Computer Recognition Systems 2 pp 589-595 | Cite as
Automatic Segmentation of EMG Signals Based on Wavelet Representation
Conference paper
Abstract
In this paper the automatic segmentation of EMG signals based on wavelet representation is presented. It is shown that wavelet representation can be usefull in detecting particular spikes in EMG signals and the presented segmentation algorithm may be usefull for the detection of active segments. The algorithms has been tested on the synthetic model signal and on real signals recorded with transcutaneous multi-point electrode.
Preview
Unable to display preview. Download preview PDF.
References
- 1.C. I. Christodoulou, C. S. Pattchis (1999) Unsupervided Pattern Recognition for the Classification of EMG Signals, IEEE Transactions on Biomedical Engineering 46:169–178CrossRefGoogle Scholar
- 2.M. H. Hassoun and Ch. Wang and R. Spitzer (1994) NNERVE: Neural Network Extraction of Repetitive Vectors for Electromyography — Part I: Algorithm, IEEE Transactions on Biomedical Engineering 41: 1039–1052CrossRefGoogle Scholar
- 3.R.S LeFever, C. J. DeLuca (1982) A procedure for decomposing the myoelectric signal into its constituent action potentials: part I, execution and test for accuracy. Technique, theory and implementation, IEEE Transactions on Biomedical Engineering, 29:149–157Google Scholar
- 4.D. W. Stashuk (1998) Decomposition and quantitative analysis of clinical electromyographic signals, Medical Engineering & Physics 21:389–404CrossRefGoogle Scholar
- 5.C. Torrence and G. P. Compo (1998) A Practical Guide to Wavelet Analysis, Bulletin of the American Meteorological Society 79:61–78CrossRefGoogle Scholar
- 6.D. Nishikawa and W. Yu and H. Yokoi and Y. Kakazu (1999) Analyzing and discriminating emg signals using wavelet transform and real-time learning method, Intelligent Engineering Systems Through Artificial Neural Networks 9:281–286Google Scholar
- 7.Yamada, R. and Ushiba, J. and Tomita, Y. and Masakado, Y. (2003) Decomposition of electromyographic signal by principal component analysis of wavelet coefficients, in IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 118–119Google Scholar
- 8.H. Nakamura and M. Yoshida and M. Kotani and K. Akazawa and T. Moritani (2004) The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part I-measuring techniques, Journal of Electromyography and Kinesiology 14:423–432CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2007