Cross-validation and neural network architecture selection for the classification of intracranial current sources

dc.contributor.author	Βάσιος, Χρήστος	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Νικήτα, Κωνσταντίνα Σ.	el
dc.contributor.author	Ουζούνογλου, Νικόλαος Κ.	el
dc.date.accessioned	2015-01-26T12:37:19Z
dc.date.issued	2015-01-26
dc.identifier.uri	http://hdl.handle.net/11400/4769
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	VAR model
dc.subject	Bioelectric potentials
dc.subject	Αυτοπαλίνδρομες διαδικασίες
dc.subject	Βιοηλεκτρικό δυναμικό
dc.title	Cross-validation and neural network architecture selection for the classification of intracranial current sources	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 7th Seminar on Neural Network Applications in Electrical Engineering (NEUREL-2004)	en
heal.classification	Medicine
heal.classification	Ιατρική
heal.classification	Neural computers	en
heal.classification	Νευρωνικά δίκτυα (Επιστήμη των υπολογιστών)
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh87008041
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Νευρωνικά δίκτυα (Επιστήμη των υπολογιστών)
heal.keywordURI	http://zbw.eu/stw/descriptor/19573-0
heal.identifier.secondary	ISBN: 0-7803-8547-0/04
heal.identifier.secondary	DOI: 10.1109/NEUREL.2004.1416561
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	2004
heal.bibliographicCitation	Vasios, C., Matsopoulos, G., Ventouras, E., Nikita, K. and Uzunoglu, N. (2004). Cross-validation and neural network architecture selection for the classification of intracranial current sources. In the 7th Seminar on Neural Network Applications in Electrical Engineering. pp. 151-158. Belgrade, 23th-25th September2004.	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 backpropagation algorithm under "leave-one-out cross-validation". The ANN is a multilayer 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 event-related potentials (ERP) using an algebraic reconstruction technique. Results implementing the proposed methodology provide classification rates of up to 93%.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	Seminar on Neural Network Applications in Electrical Engineering	en
heal.conferenceItemType	full paper