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

Volumn 8, Issue 2, 2014, Pages 155-164

  Doulah, A B M S U ^a   Fattah, S A ^a   Zhu, W P ^b   Ahmad, M O ^b  

^a BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Bangladesh)

^b Civil and Environmental Engineering   (Canada)

Author keywords

Amyotrophic lateral sclerosis (ALS); discrete wavelet transform; electromyography (EMG); feature extraction; K nearest neighborhood (KNN) classifier; motor unit action potentials (MUAP); myopathy

Indexed keywords

DISCRETE WAVELET TRANSFORMS; ELECTROPHYSIOLOGY; FEATURE EXTRACTION; NEUROMUSCULAR REHABILITATION; NEUROPHYSIOLOGY; TEMPLATE MATCHING;

AMYOTROPHIC LATERAL SCLEROSIS; CLASSIFICATION ACCURACY; DECOMPOSITION TECHNIQUE; K-NEAREST NEIGHBORHOODS; MOTOR UNIT ACTION POTENTIALS; MYOPATHY; STATISTICAL PROPERTIES; WAVELET DOMAIN FEATURES;

ELECTROMYOGRAPHY;

ACTION POTENTIAL; ADULT; AGED; ALGORITHM; AMYOTROPHIC LATERAL SCLEROSIS; CASE CONTROL STUDY; CLASSIFICATION; ELECTROMYOGRAPHY; FEMALE; HUMAN; MALE; MIDDLE AGED; MOTONEURON; MUSCLE DISEASE; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; WAVELET ANALYSIS; YOUNG ADULT;

ACTION POTENTIALS; ADULT; AGED; ALGORITHMS; AMYOTROPHIC LATERAL SCLEROSIS; CASE-CONTROL STUDIES; ELECTROMYOGRAPHY; FEMALE; HUMANS; MALE; MIDDLE AGED; MOTOR NEURONS; MUSCULAR DISEASES; WAVELET ANALYSIS; YOUNG ADULT;

EID: 84902243578     PISSN: 19324545     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBCAS.2014.2309252     Document Type: Article

References (27)

