Détail de la notice
Titre du Document
A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy
Auteur(s)
KHODAYARI-ROSTAMABAD Ahmad ; HASEY Gary M. ; MACCRIMMON Duncan J. ; ...
Résumé
Objective: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. Methods: Pre-treatment EEG data are collected in 23 + 14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators. Results: We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%. Conclusions: These findings indicate that analysis of pre-treatment EEG data can predict the clini
Editeur
Elsevier
Identifiant
ISSN : 1388-2457
Source
Clinical neurophysiology A. 2010, vol. 121, n° 12, pp. 1998-2006 [9 pages] [bibl. : 1/2 p.]
Langue
Anglais
Pour les membres de la communauté du CNRS, ce document est autorisé à la reproduction à titre gratuit.
Pour les membres des communautés hors CNRS, la reproduction de ce document à titre onéreux sera fournie sous réserve d’autorisation du Centre Français d’exploitation du droit de Copie.

Pour bénéficier de nos services (strictement destinés aux membres de la communauté CNRS (Centre National de la Recherche Scientifique), de l'ESR français (Enseignement Supérieur et Recherche), et du secteur public français & étranger) :