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Integration of Facial Thermography in EEG-based Classification of ASD

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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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Acknowledgments

This work was supported by Old Dominion University, Norfolk, Virginia and University of Moratuwa, Sri Lanka.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka

    Dilantha Haputhanthri, Gunavaran Brihadiswaran, Sahan Gunathilaka & Dulani Meedeniya

  2. Department of Computer Science, Old Dominion University, Norfolk, 23529, USA

    Sampath Jayarathna

  3. Division of Science, Indiana University-Purdue University, Indianapolis, 47203, USA

    Mark Jaime

  4. Department of Psychology, University of New Orleans, New Orleans, 70148, USA

    Christopher Harshaw

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  1. Dilantha Haputhanthri
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Correspondence to Dulani Meedeniya.

Additional information

Dilantha Haputhanthri is an undergraduate at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.

His research interests include bioinformatics, machine learning, data analytics and algorithm development.

Gunavaran Brihadiswaran is an undergraduate at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.

His research interests include emerging memory technologies, computer architecture, data structures and algorithms, and computational biology.

Sahan Gunathilaka is an undergraduate in the Department of Computer Science and Engineering at the University of Moratuwa, Sri Lanka.

His research interests include bioinformatics, machine learning, and image processing.

Dulani Meedeniya received the Ph. D. degree in computer science from University of St Andrews, UK. She is a senior lecturer in Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka. She is a Fellow of HEA(UK), MIET, MIEEE, and a Charted Engineer at EC (UK).

Her research interests indude software modeling and design, workflow tool support for bioinformatics, data visualization and recommender systems.

Sampath Jayarathna received the Ph. D. degree in computer science from the Texas A&M University College Station, USA in 2016. He is an assistant professor of computer science at Old Dominion University, USA, where he is associated with Web Science and Digital Libraries (WS-DL) research group. He is a member of ACM, IEEE, and Sigma XI. His research interests include machine learning, information retrieval, data science, eye tracking, and brain-computer interfacing.

Mark Jaime received the Ph. D. degger in psychology from Florida International University, USA in 2008, where he was trained in developmental psychobiology. He then completed two post-doctoral fellowships specializing in the neurocognitive development of autism spectrum disorder (ASD) and childhood intersensory processing in Department of Psychology, University of Miami, USA and Department of Psychology and Neuroscience at Dalhousie University, Canada. He is an associate professor of psychology at the Columbus Center of Indiana University-Purdue University, USA. He is a full member of the International Society for Autism Research.

His research interests include characterizing psychophysiological mechanisms of social information processing in ASD and typical populations. He also engages in interdisciplinary collaborations focused on the development of neurocognitive markers of social impairment.

Christopher Harshaw received the Ph. D. degree in developmental Science from Florida International University, USA. He then completed a postdoc at Indiana University, USA with a focus on Behavioral Neuroscience and Developmental Psychobiology. He is an assistant professor at Department of Psychology, University of New Orleans, USA. He directs the Mechanisms Underlying Sociality (MUS) Laboratory, which is currently focused on how deficits in temperature regulation may relate to differences in social cognitive abilities in Autism Spectrum Disorders.

His research interest is gaining a better understanding of how bodily mechanisms influence cognition and behavior.

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Haputhanthri, D., Brihadiswaran, G., Gunathilaka, S. et al. Integration of Facial Thermography in EEG-based Classification of ASD. Int. J. Autom. Comput. 17, 837–854 (2020). https://doi.org/10.1007/s11633-020-1231-6

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