Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths

Romuald Carette, Mahmoud Elbattah, Federica Cilia, Gilles Dequen, Jean-Luc Guérin, Jérôme Bosche

2019

Abstract

Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. This paper presents an approach to help with the ASD diagnosis with a particular focus on children at early stages of development. Using Machine Learning, our approach aims to learn the eye-tracking patterns of ASD. The key idea is to transform eye-tracking scanpaths into a visual representation, and hence the diagnosis can be approached as an image classification task. Our experimental results evidently demonstrated that such visual representations could simplify the prediction problem, and attained a high accuracy as well. With simple neural network models and a relatively limited dataset, our approach could realize a quite promising accuracy of classification (AUC > 0.9).

Paper Citation

in Harvard Style

Carette R., Elbattah M., Cilia F., Dequen G., Guérin J. and Bosche J. (2019). Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5: HEALTHINF; ISBN 978-989-758-353-7, SciTePress, pages 103-112. DOI: 10.5220/0007402601030112

in Bibtex Style

@conference{healthinf19,
author={Romuald Carette and Mahmoud Elbattah and Federica Cilia and Gilles Dequen and Jean-Luc Guérin and Jérôme Bosche},
title={Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5: HEALTHINF},
year={2019},
pages={103-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007402601030112},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5: HEALTHINF
TI - Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths
SN - 978-989-758-353-7
AU - Carette R.
AU - Elbattah M.
AU - Cilia F.
AU - Dequen G.
AU - Guérin J.
AU - Bosche J.
PY - 2019
SP - 103
EP - 112
DO - 10.5220/0007402601030112
PB - SciTePress