SpringerLink
Log in

Pattern Classification Techniques for EMG Signal Decomposition

  • Chapter
  • First Online:
Advanced Biosignal Processing

  • 2461 Accesses

Abstract

The electromyographic (EMG) signal decomposition process is addressed by develo** different pattern classification approaches. Single classifier and multiclassifier approaches are described for this purpose. Single classifiers include: certainty-based classifiers, classifiers based on the nearest neighbour decision rule: the fuzzy k-NN classifiers, and classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers. Multiple classifier approaches aggregate the decision of the heterogeneous classifiers aiming to achieve better classification performance. Multiple classifier systems include: one-stage classifier fusion, diversity-based one-stage classifier fusion, hybrid classifier fusion, and diversity-based hybrid classifier fusion schemes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alexandre L A, Campilho A C and M. Kamel (2001) On combining classifiers using sum and product rules. Pattern Recognition Letters, 22:1283–1289

    Article  MATH  Google Scholar 

  2. Andreassean S (1987) Methods for computer-aided measurement of motor unit parameters. The London Symposia – Supplement 39 to Electroencephalography and Clinical Neurophysiology, 13–20

    Google Scholar 

  3. Atiya A F (1992) Recognition of multiunit neural signals. IEEE Transactions on Biomedical Engineering, 39(7):723–729

    Article  Google Scholar 

  4. Brunelli R and Falavigna D (1995) Person identification using multiple cues. IEEE Transactions in Pattern Analysis and Machine Intelligence, 17(10):955–966

    Article  Google Scholar 

  5. Chauvet E, Fokapu O, Hogrel J Y et al (2003) Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques. Medical & Biological Engineering & Computing, 41:646–653

    Article  Google Scholar 

  6. Cho Sung-Bae and Kim J H (1995) Combining multiple neural networks by fuzzy integral for robust classification. IEEE Transactions on Systems Man and Cybernetics, 25(2):380–384

    Article  Google Scholar 

  7. Cho Sung-Bae and Kim J H (1995) Multiple network fusion using fuzzy logic. IEEE Transactions on Neural Networks, 6(2):497–501

    Article  Google Scholar 

  8. Cohen J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(2):37–46

    Article  Google Scholar 

  9. Duda R O, Hart P E and Stork D (2001) Pattern Classification. John Wiley & Sons, 2nd edition

    MATH  Google Scholar 

  10. Duin R (2002) The combining classifier: to train or not to train? In Proceedings of the 16th International Conference on Pattern Recognition, 2:765–770

    Google Scholar 

  11. Duin R and Taz D (2000) Experiments with classifier combining rules. In J Kittler and F Roli, editors, Multiple Classifier Systems, Lecture Notes in Computer Science, 1857:16–29, Gagliari, Italy, Springer

    Google Scholar 

  12. Etawil H A Y (1994) Motor unit action potentials: discovering temporal relations of their trains and resolving their superpositions. Master’s thesis, University of Waterloo.,Waterloo, Ontario, Canada.

    Google Scholar 

  13. Fang J, Agarwal G and Shahani B (1999) Decomposition of multiunit electromyogram signals. IEEE Transactions on Biomedical Engineering, 46(6):685–697

    Article  Google Scholar 

  14. Fleiss J L, Levin B and Paik M C (2003) Statistical Methods for Rates and Proportions. John Wiley & Sons, 3rd edition

    Book  MATH  Google Scholar 

  15. Florestal J R, Mathieu P A and Malanda A (2004) Automatic decomposition of simulated EMG signals. In Proceedings of the 28th Conference of the Canadian Medical and Biological Engineering Society, 29–30

    Google Scholar 

  16. Florestal J R, Mathieu P A and Malanda A (2006) Automated decomposition of intramuscular electromyographic signals. IEEE Transactions on Biomedical Engineering, 53(5):832–839

    Article  Google Scholar 

  17. Florestal J R, Mathieu P A and Palmondon R (2007) A genetic algorithm for the resolution of superimposed motor unit action potentials. IEEE Transactions on Biomedical Engineering, 54(12):2163–2171

