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Integration of Facial Thermography in EEG-based Classification of ASD

Authors:

Dilantha Haputhanthri,

Gunavaran Brihadiswaran,

Sahan Gunathilaka,

Dulani Meedeniya,

Sampath Jayarathna,

Christopher HarshawAuthors Info & Claims

International Journal of Automation and Computing, Volume 17, Issue 6

Pages 837 - 854

https://doi.org/10.1007/s11633-020-1231-6

Published: 01 December 2020 Publication History

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting social, communicative, and repetitive behavior. The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging, requiring highly trained clinical practitioners. The development of automated approaches to ASD classification, based on integrated psychophysiological measures, may one day help expedite the diagnostic process. This paper provides a novel contribution for classifing ASD using both thermographic and EEG data. The methodology used in this study extracts a variety of feature sets and evaluates the possibility of using several learning models. Mean, standard deviation, and entropy values of the EEG signals and mean temperature values of regions of interest (ROIs) in facial thermographic images were extracted as features. Feature selection is performed to filter less informative features based on correlation. The classification process utilizes Naïve Bayes, random forest, logistic regression, and multi-layer perceptron algorithms. The integration of EEG and thermographic features have achieved an accuracy of 94% with both logistic regression and multi-layer perceptron classifiers. The results have shown that the classification accuracies of most of the learning models have increased after integrating facial thermographic data with EEG.

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Cited By

Parsa MRad HVaezi HHossein-Zadeh GSetarehdan SRostami RRostami HVahabie A(2023)EEG-based classification of individuals with neuropsychiatric disorders using deep neural networksComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2023.107683240:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.cmpb.2023.107683
Joudar SAlbahri AHamid RZahid IAlqaysi MAlbahri OAlamoodi A(2023)Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issuesArtificial Intelligence Review10.1007/s10462-023-10536-x56:Suppl 1(53-117)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1007/s10462-023-10536-x
Meedeniya DKumarasinghe HKolonne SFernando CDíez IMarques G(2022)Chest X-ray analysis empowered with deep learningApplied Soft Computing10.1016/j.asoc.2022.109319126:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109319
Show More Cited By

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Integration of Facial Thermography in EEG-based Classification of ASD
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

cover image International Journal of Automation and Computing

International Journal of Automation and Computing Volume 17, Issue 6

Dec 2020

128 pages

ISSN:1476-8186

Issue’s Table of Contents

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2020

Accepted: 20 March 2020

Received: 31 October 2019

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Cited By

Parsa MRad HVaezi HHossein-Zadeh GSetarehdan SRostami RRostami HVahabie A(2023)EEG-based classification of individuals with neuropsychiatric disorders using deep neural networksComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2023.107683240:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.cmpb.2023.107683
Joudar SAlbahri AHamid RZahid IAlqaysi MAlbahri OAlamoodi A(2023)Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issuesArtificial Intelligence Review10.1007/s10462-023-10536-x56:Suppl 1(53-117)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1007/s10462-023-10536-x
Meedeniya DKumarasinghe HKolonne SFernando CDíez IMarques G(2022)Chest X-ray analysis empowered with deep learningApplied Soft Computing10.1016/j.asoc.2022.109319126:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109319
Marasca ABackes AFavarim FTeixeira MCasanova D(2021)EDT Method for Multiple Labelled Objects Subject to Tied DistancesInternational Journal of Automation and Computing10.1007/s11633-021-1285-018:3(468-479)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11633-021-1285-0

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