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
Link To Document