Barik, Kasturi; Watanabe, Katsumi; Bhattacharya, Joydeep and Saha, Goutam. 2023. A Fusion-based Machine Learning Approach for Autism Detection in Young Children using Magnetoencephalography Signals. Journal of Autism and Developmental Disorders, 53(12), pp. 4830-4848. ISSN 0162-3257 [Article]

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Abstract or Description

In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4-7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA). Machine learning based classifier showed a higher classification accuracy (88%) for PPA features than PSD features (82%). Further, by a novel fusion method combining PSD and PPA features, we achieved an average classification accuracy of 94% and 98% for feature-level and score-level fusion, respectively. These findings reveal discriminatory patterns of neural oscillations of autism
in young children and provide novel insight into autism pathophysiology.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1007/s10803-022-05767-w
Additional Information:	Availability of data and material: The data and codes would be made available at a reasonable request made to the corresponding author.
Keywords:	Autism Spectrum Disorder, Brain Oscillations, Preferred Phase Angle, MEG, Classification, Fusion
Departments, Centres and Research Units:	Psychology
Dates:	Date Event 16 September 2022 Accepted 3 October 2022 Published Online December 2023 Published
Item ID:	32201
Date Deposited:	20 Sep 2022 08:55
Last Modified:	08 Jan 2024 13:45
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/32201

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