Τίτλος:
Design and implementation of a multi-PNN structure for discriminating
one-month abstinent heroin addicts from healthy controls using the P600
component of ERP signals
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
A multi-probabilistic neural network (multi-PNN) classification
structure has been designed for distinguishing one-month abstinent
heroin addicts from normal controls by means of the Event-Related
Potentials’ P600 component, selected at 15 scalp leads, elicited under a
Working Memory (WM) test. The multi-PNN structure consisted of 15
optimally designed PNN lead-classifiers feeding an end-stage PNN
classifier. The multi-PNN structure classified correctly all subjects.
When leads were grouped into compartments, highest accuracies were
achieved at the frontal (91.7%) and left temporo-central region
(86.1%). Highest single-lead precision (86.1%) was found at the P3, C5
and F3 leads. These findings indicate that cognitive function, as
represented by P600 during a WM task and explored by the PNN signal
processing techniques, may be involved in short-term abstinent heroin
addicts. Additionally, these findings indicate that these techniques may
significantly facilitate computer-aided analysis of ERPs. (c) 2005
Elsevier B.V. All rights reserved.
Συγγραφείς:
Kalatzis, I
Piliouras, K
Ventouras, E
Papageorgiou, CC and
Liappas, IA
Nikolaou, CC
Rabavilas, AD
Cavouras, DD
Περιοδικό:
Pattern Recognition Letters
Εκδότης:
ELSEVIER SCIENCE BV
Λέξεις-κλειδιά:
heroin addicts; event-related potentials (ERPs); P600 component; pattern
recognition
DOI:
10.1016/j.patrec.2005.01.012
