Cubic least-squares minimum-distance classifier 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	2015-01-26T12:02:43Z
dc.date.issued	2015-01-26
dc.identifier.uri	http://hdl.handle.net/11400/4766
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	Heroin addicts
dc.subject	Pattern recognition systems--Congresses
dc.subject	Ηρωινομανής
dc.subject	Αναγνώριση προτύπων
dc.title	Cubic least-squares minimum-distance classifier for discriminating one-month abstinent heroin addicts from healthy controls using the P600 component of ERP signals	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 1st International Conference “From Scientific Computing to Computational Engineering” (1ST IC-SCCE) (CD-ROM).	en
heal.classification	Medicine
heal.classification	Pharmacognosy
heal.classification	Ιατρική
heal.classification	Φαρμακογνωσία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85100596
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Φαρμακογνωσία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008108988
heal.contributorName	Νικολάου, Χρυσούλα	el
heal.contributorName	Ραμπαβίλας, Ανδρέας Ν.	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
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	Kalatzis, I., Piliouras, N., Ventouras, E., Papageorgiou, C., Liappas, I., et al. (2004). Cubic least-squares minimum-distance classifier for discriminating one-month abstinent heroin addicts from healthy controls using the P600 component of ERP signals, In the 1st International Conference “From Scientific Computing to Computational Engineering”. University of Patras: Athens, 8th-10th September 2004.	en
heal.abstract	The aim of this study was to investigate whether brain information storage and processing in one-month abstinent heroin addicts differ from healthy controls, as reflected by the P600 component of Event-related potential (ERP) signals elicited during a working memory test, as well to search deeper into the P600 signals by extracting new features and, by employing powerful classification procedures, to develop a pattern recognition system for discriminating drug users from controls. Sixteen one-month abstinent heroin addicts and twenty normal controls were examined. All subjects were evaluated by a computerized version of the digit span subtest of the Wechsler Adult Intelligence Scale. EEG activity was recorded from 15 scalp electrodes (leads). From the P600 component of each signal nineteen waveform-features were calculated. The cubic least-square minimum-distance classifier was developed and it was fed with features from all leads. The system was evaluated by means of the exhaustive search and leave-one-out methods. Highest single-lead precision (86.1%) was found at the P3, C5 and F3 leads at left. When leads were grouped into compartments, highest accuracies were achieved at the temporo-central region (88.9%). These findings may be indicative that one-month abstinent heroin addicts present deficits in working memory processes, as reflected by the P600 component.	en
heal.fullTextAvailability	true
heal.conferenceName	The aim of this study was to investigate whether brain information storage and processing in one-month abstinent heroin addicts differ from healthy controls, as reflected by the P600 component of Event-related potential (ERP) signals elicited during a working memory test, as well to search deeper into the P600 signals by extracting new features and, by employing powerful classification procedures, to develop a pattern recognition system for discriminating drug users from controls. Sixteen one-month abstinent heroin addicts and twenty normal controls were examined. All subjects were evaluated by a computerized version of the digit span subtest of the Wechsler Adult Intelligence Scale. EEG activity was recorded from 15 scalp electrodes (leads). From the P600 component of each signal nineteen waveform-features were calculated. The cubic least-square minimum-distance classifier was developed and it was fed with features from all leads. The system was evaluated by means of the exhaustive search and leave-one-out methods. Highest single-lead precision (86.1%) was found at the P3, C5 and F3 leads at left. When leads were grouped into compartments, highest accuracies were achieved at the temporo-central region (88.9%). These findings may be indicative that one-month abstinent heroin addicts present deficits in working memory processes, as reflected by the P600 component.	en
heal.conferenceItemType	full paper