Comparative evaluation of probabilistic neural network versus support vector machines classifiers in discriminating ERP signals of depressive patients from healthy controls

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	2015-01-26T10:49:48Z
dc.date.issued	2015-01-26
dc.identifier.uri	http://hdl.handle.net/11400/4755
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	Electroencephalography
dc.subject	Medical signal processing
dc.subject	Ηλεκτροεγκεφαλογραφία
dc.subject	Ιατρική επεξεργασία σήματος
dc.title	Comparative evaluation of probabilistic neural network versus support vector machines classifiers in discriminating ERP signals of depressive patients from healthy controls	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (Vol.2 )	en
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85042138
heal.contributorName	Κανδαράκης, Διονύσης Α.	el
heal.contributorName	Loncaric, S. (ed.)	en
heal.contributorName	Neri, A. (ed.)	en
heal.contributorName	Babic, H. (ed.)	en
heal.identifier.secondary	ISBN: 953-184-062-8
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	2003
heal.bibliographicCitation	Kalatzis, I., Piliouras, N., Ventouras, E., Papageorgiou, C., Rabavilas, A., et al. (2003). Comparative evaluation of probabilistic neural network versus support vector machines classifiers in discriminating ERP signals of depressive patients from healthy controls. In the 3rd International Symposium on Image and Signal Processing and Analysis. pp. 981-985. IEEE Signal Processing Society: Rome, 18th-20th September 2003.	en
heal.abstract	This paper describes the design of classification system capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised twenty-five patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEC activity was recorded from 15 scalp electrodes and recordings were digitized for further computer processing. Features related to the shape of the waveform were generated using a dedicated custom software interface system developed in C++ for the purposes of this work. A software classification system was designed, consisting of (a) two classifiers, the probabilistic neural network (PNN) and the support vector machines (SVM), (b) two routines for feature reduction and feature selection, and (c) an overall system evaluation routine, comprising the exhaustive search and the leave-one-out methods. Highest classification accuracies achieved were 92% for the PNN and 96% for the SVM, using the 'latency/amplitude ratio' and 'peak-to-peak slope' two-feature combination. In conclusion, employing computer-based pattern recognition techniques with features not easily evaluated by the clinician, patients with depression could be distinguished from healthy subjects with high accuracy.	en
heal.publisher	IEEE	el
heal.fullTextAvailability	true
heal.conferenceName	International Symposium on Image and Signal Processing and Analysis	en
heal.conferenceItemType	full paper