Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/4755
Title: Comparative evaluation of probabilistic neural network versus support vector machines classifiers in discriminating ERP signals of depressive patients from healthy controls
Authors: Καλατζής, Ιωάννης
Πήλιουρας, Νικόλαος
Βεντούρας, Ερρίκος Μ.
Παπαγεωργίου, Χαράλαμπος
Ραμπαβίλας, Ανδρέας Ν.
Contributors: Κανδαράκης, Διονύσης Α.
Loncaric, S. (ed.)
Neri, A. (ed.)
Babic, H. (ed.)
Item type: Conference publication
Conference Item Type: Full Paper
Keywords: Electroencephalography;Medical signal processing;Ηλεκτροεγκεφαλογραφία;Ιατρική επεξεργασία σήματος
Subjects: Medicine
Biomedical engineering
Ιατρική
Βιοϊατρική τεχνολογία
Issue Date: 26-Jan-2015
2003
Publisher: IEEE
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.
Description: Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (Vol.2 )
Language: English
Citation: 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.
Conference: International Symposium on Image and Signal Processing and Analysis
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/4755
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