Literaturnachweis - Detailanzeige
Autor/inn/en | Barik, Kasturi; Watanabe, Katsumi; Bhattacharya, Joydeep; Saha, Goutam |
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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 |
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 |
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 |