Intracranial current signals classification using multivariate autoregressive modeling and simulated annealing technique

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	Νικήτα, Κωνσταντίνα Σ.	el
dc.date.accessioned	2015-01-28T14:27:20Z
dc.date.issued	2015-01-28
dc.identifier.uri	http://hdl.handle.net/11400/4938
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	Brain
dc.subject	Cathode ray oscillographs
dc.subject	Εγκέφαλος
dc.subject	Κυματομορφές
dc.title	Intracranial current signals classification using multivariate autoregressive modeling and simulated annealing technique	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the International Conference on Biomedical Engineering of the IASTED (International Association of Science and Technology for Development)	en
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρικά όργανα και εξοπλισμός
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-Ιατρικά όργανα και εξοπλισμός
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85021030
heal.contributorName	Κονταξάκης, Βασίλειος Π.	el
heal.contributorName	Χριστοδούλου, Γιώργος Ν.	el
heal.contributorName	Ουζούνογλου, Νικόλαος Κ.	el
heal.contributorName	Hamza, M.H. (Ed.)	en
heal.identifier.secondary	ISBN: 0-88986-353-9
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.bibliographicCitation	Vasios, C., Matsopoulos, G., Ventouras, E., Papageorgiou, C., Nikita, K., et al. (2003). Intracranial current signals classification using multivariate autoregressive modeling and simulated annealing technique. In the International Conference on Biomedical Engineering of the IASTED (International Association of Science and Technology for Development). pp. 33-38. IASTED: Salzburg, 2003.	en
heal.abstract	Intracranial currents computed by the scalp-recorded ERPs, provide information on the non-observable electrical phenomena taking place in the brain, related to the cognitive mechanisms induced by the experimental task used in the ERP recording procedure. The use of current source waveforms, as input in classification systems, may provide robust classifiers due to the immediate relationship of the current sources to the brain electrical activity related to cognitive mechanisms. In the present work, a new method for the classification of intracranial current sources is proposed, combining the Multivariate Autoregressive model with the Simulated Annealing technique, in order to extract optimum features, in terms of the classification rate. The classification is implemented using a three-layer neural network (NN) trained with the back-propagation algorithm. The system was applied in the classification of normal controls and schizophrenic patients, providing classification rates of up to 100%. Furthermore, the clustering of intracranial source locations providing best classification performance may indicate relationships between the brain areas corresponding to these locations and pathological mechanisms.	en
heal.publisher	IASTED	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Biomedical Engineering of the IASTED	en
heal.conferenceItemType	full paper