title: Automatic analysis and classification of surface electromyography.
abstract: In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.
authors.affiliation: Department of Electronics and Communications Engineering, Mansoura University, Egypt. f-abochadi@ieee.org
authors.forename: F E
authors.initials: FE
authors.lastname: Abou-Chadi
authors.forename: A
authors.initials: A
authors.lastname: Nashar
authors.forename: M
authors.initials: M
authors.lastname: Saad
mesh_terms: D000465:Algorithms; D003658:Decision Making, Computer-Assisted; D004576:Electromyography / Q000145:classification / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer