A probabilistic neural network (PNN) classifier for discriminating obsessive-compulsive disorder (OCD) patients 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	Ραμπαβίλας, Ανδρέας Ν.	el
dc.date.accessioned	2015-01-26T11:05:26Z
dc.date.issued	2015-01-26
dc.identifier.uri	http://hdl.handle.net/11400/4759
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	Neuropsychological tests
dc.subject	Pattern recognition
dc.subject	Νευροψυχολογία
dc.subject	Αναγνώριση προτύπων
dc.title	A probabilistic neural network (PNN) classifier for discriminating obsessive-compulsive disorder (OCD) patients from healthy controls using the P600 component of ERP signals	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 4th European Symposium on Biomedical Engineering	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/sh85091162
heal.keywordURI	http://skos.um.es/unescothes/C02924
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., Rabavilas, A., et al. (2004). A probabilistic neural network (PNN) classifier for discriminating obsessive-compulsive disorder (OCD) patients from healthy controls using the P600 component of ERP signals. In the 4th European Symposium on Biomedical Engineering. University of Patras: Patras, 2004	en
heal.abstract	Neuropsychological research yields diverging results regarding Working Memory (WM) in Obsessive-Compulsive Disorder (OCD). In the present study an attempt was made to focus in the differences between OCD patients and healthy controls, as reflected by the P600 component of ERP signals, 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 OCD patients from controls. Eighteen patients with OCD symptomatology and twenty age and sex matched 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 Probabilistic Neural Network (PNN) classifier was developed and it was fed with features from all leads. Highest single-lead precision (86.8%) was found at the Fp2 and C6 leads. When leads were grouped into anatomical regions, highest accuracies were achieved at the temporo-central (86.8%) region (C5,C6). These findings may be indicative that OCD patients present deficits related to WM mechanisms, corresponding to prefrontal, central, and temporocentral regions, as reflected by the P600 component.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	4th European Symposium on Biomedical Engineering	en
heal.conferenceItemType	full paper