A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals

Autor/inn/en	Barik, Kasturi; Watanabe, Katsumi; Bhattacharya, Joydeep; Saha, Goutam
Titel	A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals
Quelle	In: Journal of Autism and Developmental Disorders, 53 (2023) 12, S.4830-4848 (19 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bhattacharya, Joydeep)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3257
DOI	10.1007/s10803-022-05767-w
Schlagwörter	Autism Spectrum Disorders; Young Children; Measurement Techniques; Diagnostic Tests; Brain; Brain Hemisphere Functions; Accuracy; Artificial Intelligence; Cartoons + Suchen Sie Ihr Suchwort? Autism; Autismus; Frühe Kindheit; Messtechnik; Diagnostic test; Diagnostischer Test; Gehirn; Künstliche Intelligenz; Zeichentrickfilm
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01