Cross-validated classification of intracranial sources extracted by BET-ART method

dc.contributor.author	Βάσιος, Χρήστος	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.contributor.author	Κονταξάκης, Βασίλειος Π.	el
dc.date.accessioned	2015-01-25T22:00:04Z
dc.date.issued	2015-01-26
dc.identifier.uri	http://hdl.handle.net/11400/4730
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Electroencephalography
dc.subject	Bioelectric potentials
dc.subject	Ηλεκτροεγκεφαλογραφία
dc.subject	Βιοηλεκτρικό δυναμικό
dc.title	Cross-validated classification of intracranial sources extracted by BET-ART method	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 2nd International IEEE-EMBS Conference on Neural Engineering	en
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://skos.um.es/unescothes/C02465
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85042138
heal.contributorName	Νικήτα, Κωνσταντίνα Σ.	el
heal.contributorName	Ουζούνογλου, Νικόλαος Κ.	el
heal.identifier.secondary	DOI: 10.1109/CNE.2005.1419573
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Vasios , C., Matsopoulos, G., Ventouras, E., Papageorgiou, C., Kontaxakis, V., et al. (2005). Cross-validated classification of intracranial sources extracted by BET-ART method. In the 2nd International IEEE-EMBS Conference on Neural Engineering. pp. 140-143. IEEE Engineering in Medicine and Biology Society: Arlington, 16th-19th March 2005.	en
heal.abstract	In the present paper, a new methodological approach, for the classification of first episode schizophrenic patients (FES) against normal controls, is proposed. The first step of the methodology applied is the feature extraction, which is based on the combination of the multivariate autoregressive model with the simulated annealing technique, in order to extract optimum features, in terms of classification rate. The classification, as the second step of the methodology, is implemented by means of an artificial neural network (ANN) trained with the back-propagation algorithm under "leave-one-out cross-validation". The ANN is a multi-layer perceptron, the architecture of which, is selected after a detailed search. The proposed methodology has been applied for the classification of FES patients and normal controls using as input signals the Intracranial current sources obtained by the inversion of ERPs using an algebraic reconstruction technique. Results by implementing the proposed methodology provide classification rates of up to 93.1%	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	International IEEE-EMBS Conference on Neural Engineering	en
heal.conferenceItemType	full paper