Title :
A computerized system for SEMG signals analysis and classifieation
Author :
El-Daydamony, E.M. ; El-Gayar, M. ; Abou-Chadi, F.
Author_Institution :
Delta Higher Inst. for Comput., Mansoura
Abstract :
In this paper, Hidden Markov Model of surface electromyography (EMG) algorithm that facilitates automatic SEMG feature extraction and artificial neural network (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of neuromuscle disorders. The investigated ANN were: the multilayer backpropagation algorithm. The percentage of correct classification reaches 90.91%. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.
Keywords :
backpropagation; electromyography; feature extraction; hidden Markov models; medical signal processing; neural nets; neurophysiology; patient diagnosis; signal classification; SEMG signal analysis; artificial neural network; automatic SEMG feature extraction; computerized system; hidden Markov model; multilayer backpropagation algorithm; neuromuscle disorder diagnosis; signal classification; surface electromyography algorithm; Artificial neural networks; Backpropagation algorithms; Diseases; Electromyography; Hidden Markov models; Multi-layer neural network; Muscles; Shape; Signal analysis; Signal processing algorithms; Biological signal processing; automatic classification; hidden markov model; neural network; surface electromyography;
Conference_Titel :
Radio Science Conference, 2008. NRSC 2008. National
Conference_Location :
Tanta
Print_ISBN :
978-977-5031-95-2
DOI :
10.1109/NRSC.2008.4542388
