Associative Memories and Diagnostic Classification of EMG Signals

  • C. Shirota
  • M. Y. Barretto
  • C. Itiki
Conference paper


In this work, associative memories are used for diagnostic classification of needle EMG signals. Vectors containing 44 autoregressive coefficients represent each signal and are presented as stimuli to associative memories. As the number of training stimuli increases, the method recursively updates associative memories. The obtained classification results are equivalent to the ones provided by the traditional Fisher’s discriminant, indicating the feasibility of the proposed method.


Training Group Classification Rate Associative Memory Generalize Inverse Training Stimulus 
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  1. [l]
    Graupe, D., Kordylewski, H. (1995) Artificial Neural Network Control of FES in Paraplegics for Patient Responsive Ambulation. IEEE Trans. Biom. Eng. 42(7): 699–707.CrossRefGoogle Scholar
  2. [2]
    Lee, S., Kim, J., Park, S. (1996) An Enhanced Feature Extraction Algorithm for EMG Pattern Classification. IEEE Trans. on Rehabilitation Engineering 4(4): 439–443.CrossRefGoogle Scholar
  3. [3]
    Gazzoni, M., Farina, D., Merletti, R. (2004) A new method for the extraction and classification of single motor unit action potentials from surface EMG signals. Journal of Neuroscience Methods 136(2): 165–177.CrossRefGoogle Scholar
  4. [4]
    Desmedt, J. E. (1989) Computer-Aided Electromyography and Expert Systems. Elsevier-Science, Amsterdam.Google Scholar
  5. [5]
    Berzuini, C., Maranzana-Figini, M., Bernardinelli, L. (1982) Effective use of EMG parameters in the assessment of neuromuscular diseases. International Journal of Bio-Medical Computing 13:481–499.CrossRefGoogle Scholar
  6. [6]
    Inbar, G. F., Noujaim, A. E. (1984) On Surface EMG spectral characterization and its application to diagnostic classification. IEEE Trans. on Biomedical Engineering 31(9):597–604.Google Scholar
  7. [7]
    Abou-Chadi, F. E., Nashar, A., Saad, M. (2001) Automatic analysis and classification of surface electromyography. Frontiers of Medical and Biological Engineering 11: 13–29.CrossRefGoogle Scholar
  8. [8]
    Shirota, C, Barretto, M. Y., Itiki, C. (2004) Classificação de sinais eletromiográficos de agulha por memórias associativas e modelagem auto-regressiva. Proceedings of the International Federation for Medical and Biological Engineering 5(1): 959–962.Google Scholar
  9. [9]
    Bendat, J. S., Piersol, A. G. (1986) Random Data-Analysis and Measurement Procedures, 2nd ed. John Wiley & Sons, New York.Google Scholar
  10. [10]
    Marple, S. L. (1987) Digital Spectral Analysis: with Applications. Prentice-Hall, Englewood Cliffs.Google Scholar
  11. [11]
    Barretto, M. Y., Kohn, A. F., Itiki, C. (2004) Modelagem auto-regressiva e discriminante de Fisher na classificação de sinais eletromiográficos de agulha. Proceedings of the International Federation for Medical and Biological Engineering 5(1): 939–942.Google Scholar
  12. [12]
    Kandel, E. R., Schwartz, J. H., Jessell, T. M. (1991) Principles of Neural Science, 3rd ed. Elsevier Science, New York.Google Scholar
  13. [13]
    Kohonen, T. (1984) Self-Organization and Associative Memory. Springer-Verlag, Berlin.Google Scholar
  14. [14]
    Graybill, F. A. (1983) Matrices with Applications in Statistics. Wadsworth, Pacific Grove.Google Scholar
  15. [15]
    Nadler, M., Smith, E. P. (1993) Pattern Recognition Engineering. John Wiley, New York.Google Scholar
  16. [16]
    Haykin, S. (1999) Neural Networks: a Comprehensive Foundation, 2nd ed. Prentice Hall, Upper Saddle River.Google Scholar
  17. [17]
    Pattichis, C., Elia, A. G. (1999) Autoregressive and cepstral analyses of motor unit action potentials. Med. Eng. Phys. 21: 405–419.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • C. Shirota
    • 1
  • M. Y. Barretto
    • 1
  • C. Itiki
    • 1
  1. 1.Biomedical Engineering LaboratoryEscola Politécnica da Universidade de São PauloBrazil

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