Identifying Differences in the P600 Component of ERP-Signals between OCD Patients and Controls Employing a PNN-based Majority Vote Classification Scheme

I Kalatzis; N Piliouras; D Glotsos; E Ventouras; C Papageorgiou; A Rabavilas; C Soldatos; D Cavouras

doi:10.1109/IEMBS.2005.1615337

Identifying Differences in the P600 Component of ERP-Signals between OCD Patients and Controls Employing a PNN-based Majority Vote Classification Scheme

Conf Proc IEEE Eng Med Biol Soc. 2005:2005:3994-7. doi: 10.1109/IEMBS.2005.1615337.

Authors

I Kalatzis¹, N Piliouras, D Glotsos, E Ventouras, C Papageorgiou, A Rabavilas, C Soldatos, D Cavouras

Affiliation

¹ Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Athens, Greece.

PMID: 17281107
DOI: 10.1109/IEMBS.2005.1615337

Abstract

In the present study an attempt was made to focus in the differences between Obsessive-Compulsive Disorder (OCD) patients and healthy controls, as reflected by the P600 component of event-related potential (ERP) signals, to locate brain areas that may be related to Working Memory (WM) deficits. Neuropsychological research has yielded contradicting results regarding WM in OCD. Eighteen patients with OCD symptomatology and 20 normal controls (age and sex matched) were subjected to a computerized version of the digit span Wechsler test. EEG activity was recorded from 15 scalp electrodes (leads). A dedicated computer software was developed to read the ERP signals and to calculate features related to the ERP P600 component (500-800 ms). Nineteen features were generated, from each ERP-signal and each lead, and were employed in the design of the Probabilistic Neural Network (PNN) classifier. Highest single-lead precision (86.8%) was found at the Fp2 and C6 leads. When the output from all single-lead PNN classifiers fed a Majority Vote Engine (MVE), the system classified correctly all subjects, providing a powerful classification scheme. Findings indicated that OCD patients differed from normal controls at the prefrontal and temporo-central brain regions.