Call for Paper - November 2019 Edition
IJCA solicits original research papers for the November 2019 Edition. Last date of manuscript submission is October 21, 2019. Read More

A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Muzaffar Khan, Jaikaran Singh, Mukesh Tiwari
10.5120/ijca2016907710

Muzaffar Khan, Jaikaran Singh and Mukesh Tiwari. Article: A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders. International Journal of Computer Applications 133(4):13-18, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Muzaffar Khan and Jaikaran Singh and Mukesh Tiwari},
	title = {Article: A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {4},
	pages = {13-18},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy. The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label ( Myopathic , Neuropathic , or Normal ) for a given MUAP. Extensive analysis was performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.

References

  1. Tahereh Kamali, Reza Boostani and Hoossein Parsaei, “A Multi-Classifier Approach to MUAP Classification of Neuromuscular Disorders” IEEE Transaction On Neural Systems And Rehabilitation Vol22 No1 January 2014
  2. Hoosien Parsaei and Daniel .W.Stashul, “EMG Signal Decomposition Using Motor Unit Potential Train Validity” ,IEEE Transaction On Neural Systems And Rehabilitation Vol21 No2 Marchry 2013
  3. Gurmanik Kaur , A. Arora and V. K. Jain , “ EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features”, ACEEE International. Journal. on Electrical and Power Engineering, Vol. 01, No. 03, Dec 2010.
  4. Hossein Parsaei and Daniel W. Stashuk, “EMG Signal Decomposition Using Motor Unit Potential Train Validity”, IEEE Transactions On Neural Systems And Rehabilitation Engineering, Vol. 21, No. 2, March 2013.
  5. Paulito Palmes∗, Wei Tech Ang, Ferdinan Widjaja, Louis CS Tan, and Wing Lok Au, “Pattern Mining of Multichannel sEMG for Tremor Classification” ,IEEE Transactions On Biomedical Engineering, Vol. 57, No. 12, December 2010.
  6. SHANG Xiaojing, TIAN Yantao and LI Yang ,“Feature Extraction and Classification of sEMG Based on ICA and EMD Decomposition of AR Model”,IEEE Conference on Biomedical Engineering 2011 ISN 978-1-4577-0321-8/11
  7. Aaron J. Young, Lauren H and Smith, Elliott . “Classification of Simultaneous Movements Using Surface EMG Pattern Recognition” IEEE Transactions On Biomedical Engineering, Vol. 60, No. 5, May 2013
  8. Sarbast Rasheed, Daniel W. Stashuk and Mohamed S. Kamel, “Integrating Heterogeneous Classifier Ensembles for EMG Signal Decomposition Based on Classifier Agreement” ,IEEE Transactions On Information Technology In Biomedicine, Vol. 14, No. 3, May 2010
  9. Rami N. Khushaba, “Correlation Analysis of Electromyogram (EMG) Signals for Multi-User Myoelectric Interfaces”, IEEE Transactions On Neural Systems And Rehabilitation Engineering, Vol. 11, No. 1, January 2014
  10. Saara M. Rissanen , Markku Kankaanp and Mika P. Tarvainen ,” Analysis of EMG and Acceleration Signals for Quantifying the Effects of Deep Brain Stimulation in Parkinson ’s disease” ,IEEE Transactions On Biomedical Engineering, Vol. 58, No. 9, September 2011.
  11. Sarbast Rasheed, Daniel W. Stashuk, and Mohamed S. Kamel. “A Hybrid Classifier Fusion Approach for Motor Unit Potential Classification During EMG Signal Decomposition”, IEEE Transactions On Biomedical Engineering, Vol. 54, No. 9, September 2007
  12. Todd R. Farrell and Richard F. ff. Weir, “A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control.”, IEEE Transactions On Biomedical Engineering, Vol. 55, No. 9, September 2008.
