dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Schizas, Christos N. | en |
dc.contributor.author | Middleton, Lefkos T. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Schizas, Christos N. | en |
dc.creator | Middleton, Lefkos T. | en |
dc.date.accessioned | 2019-11-13T10:41:56Z | |
dc.date.available | 2019-11-13T10:41:56Z | |
dc.date.issued | 1995 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54813 | |
dc.description.abstract | In the past years, several computer-aided quantitative motor unit action potential (MUAP) techniques were reported. It is now possible to add to these techniques the capability of automated medical diagnosis so that all data can be processed in an integrated environment. In this study, the parametric pattern recognition (PPR) algorithm that facilitates automatic MUAP feature extraction and Artificial Neural Network (ANN) models are combined for providing an integrated system for the diagnosis of neuromuscular disorders. Two paradigms of learning for training ANN models were investigated, supervised, and unsupervised. For supervised learning, the back propagation algorithm and for unsupervised learning, the Kohonen's selforganizing feature maps algorithm were used. Diagnostic yield for models trained with both procedures was similar and on the order of 80%. However, back propagation models required considerably more computational effort compared to the Kohonen's self-organizing feature map models. Poorer diagnostic performance was obtained when the K-means nearest neighbor clustering algorithm was applied on the same set of data. © 1995 IEEE | en |
dc.source | IEEE Transactions on Biomedical Engineering | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029294170&doi=10.1109%2f10.376153&partnerID=40&md5=6e098ec86ddc345853d9fa3d4ec5ebf8 | |
dc.subject | Mathematical models | en |
dc.subject | learning | en |
dc.subject | article | en |
dc.subject | Algorithms | en |
dc.subject | Feature extraction | en |
dc.subject | Neural networks | en |
dc.subject | human | en |
dc.subject | controlled study | en |
dc.subject | algorithm | en |
dc.subject | clinical article | en |
dc.subject | human tissue | en |
dc.subject | Diagnosis | en |
dc.subject | myopathy | en |
dc.subject | Neurology | en |
dc.subject | cluster analysis | en |
dc.subject | artificial intelligence | en |
dc.subject | computer model | en |
dc.subject | motor neuron disease | en |
dc.subject | Neuromuscular disorders | en |
dc.subject | Electromyography | en |
dc.subject | Unsupervised learning | en |
dc.subject | computer assisted diagnosis | en |
dc.subject | neuromuscular disease | en |
dc.subject | biceps brachii muscle | en |
dc.subject | motor unit potential | en |
dc.subject | Motor unit action potential techniques | en |
dc.subject | Parametric pattern recognition | en |
dc.subject | pattern recognition | en |
dc.subject | Supervised learning | en |
dc.title | Neural Network Models in EMG Diagnosis | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/10.376153 | |
dc.description.volume | 42 | |
dc.description.issue | 5 | |
dc.description.startingpage | 486 | |
dc.description.endingpage | 496 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :91</p> | en |
dc.source.abbreviation | IEEE Trans.Biomed.Eng. | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Schizas, Christos N. [0000-0001-6548-4980] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0001-6548-4980 | |