Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results.
characterization models
cognitive deficits
machine learning
quantitative EEG
Journal
Movement disorders : official journal of the Movement Disorder Society
ISSN: 1531-8257
Titre abrégé: Mov Disord
Pays: United States
ID NLM: 8610688
Informations de publication
Date de publication:
02 2019
02 2019
Historique:
received:
02
04
2018
revised:
25
08
2018
accepted:
03
09
2018
pubmed:
23
10
2018
medline:
3
1
2020
entrez:
23
10
2018
Statut:
ppublish
Résumé
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.
Sections du résumé
BACKGROUND
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.
OBJECTIVE
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.
METHODS
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.
RESULTS
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.
CONCLUSION
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
210-217Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2018 International Parkinson and Movement Disorder Society.