• DocumentCode
    1254624
  • Title

    Artificial neural nets in computer-aided macro motor unit potential classification

  • Author

    Schizas, C.N. ; Pattichis, C.S. ; Schofield, I.S. ; Fawcett, P.R. ; Middleton, L.T.

  • Author_Institution
    MDRTC Neuromuscular Unit, Makarios Hospital, Nicosia, Cyprus
  • Volume
    9
  • Issue
    3
  • fYear
    1990
  • Firstpage
    31
  • Lastpage
    38
  • Abstract
    The use of macro electromyography to obtain a macro motor unit potential (MMUP) is described. At least 20 potentials are measured from a single muscle to obtain a reasonable estimate of the parameters of an average motor unit potential. The MMUP data are analyzed by means of the peak-to-peak amplitude and the integral of the central 50 ms of the signal. The possibility of using artificial neural networks (ANNs) to analyze the macro data in a way that makes no assumptions about the relationships between the parameters and without recourse to conventional modeling methods is discussed. The results of an analysis carried out on 820 MMUPs recorded from 41 subjects who were classified on the basis of a clinical opinion and the appearance of a muscle biopsy are presented and discussed.<>
  • Keywords
    bioelectric potentials; medical diagnostic computing; muscle; neural nets; artificial neural networks; clinical opinion; computer-aided macro motor unit potential classification; integral; macro electromyography; muscle biopsy; peak-to-peak amplitude; single muscle; Artificial neural networks; Electrodes; Electromyography; Filters; Measurement units; Muscles; Needles; Nerve fibers; Neuromuscular; Neurons;
  • fLanguage
    English
  • Journal_Title
    Engineering in Medicine and Biology Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0739-5175
  • Type

    jour

  • DOI
    10.1109/51.59210
  • Filename
    59210