1. H. Christensen and A. Fuglsang-Frederiksen, "Power spectrum and turns analysis of EMG at different voluntary efforts in normal subjects," Electromyogr. Clin. Neurophysiol., vol. 64, no. 8, pp. 528-535, Aug. 1986.
2. A. Fuglsang-Frederiksen, "The utility of interference pattern analysis," Muscle and Nerve, vol. 23, no. 1, pp. 18-36, Jan. 2000.
3. T. Y. Fukuda, J. O. Echeimberg, J. E. Pompeu, P. R. G. Lucareli, S. Garbelotti, R. O. Gimenes, andA.Apolinario, "Rootmean square value of the electromyographic signal in the isometric torque of the quadriceps, hamstrings and brachial biceps muscles in female subjects," J. Appl. Res., vol. 10, no. 1, pp. 32-39, 2010.
4. K. Tohru, "Investigation of parametric analysis of dynamic EMG signals by a muscle-structured stimulation study," IEEE Trans. Biomed. Eng., vol. 39, no. 3, pp. 280-288, Mar. 1992.
5. F. G. Nihal and K. Sabri, "Classification of EMG signals using PCA and FFT," J Med. Syst., vol. 29, no. 3, pp. 15-25, 2005.
6. H. J. Hermens, T. A.M. Bruggena, C. T. M. Batena,W. L. C. Rutten, and H. B. K. Boom, "The median frequency of the surface EMG power spectrum in relation to motor unit firing and action potential properties," J. Electromyogr. Kinesiol., vol. 2, no. 1, pp. 15-25, 1992.
7. M. Bernardi, F. Felici,M. Marchetti, F.Montellanico,M. F. Piacentini, andM. Solomonow, "Force generation performance and motor unit recruitment strategy in muscles of contralateral limbs," J. Electromyogr. Kinesiol., vol. 9, pp. 121-130, 1999.
8. E. W. Abel, H.Meng, A. Forster, and D. Holder, "Singularity characteristics of needle EMG IP signals," IEEE Trans. Biomed. Eng., vol. 53, no. 2, pp. 219-225, Feb. 2006.
9. Y. Bentaleb, "An algorithm of wavelets for the pretreatment of EMG biomedical signals," Int. J. Contemp. Eng. Sci., vol. 3, no. 6, pp. 285-294, 2010.
10. 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.
11. A. Subasi, "Classification of EMG signals using combined features and soft computing techniques," Appl. Soft Comput., vol. 12, no. 8, pp. 2188-2198, Aug. 2012.
12. A. Subasi, "Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines," Comput. Biol. Med., vol. 42, no. 8, pp. 806-815, Aug. 2012.
13. A. Subasi, "Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders," Comput. Biol. Med., vol. 43, no. 5, pp. 576-586, June 2013.
14. A. O. Andrade, S. Nasuto, P. Kyberd, C. M. Sweeney-Reed, and F. R. V. Kanijn, "EMG signal filtering based on empirical mode decomposition," Biomed. Signal Process. Control, vol. 1, no. 1, pp. 44-55, Jan. 2006.
15. T. Grujic and A. Kuzmanic, "Denoising of surface EMG signals: A comparison of wavelet and classical digital filtering procedures," Technol. Healthcare, vol. 12, no. 2, pp. 130-135, 2004.
16. S. A. Fattah, A. B. M. S. U. Doulah, M. A. Iqbal, C. Shahnaz, W.-P. Zhu, and M. O. Ahmad, "Identification of motor neuron disease using wavelet domain features extracted from EMG signal," in Proc. IEEE Int. Conf. Circuits and Systems, 2013, pp. 1308-1311.
17. G. Pfeiffer and K. Kunze, "Discriminant classification of motor unit potentials MUPs successfully separates neurogenic and myogenic conditions. A comparison of multi and univariate diagnostic algorithms forMUP analysis," Electrocephalogr. Clin. Neurophysiol., vol. 97, pp. 191-207, 1995.
18. C. R. Stewart, S. D. Nandedkar, J. M. Massey, J. M. Gilchrist, P. E. Barkhaus, and D. B. Sanders, "Evaluation of an automatic method of measuring features of motor unit action potentials," Muscle and Nerve, vol. 12, no. 2, pp. 18-36, Feb. 1989.
19. E. Stalberg, S. Andreassen, B. Falck, H. Lang, A. Rosenfalck, and W. Trojaborg, "Quantitative analysis of individualmotor unit potentials: A proposition for standardized technology and criteria for measurement," J. Clin. Neurophysiol., vol. 3, no. 4, pp. 313-348, Dec. 1986.
20. C. S. Pattichis, C. N. Schizas, and L. T. Middleton, "Neural network models in EMG diagnosis," IEEE Trans. Biomed. Eng., vol. 42, no. 5, pp. 486-496, May 1995.
21. C. D. Katsis, T. Exarchos, C. Papaloukas, Y. Goletsis, D. I. Fotiadis, and I. Sarmas, "A two-stage method for MUAP classification based on EMG decomposition," Comput. Biol. Med., vol. 31, no. 9, pp. 1232-1240, Jan. 2007.
22. C. S. Pattichis and M. S. Pattichis, "Time-scale analysis of motor unit action potentials," IEEE Trans. Biomed. Eng., vol. 46, no. 11, pp. 1320-1329, Nov. 1999.
23. E. Stalberg, C. Bischoff, and B. Falck, "Outliers, a way to detect abnormality in quantiative EMG," Muscle and Nerve, vol. 17, pp. 392-399, 1994.
24. R. Gut and G. S. Moschytz, "High-precision EMG signal decomposition using communication techniques," IEEE Trans. Signal Process., vol. 48, no. 9, pp. 2487-2494, Sep. 2000.
25. K. C. McGill, Z. C. Lateva, and H. R. Marateb, "EMGLAB: An interative EMG decomposition program," J. Neurosci. Methods, vol. 149, no. 2, pp. 121-133, Dec. 2005.
26. G. L. Sheean, "Quantification of motor unit action potential energy," Clin. Neurophysiol., vol. 123, no. 3, pp. 621-625, Mar. 2012.
27. M. Nikolic, "Detailed analysis of clinical electromyography signals EMG decomposition, findings and firing pattern analysis in controls and patientswith myopathy and amytrophic lateral sclerosis" Ph.D. dissertation, Faculty of Health Science, Univ. Copenhagen, Copenhagen, Denmark, 2001 [Online]. Available: http://www.emglab.net, data are available as dataset N2001.