Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification
Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.
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Canal, M. R. (2010). Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals. J. Med. Syst., 34 (1), 91-94.
Oskoei, M. A., Hu, H. (2007). Myoelectric control systems - a survey. Biomed. Signal Process. Control., 2 (4), 275-294.
Pauk, J. (2008). Different techniques for EMG signal processing. J. Vibroeng., 10 (4), 571-576.
Neto, O. P., Marzullo, A. C. D. (2009). Wavelet transform analysis of electromyography Kung Fu strikes data. J. Sports Sci. Med., 8 (CSSI 3), 25-28.
Kumar, S., Prasad, N. (2010). Torso muscle EMG profile differences between patients of back pain and control. Clin. Biomech., 25 (2), 103-109.
Kumar, D. K., Pah, N. D., Bradley, A. (2003). Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE Trans. Neural Syst. Rehabil. Eng., 11 (4), 400-406.
Delis, A. L., Carvalho, J. L. A., Rocha, A. F. D., Ferreira, R. U., Rodrigues, S. S., Borges, G. A. (2009). Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses. Physiol. Meas., 30 (9), 931-946.
Hussain, M. S., Reaz, M. B. I., Mohd-Yasin, F., Ibrahimy, M. I. (2009). Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. Expert Syst., 26 (1), 35-48.
Wang, G., Wang, Z., Chen, W., Zhuang, J. (2006). Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med. Biol. Eng. Comput., 44 (4), 865-872.
Englehart, K., Hudgins, B., Parker, P. A. (2001). A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng., 48 (3), 302-311.
Chu, J. U., Moon, I., Mun, M. S. (2006). A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric control. IEEE Trans. Biomed. Eng., 53 (11), 2232-2239.
Chu, J. U., Moon, I., Lee, Y. J., Kim, S. K., Mun, M. S. (2007). A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Trans. Mechatron., 12 (3), 282-290.
Boostani, R., Moradi, M. H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol. Meas., 24 (2), 309-319.
Khezri, M., Jahed, M. (2011). A neuro-fuzzy inference system for sEMG-based identification of hand motion commands. IEEE Trans. Ind. Electron., 58 (5), 1952-1960.
Rafiee, J., Rafiee, M. A., Yavari, F., Schoen, M. P. (2011). Feature extraction of forearm EMG signals for prosthetics. Expert Syst. Appl., 38 (4), 4058-4067.
Reaz, M. B. I., Hussain, M. S., Mohd-Yasin, F. (2006). EMG analysis using wavelet functions to determine muscle contraction. In 8th International Conference on e-Health Networking, Applications and Services, August 2006. New Delhi, India, 132-134.
Mahaphonchaikul, K., Sueaseenak, D., Pintavirooj, C., Sangworasil, M., Tungjitkusolmun, S. (2010). EMG signal feature extraction based on wavelet transform. In 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, May 2010. Chiang Mai, Thailand, 356-360.
Naik, G. R., Kumar, D. K., Arjunan, S. P. (2010). Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques. Meas. Sci. Rev., 10 (1), 1-6.
Yang, G.-Y., Luo, Z.-Z. (2004). Surface electromyography disposal based on the method of wavelet de-noising and power spectrum. In International Confernece on Intelligent Mechatronics and Automation, August 2004. Chengdu, China, 896-900.
Buranachai, C., Thanvarungkul, P., Kanatharanaa, P., Meglinski, I. (2009). Application of wavelet analysis in optical coherence tomography for obscured pattern recognition. Laser Phys. Lett., 6 (12), 892-895.
Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2010). Optimal wavelet functions in wavelet denoising for multifunction myoelectric control. ECTI Transactions on Electrical Eng., Electronics, and Communications, 8 (1), 43-52.
Singh, B. N., Tiwari, A. K. (2006). Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process., 16 (3), 275-287.
Phinyomark, A., Hirunviriya, S., Limsakul, C., Phukpattaranont, P. (2010). Evaluation of EMG feature extraction for hand movement recognition based on euclidean distance and standard deviation. In 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, May 2010. Chiang Mai, Thailand, 856-860.
Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2009). A novel feature extraction for robust EMG pattern recognition. J. Comput., 1 (1), 71-80.
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.