Title :

Electroencephalography-based machine learning for cognitive profiling in parkinson''s disease: preliminary results

Author(s) :

Betrouni, Nacim [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV]
Delval, Arnaud [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV]
Chaton, Laurence [Auteur]
DEFEBVRE, Luc [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV]
Duits, Annelien A. [Auteur]
Moonen, Anja J. H. [Auteur]
Leentjens, Albert F. G. [Auteur]
Dujardin, Kathy [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 [TCDV]

Journal title :

Movement disorders . official journal of the Movement Disorder Society

Abbreviated title :

Mov. Disord.

Publication date :

2018-10-21

ISSN :

1531-8257

English keyword(s) :

cognitive deficits
machine learning
quantitative EEG
characterization models

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CHU Lille
CNRS
Inserm
Université de Lille

Collections :

• Lille Neurosciences & Cognition (LilNCog) - U 1172

Research team(s) :

Troubles cognitifs dégénératifs et vasculaires

Submission date :

2019-11-27T13:34:12Z