Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/4759
Title: A probabilistic neural network (PNN) classifier for discriminating obsessive-compulsive disorder (OCD) patients from healthy controls using the P600 component of ERP signals
Authors: Καλατζής, Ιωάννης
Πήλιουρας, Νικόλαος
Βεντούρας, Ερρίκος Μ.
Παπαγεωργίου, Χαράλαμπος
Ραμπαβίλας, Ανδρέας Ν.
Contributors: Κάβουρας, Διονύσης Α.
Item type: Conference publication
Conference Item Type: Full Paper
Keywords: Neuropsychological tests;Pattern recognition;Νευροψυχολογία;Αναγνώριση προτύπων
Subjects: Medicine
Medical technology
Ιατρική
Ιατρικά όργανα και εξοπλισμός
Issue Date: 26-Jan-2015
2004
Publisher: IEEE
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.
Description: Proceedings of the 4th European Symposium on Biomedical Engineering
Language: English
Citation: 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
Conference: 4th European Symposium on Biomedical Engineering
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/4759
Appears in Collections:Δημοσιεύσεις

Files in This Item:
File Description SizeFormat 
58 - 2004 - Proc4thESBE - Kalatzis.pdf
  Restricted Access
234.93 kBAdobe PDFView/Open Request a copy


This item is licensed under a Creative Commons License Creative Commons