A Fusion-based Machine Learning Approach for Autism Detection in Young Children using Magnetoencephalography Signals

Barik, Kasturi; Watanabe, Katsumi; Bhattacharya, Joydeep and Saha, Goutam. 2022. A Fusion-based Machine Learning Approach for Autism Detection in Young Children using Magnetoencephalography Signals. Journal of Autism and Developmental Disorders, ISSN 0162-3257 [Article] (In Press)

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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

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:

DateEvent
16 September 2022Accepted

Item ID:

32201

Date Deposited:

20 Sep 2022 08:55

Last Modified:

21 Sep 2022 05:39

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

https://research.gold.ac.uk/id/eprint/32201

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