Design and implementation of a multi-PNN structure for discriminating one-month abstinent heroin addicts from healthy controls using the P600 component of ERP signals

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.contributor.author	Λιάππας, Ιωάννης Α.	el
dc.date.accessioned	2015-02-02T18:36:37Z
dc.date.available	2015-02-02T18:36:37Z
dc.date.issued	2015-02-02
dc.identifier.uri	http://hdl.handle.net/11400/5515
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.source	http://www.elsevier.com	en
dc.subject	Heroin addict
dc.subject	Pattern recognition
dc.subject	Ηρωινομανής
dc.subject	Αναγνώριση προτύπων
dc.title	Design and implementation of a multi-PNN structure for discriminating one-month abstinent heroin addicts from healthy controls using the P600 component of ERP signals	en
heal.type	journalArticle
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://skos.um.es/unescothes/C02924
heal.contributorName	Νικολάου, Χρυσούλα	el
heal.contributorName	Ραμπαβίλας, Ανδρέας Δ.	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.identifier.secondary	doi:10.1016/j.patrec.2005.01.012
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Kalatzis, I., Piliouras, N., Ventouras, E., Papageorgiou, C.,Liappas, I., et al. (August 2005). Design and implementation of a multi-PNN structure for discriminating one-month abstinent heroin addicts from healthy controls using the P600 component of ERP signals. Pattern Recognition Letters. 26(11). pp. 1691–1700. Elsevier Science Ltd. Available from: http://www.sciencedirect.com [Accessed 07/04/2005]	en
heal.abstract	A multi-probabilistic neural network (multi-PNN) classification structure has been designed for distinguishing one-month abstinent heroin addicts from normal controls by means of the Event-Related Potentials’ P600 component, selected at 15 scalp leads, elicited under a Working Memory (WM) test. The multi-PNN structure consisted of 15 optimally designed PNN lead-classifiers feeding an end-stage PNN classifier. The multi-PNN structure classified correctly all subjects. When leads were grouped into compartments, highest accuracies were achieved at the frontal (91.7%) and left temporo-central region (86.1%). Highest single-lead precision (86.1%) was found at the P3, C5 and F3 leads. These findings indicate that cognitive function, as represented by P600 during a WM task and explored by the PNN signal processing techniques, may be involved in short-term abstinent heroin addicts. Additionally, these findings indicate that these techniques may significantly facilitate computer-aided analysis of ERPs.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Pattern Recognition Letters	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true