A combination of P300 and eye movement data improves the accuracy of auxiliary diagnoses of depression.
Auxiliary diagnoses
Depression
Efficacy prediction
Electrophysiology
Machine learning
Journal
Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073
Informations de publication
Date de publication:
15 01 2022
15 01 2022
Historique:
received:
21
07
2021
revised:
09
10
2021
accepted:
20
10
2021
pubmed:
29
10
2021
medline:
27
1
2022
entrez:
28
10
2021
Statut:
ppublish
Résumé
Exploratory eye movements (EEMs) and P300 are often used to facilitate the clinical diagnosis of depression. However, There were few studies using the combination of EEMs and P300 to build a model for detecting depression and predicting a curative effect. Sixty patients were recruited for 2 groups: high frequency repetitive transcranial magnetic stimulation(rTMS) combined with paroxetine group and simple paroxetine group. Clinical efficacy was evaluated by the Hamilton Depression scale-24(HAMD-24), EEMs and P300. The classification model of the auxiliary diagnosis of depression and the prediction model of the two treatments were developed based on a machine learning algorithm. The classification model with the greatest accuracy for patients with depression and healthy controls was 95.24% (AUC = 0.75, recall = 1.00, precision = 0.95, F1-score = 0.97). The root mean square error (RMSE) of the model for predicting the efficacy of high frequency rTMS combined with paroxetine was 3.54 (MAE [mean absolute error] = 2.56, R Based on the machine learning algorithm, P300 and EEMs data was suitable for modeling to distinguish depression patients and healthy individuals. However, it was not suitable for predicting the efficacy of high frequency rTMS combined with paroxetine or to predict the efficacy of paroxetine.
Sections du résumé
BACKGROUND
Exploratory eye movements (EEMs) and P300 are often used to facilitate the clinical diagnosis of depression. However, There were few studies using the combination of EEMs and P300 to build a model for detecting depression and predicting a curative effect.
METHODS
Sixty patients were recruited for 2 groups: high frequency repetitive transcranial magnetic stimulation(rTMS) combined with paroxetine group and simple paroxetine group. Clinical efficacy was evaluated by the Hamilton Depression scale-24(HAMD-24), EEMs and P300. The classification model of the auxiliary diagnosis of depression and the prediction model of the two treatments were developed based on a machine learning algorithm.
RESULTS
The classification model with the greatest accuracy for patients with depression and healthy controls was 95.24% (AUC = 0.75, recall = 1.00, precision = 0.95, F1-score = 0.97). The root mean square error (RMSE) of the model for predicting the efficacy of high frequency rTMS combined with paroxetine was 3.54 (MAE [mean absolute error] = 2.56, R
CONCLUSION
Based on the machine learning algorithm, P300 and EEMs data was suitable for modeling to distinguish depression patients and healthy individuals. However, it was not suitable for predicting the efficacy of high frequency rTMS combined with paroxetine or to predict the efficacy of paroxetine.
Identifiants
pubmed: 34710500
pii: S0165-0327(21)01103-4
doi: 10.1016/j.jad.2021.10.028
pii:
doi:
Substances chimiques
Paroxetine
41VRH5220H
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
386-395Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.