  13. E. R. Kandel, J. H. Schwartz, and T. M. Jessell, “Principles of Neural Science”, 4 edition. New York: McGraw-Hill, 2000.
  14. L. J. Pino, D. W. Stashuk, S. G. Boe, and T. J. Doherty, “Motor unit potential characterization using pattern discovery” ,Med. Eng. Phys.,vol. 30, pp. 563–573, 2008.
  15. Hossein Parsaei and Daniel W. Stashuk “SVM-Based Validation of Motor Unit Potential Trains Extracted by EMG Signal Decomposition” IEEE Transactions On Biomedical Engineering, Vol. 59, No. 1, January 2012
  16. K. Englehart, B. Hudgins, and A. Philip, “A wavelet-based continuous classification scheme for multifunction myoelectric control,” IEEE Trans. Biomed. Eng., vol. 48, no. 3, pp. 302–311, 2001.
  17. Carlo J. De Luca , L. Donald Gilmore , “ Filtering the surface EMG signal: Movement artifact and baseline noise contamination”, Journal of Biomechanics 43 (2010) 1573–1579 Science Direct
  18. A. Subasi, “Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines”. Comput. Biol. Med., vol. 42, pp. 806–815, 2012.
  19. J. V. Basmajian and C. J. de Luca, “Muscles Alive: Their Functions Revealed by Electromyography”, 5 ed. Philadelphia, PA: William Wilkins, 1985.
  20. E. R. Kandel, J. H. Schwartz, and T. M. Jessell,” Principles of Neural”, 2 Science, 4 edition. New York: McGraw-Hill, 2000.
  21. G. L. Sheean, “Quantification of motor unit action potential energy”, Clin. neurophysiol vol. 123, no. 3, pp. 621–625, Mar. 2012
  22. Gurmanik kaur, A. S. Arora and V. K. Jain, “Comparison of the techniques used for segmentation of EMG signals,” in Proc. WSEAS Int. Conf. on Mathematical and Computational Methods, Baltimore, USA, pp. 124-129, 2009.
  23. Daniel Zennaro, Peter Wellig, “A Software Package for the Decomposition of Long-Term Multichannel EMG Signals Using Wavelet Coefficients”, IEEE Transactions On Biomedical Engineering, Vol. 50, No. 1, January 2003
  24. Constantinos S. Pattichis and Marios S. Pattichis , “Time-Scale Analysis of Motor Unit Action Potentials ,” IEEE Transactions On Biomedical Engineering, Vol. 46, No. 11, November 1999.
  25. A. B. M. S. U. Doulah , S. A. Fattah , W.P. Zhu and M. O. Ahmad , “ Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification .” IEEE Transactions On Biomedical Circuits And Systems, Vol. 8, No. 2, April 2014
  26. H. P. Clamann, “Statistical analysis of motor unit firing patterns in a human skeletal muscle,” Biophys. J., vol. 9, no. 10, pp. 1233–1251, Oct. 1969.
  27. C. T. Moritz, B. K. Barry,M. A. Pascoe, and R.M. Enoka, “Discharge rate variability influences the variation in force fluctuations across the working range of a hand muscle,” J. Neurophysiol., vol. 93, no. 5, pp. 2449–2459, May 2005.
  28. S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited: A stepwise procedure for building and training a neural network,” in Neurocomputing: Algorithms, Architectures and Applications, J. Fogelman, Ed. New York: Springer-Verlag, 1990.
  29. L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: A case study in handwriting digit recognition,” in Proc. Int. Conf. Pattern Recognit., 1994, pp. 77–87.
  30. Dudani.S.A, “The Distance weighted K-Nearest Rule”, IEEE Transaction On System Man &Cybernetic 6,325-327 (1976)
  31. SHANG Xiaojing, TIAN Yantao and LI Yang “Feature Extraction and Classification of sEMG Based on ICA and EMD Decomposition of AR Model”, IEEE 978-1-4577-0321-8/11 ©2011
  32. Xu Zhang and Ping Zhou, “High-Density Myoelectric Pattern Recognition Toward Improved Stroke Rehabilitation”, IEEE Transactions On Biomedical Engineering, Vol. 59, No. 6, June 20

Keywords

Support Vector Machine, EMG; Discrete wavelet Transform; K-nearest neighborhood (KNN)