OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION BASED ON ADAPTIVE WAVELETS K-MEAN ALGORITHM

Abbas K. Abbas, Rasha Bassam, Rana M. Kasim

Abstract

In this paper the optimization of EMG signals segmentation and decomposition based on wavelet represen-tation and k-mean clustering technique is presented. It is shown that wavelet decomposition can be usefull in detecting particular spikes in EMG signals and the presented segmentation algorithm may be useful for the detection of active segments in related MUAP’s action potentials. The algorithms has been tested on the synthetic model signal and on real signals recorded with intramuscular multi-point electrode. The efficiency of EMG signal decomposition and classification with adaptive wavelet algorithm were presented. Single and multiple fibers MUAP patterns were tested and identified. By applying a Debauchies wavelet transformation and k-mean clustering algorithm to localize the action-potential source in the presence of specific neuromuscular diseases like NMI neuropathy, muscular dystrophy and myasthenia gravis (MG), instead of many decomposition and pattern recognition algorithm, wavelets and k-mean clustering have its flexibility for robustly classify and localize the signal stochastic sources with a linear way, in addition to identify the blind source for EMG bioelectric potential.

References

  1. Zazula, D. (1999). Higher-order statistics used for decomposition of sEMGs. Proceedings of the 12th IEEE Symposium on Computer Based Medical System, 72- 77.
  2. Wang, R., C. Huang, and B. Li (1997). A neural networkbased surface electromyography motion pattern classifier for the control of prostheses. Proceedings of the 19th Annual International Conference of the IEEEEMBS 1277.
  3. Thompson, B., P. Picton, and N. Jones (1996). A comparison of neural network and traditional signal processing techniques in the classification of EMG signals. IEEE Colloquium on Artificial Intelligence Methods for Biomedical Data Processing, Vol 2:1996 , Pp 321-327.
  4. McKeown, M. J., Torpey, D.C., Gehm W. C.: NonInvasive Monitoring of Functionally Distinct Muscle Activations during Swallowing. Clinical Neurophysiology (2002). 109, 112
  5. McKeown, M. J.: Cortical activation related to arm movement combinations. Muscle Nerve. 9:19-25 (2000). 110, 112
  6. Fang, J., G. C. Agarwal, and B. T. Shahani, “Decomposition of multiunit electromyographic signals,” IEEE TransBME 46 , 685-697 (1999).
  7. Jung T.P., Makeig S., McKeown M. J., Bell A. J., Lee T.W., Sejnowski T. J.: Imaging brain dynamics using independent component analysis. Proc. IEEE. 89(7): 1107-22, (2001). 112
  8. Andrzej Cichocki, Shun-ichi AMARI, “ Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications”, Wiley Press ,2003.
  9. Bell A. J., Sejnowski T. J.: An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7:1129-1159, (1995). 112
  10. Micera S.,Vannozzi G., Sabatini A.M., Dario P.: Improving Detection of Muscle Activation intervals, IEEE Engineering in Medicine and Biology, vol. 20 n.6:38- 46 (2001). 112, 113
  11. Karhunen J., Oja E.: A Class of Neural Networks for Independent Component Analysis, IEEE Transactions on Neural Network, vol. 8 n. 3:486-504, (1997). 112.
  12. Farina, D., R. Colombo, R. Merletti, and H. Baare Olsen, “Evaluation of intra-muscular EMG decomposition algorithms,” J Electromyogr Kinesiol 11, 175-187 (2001).
  13. Kadefors, R., M. Forsman, B. Zoega, and P. Herberts, “Recruitment of low threshold motor-units in the trapezius muscle during different static arm positions,” Ergonomics 42, 359-375 (1999).
  14. Olsen, H. B., H. Christensen, and K. Søgaard, “An analysis of motor unit firing pattern during sustained low force contraction in fatigued muscle,” Acta Physiology Pharmacology Bulg 26, 73-78 (2001).
Download


Paper Citation


in Harvard Style

K. Abbas A., Bassam R. and M. Kasim R. (2009). OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION BASED ON ADAPTIVE WAVELETS K-MEAN ALGORITHM . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 491-497. DOI: 10.5220/0001542804910497


in Bibtex Style

@conference{biosignals09,
author={Abbas K. Abbas and Rasha Bassam and Rana M. Kasim},
title={OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION BASED ON ADAPTIVE WAVELETS K-MEAN ALGORITHM},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={491-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001542804910497},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION BASED ON ADAPTIVE WAVELETS K-MEAN ALGORITHM
SN - 978-989-8111-65-4
AU - K. Abbas A.
AU - Bassam R.
AU - M. Kasim R.
PY - 2009
SP - 491
EP - 497
DO - 10.5220/0001542804910497