Title :
Artificial neural net algorithms in classifying electromyographic signals
Author :
Schizas, C.N. ; Pattichis, C.S. ; Schofield, I.S. ; Fawcett, P.R. ; Middleton, L.T.
Author_Institution :
MDRTC, Neurodiagnostic Unit, Cyprus
Abstract :
Examines how artificial neural nets (ANN) can be used as a computerized method for electromyographic diagnosis. For this reason, a number of well defined neuromuscular disorders have been selected: the Becker´s muscular dystrophy; the spinal muscular atrophy; and the motor neuron disease. In this study the macro motor unit potential shape descriptors form continuous valued inputs, which are used to excite a multilayer perceptron net. Learning is carried out under supervision by providing to the net the desired output
Keywords :
bioelectric potentials; computerised pattern recognition; medical diagnostic computing; muscle; neural nets; patient diagnosis; Becker´s muscular dystrophy; artificial neural nets; computerised pattern recognition; electromyographic diagnosis; medical diagnostic computing; motor neuron disease; motor unit potential; multilayer perceptron net; neuromuscular disorders; spinal muscular atrophy;
Conference_Titel :
Artificial Neural Networks, 1989., First IEE International Conference on (Conf. Publ. No. 313)
Conference_Location :
London
