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Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by ma...

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Autores principales: Ghasemi, Elham, Ebrahimi, Mansour, Ebrahimie, Esmaeil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666608/
https://www.ncbi.nlm.nih.gov/pubmed/36408064
http://dx.doi.org/10.1007/s11571-021-09746-2
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author Ghasemi, Elham
Ebrahimi, Mansour
Ebrahimie, Esmaeil
author_facet Ghasemi, Elham
Ebrahimi, Mansour
Ebrahimie, Esmaeil
author_sort Ghasemi, Elham
collection PubMed
description Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy. GRAPHICAL ABSTRACT: [Image: see text]
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spelling pubmed-96666082022-11-17 Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials Ghasemi, Elham Ebrahimi, Mansour Ebrahimie, Esmaeil Cogn Neurodyn Research Article Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy. GRAPHICAL ABSTRACT: [Image: see text] Springer Netherlands 2022-02-15 2022-12 /pmc/articles/PMC9666608/ /pubmed/36408064 http://dx.doi.org/10.1007/s11571-021-09746-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Ghasemi, Elham
Ebrahimi, Mansour
Ebrahimie, Esmaeil
Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
title Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
title_full Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
title_fullStr Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
title_full_unstemmed Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
title_short Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
title_sort machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666608/
https://www.ncbi.nlm.nih.gov/pubmed/36408064
http://dx.doi.org/10.1007/s11571-021-09746-2
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