Electroencephalography-based machine ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
PMID :
URL permanente :
Titre :
Electroencephalography-based machine learning for cognitive profiling in parkinson''s disease: preliminary results
Auteur(s) :
Betrouni, Nacim [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Delval, Arnaud [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Chaton, Laurence [Auteur]
DEFEBVRE, Luc [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Duits, Annelien A. [Auteur]
Moonen, Anja J. H. [Auteur]
Leentjens, Albert F. G. [Auteur]
Dujardin, Kathy [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Delval, Arnaud [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Chaton, Laurence [Auteur]
DEFEBVRE, Luc [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Duits, Annelien A. [Auteur]
Moonen, Anja J. H. [Auteur]
Leentjens, Albert F. G. [Auteur]
Dujardin, Kathy [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Titre de la revue :
Movement disorders . official journal of the Movement Disorder Society
Nom court de la revue :
Mov. Disord.
Date de publication :
2018-10-21
ISSN :
1531-8257
Mot(s)-clé(s) en anglais :
cognitive deficits
machine learning
quantitative EEG
characterization models
machine learning
quantitative EEG
characterization models
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening.
The aim of this ...
Lire la suite >Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models. Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients. The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society.Lire moins >
Lire la suite >Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models. Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients. The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
CNRS
Inserm
Université de Lille
CNRS
Inserm
Université de Lille
Collections :
Équipe(s) de recherche :
Troubles cognitifs dégénératifs et vasculaires
Date de dépôt :
2019-11-27T13:34:12Z