    Article  Google Scholar 

  18. Giacinto G and Roli F (2001) An approach to the automatic design of multiple classifier systems. Pattern Recognition Letters, 22:25–33

    Article  Google Scholar 

  19. Gut R and Moschytz G S (2000) High-precision EMG signal decomposition using communication techniques. IEEE Transactions on Signal Processing, 48(9):2487–2494

    Article  Google Scholar 

  20. Hamilton-Wright A and Stashuk D W (2005) Physiologically based simulation of clinical EMG signals. IEEE Transactions on Biomedical Engineering, 52(2):171–183

    Article  Google Scholar 

  21. Hassoun M H, Wang C and Spitzer R (1994) Nnerve: Neural network extraction of repetitive vectors for electromyography – part i: algorithm. IEEE Transactions on Biomedical Engineering, 41(11):1039–1052

    Article  Google Scholar 

  22. Hassoun M H, Wang C and Spitzer R (1994). Nnerve: Neural network extraction of repetitive vectors for electromyography – part ii: performance analysis. IEEE Transactions on Biomedical Engineering, 41(11):1053–1061

    Article  Google Scholar 

  23. Kamel M S and Wanas N M (2003) Data dependence in combining classifiers. In T.Windeatt and F. Roli, editors, Multiple Classifier Systems, Lecture Notes in Computer Science, 2790: 1–14 Guilford UK Springer

    Google Scholar 

  24. Keller J M, Gray M R, and Givens J A (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Systems Man and Cybernetics, 15(4):580–585

    Article  Google Scholar 

  25. Kittler J M, Hatef M, Duin R P V et al. (1998) On combining classifiers. IEEE Transactions in Pattern Analysis and Machine Intelligence, 20(3):955–966

    Google Scholar 

  26. Lam L and Suen C Y (1997) Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transaction on Systems Man and Cybernetics – Part A: Systems and Humans, 27(5):553–568

    Article  Google Scholar 

  27. LeFever R S and De Luca C J (1982) A procedure for decomposing the myoelectric signal into its constituent action potentials – part i: technique, theory, and implementation. IEEE Transactions on Biomedical Engineering, 29(3):149–157

    Article  Google Scholar 

  28. Loudon G H, Jones N B and Sehmi A S (1992) New signal processing techniques for the decompositon of emg signals. Medical & Biological Engineering & Computing, 30(11): 591–599

    Article  Google Scholar 

  29. McGill K C (1984) A method for quantitating the clinical electromyogram. PhD dissertation, Stanford University, Stanford, CA

    Google Scholar 

  30. McGill K C, Cummins K and Dorfman L J (1985) Automatic decomposition of the clinical electromyogram. IEEE Transactions on Biomedical Engineering, 32(7):470–477

    Article  Google Scholar 

  31. Mirfakhraei K and Horch K (1997) Recognition of temporally changing action potentials in multiunit neural recordings. IEEE Transactions on Biomedical Engineering, 44(2):123–131

    Article  Google Scholar 

  32. Hamid Nawab S, Wotiz R and De Luca C J (2004) Improved resolution of pulse superpositions in a knowledge-based system EMG decomposition. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1: 69–71

    Google Scholar 

  33. Nikolic M, Sørensen J A, Dahl K et al. (1997) Detailed analysis of motor unit activity. In Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference, 1257–1260

    Google Scholar 

  34. Paoli G M (1993) Estimating certainty in classification of motor unit action potentials. Master’s thesis, University of Waterloo., Waterloo, Ontario, Canada

    Google Scholar 

  35. Partridge D and Yates W B (1996) Engineering multiversion neural-net systems. Neural Computation, 8:869–893

    Article  Google Scholar 

  36. Rasheed S (2006) A multiclassifier approach to motor unit potential classification for EMG signal decomposition. Ph. D. dissertation, URL: http://etd.uwaterloo.ca/etd/srasheed2006.pdf, University of Waterloo, Waterloo, Ontario, Canada

  37. Rasheed S (2008) Diversity-Based Hybrid Classifier Fusion: A Practical Approach to Motor Unit Potential Classification for Electromyographic Signal Decomposition. VDM Verlag Dr. Müller, Berlin, Germany

