scholar 

10. M. Lascu and D. Lascu, “Graphical programming based biomedical signal acquisition and processing,” Int. J. Circ. Syst. Sign. Proc., 1, No. 4, 317-326 (2007).

    Google Scholar 

11. R. L. Ortolan, R. N. Mori, R. R. Pereira, et al., “Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment,” IEEE Transact. Neural Syst. Rehabil. Eng., 11, No. 1, 60-69 (2010).

    Article  Google Scholar 

12. C. F. Jiang and S. L. Kuo, “A comparative study of wavelet denoising of surface electromyographic signals,” Conf. Proc. IEEE Eng. Med. Biol. Sci., 1868-1871 (2007).

13. X. Zhang, Y. Wang, and R. P. S. Han, “Wavelet transform theory and its application in EMG signal processing,” 7th Int. Conf. Fuzzy Syst. Knowl. Discov. (Aug 23-26, 2010), 6 (2010).

14. S. Micera, “Control of hand prostheses using peripheral information,” IEEE Rev. Biomed. Eng., 3, 48-68 (2010).

    Article  PubMed  Google Scholar 

15. K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Med. Eng. Phys., 21, Nos. 6/7, 431-438 (1999).

    Article  CAS  PubMed  Google Scholar 

16. A. Phinyomark, P. Phukpattaranont, C. Limsakul, et al., “The usefulness of mean and median frequencies in electromyography analysis,” in: Computational Intelligence in Electromyography Analysis – A Perspective on Current Applications and Future Challenges, G. R. Naik (ed.), InT ech, Chap. 8 (2012).

17. C. J. De Luca, M. A. Sabbahi, and S. H. Roy, “Median frequency of the myoelectric signal: Effect of hand dominance,” Eur. J. Appl. Physiol. Occup. Physiol., 55, No. 5, 457-464 (1986).

    Article  PubMed  Google Scholar 

18. Sh. J. Coakers, SPSS Version 12.0 for Windows, Analysis Without Anguish, John Wiley Sons Publ., Australia (2005), p. 88.

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