    Google Scholar 

  38. Rasheed S, Stashuk D and Kamel M (2004) Multi-classification techniques applied to EMG signal decomposition. In Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 04, 2:1226–1231, The Hugue, The Netherland

    Google Scholar 

  39. Rasheed S, Stashuk D and Kamel M (2006) Adaptive certainty-based classification for decomposition of EMG signals. Medical & Biological Engineering & Computing, 44(4):298–310

    Google Scholar 

  40. Rasheed S, Stashuk D and Kamel M (2006) Adaptive fuzzy k-NN classifier for EMG signal decomposition. Medical Engineering & Physics 28(7):694–709

    Article  Google Scholar 

  41. Rasheed S, Stashuk D and Kamel M (2008) Fusion of multiple classifiers for motor unit potential sorting. Biomedical Signal Processing and Control, 3(3):229–243

    Google Scholar 

  42. Rasheed S, Stashuk D and Kamel M (2008). A software package for motor unit potential classification using fuzzy k-NN classifier. Computer Methods and Programs in Biomedicine, 89:56–71

    Article  Google Scholar 

  43. Rasheed S, Stashuk D and Kamel M (2009) Integrating heterogeneous classifier ensembles for EMG signal decomposition based on classifier agreement. Accepted for publication in IEEE Transactions on Information Technology in Biomedicine and now is published on-line at http://www.ieeexplore.ieee.org

  44. Rasheed S, Stashuk D and Kamel M (2007) A hybrid classifier fusion approach for motor unit potential classification during EMG signal decomposition. IEEE Transactions on Biomedical Engineering, 54(9):1715–1721

    Article  Google Scholar 

  45. Rasheed S, Stashuk D and Kamel M (2008) Diversity-based combination of non-parametric classifiers for EMG signal decomposition. Pattern Analysis & Applications, 11:385–408

    Google Scholar 

  46. Stashuk D W (1999) Decomposition and quantitative analysis of clinical electromyographic signals. Medical Engineering & Physics, 21:389–404

    Article  Google Scholar 

  47. Stashuk D W (2001) EMG signal decomposition: how can it be accomplished and used? Journal of Electromyography and Kinesiology, 11:151–173

    Article  Google Scholar 

  48. Stashuk D W and de Bruin H (1988) Automatic decomposition of selective needle-detected myoelectric signals. IEEE Transactions on Biomedical Engineering, 35(1):1–10

    Article  Google Scholar 

  49. Stashuk D W and Paoli G (1998) Robust supervised classification of motor unit action potentials. Medical & Biological Engineering & Computing, 36(1):75–82

    Google Scholar 

  50. Stefano L D and Mattoccia S (2003) Fast template matching using bounded partial correlation. Machine Vision and Applications, 13:213–221

    Article  Google Scholar 

  51. Sugeno M (1977) Fuzzy measures and fuzzy integrals - a survey. In Fuzzy Automata and Decision Processes 89–102 North-Holland, Amsterdam

    Google Scholar 

  52. Xu L, Krzyzak A and Suen C Y (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transaction on Systems Man and Cybernetics, 22(3):418–435

    Article  Google Scholar 

  53. Zennaro D, Wellig P, Koch V. M et al. (2003) A software package for the decomposition of long-term multi-channel EMG signals using wavelet coefficients. IEEE Transactions on Biomedical Engineering, 50(1):58–69

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

    Sarbast Rasheed & Daniel Stashuk

Authors
  1. Sarbast Rasheed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Stashuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarbast Rasheed .

Editor information

Editors and Affiliations

  1. Labo. Images, Signaux et, Systèmes Intelligents (LISSI) EA 3956, Université Paris 12, av. du Général de Gaille 61, Créteil, 94010, France

    Amine Naït-Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Rasheed, S., Stashuk, D. (2009). Pattern Classification Techniques for EMG Signal Decomposition. In: Naït-Ali, A. (eds) Advanced Biosignal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89506-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89506-0_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89505-3

  • Online ISBN: 978-3-540-89506-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation