A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals.
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...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 53; no. 12; pp. 4830 - 4849 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173458787&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173458787 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Dec2023 vid: 53 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173458787 159461027 173458787 173458787 10.1007/s10803-022-05767-w 173458787 ppf: 4830 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals. aug: au: Barik, Kasturi Watanabe, Katsumi Bhattacharya, Joydeep Saha, Goutam affil: https://ror.org/03w5sq511 Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India sug: subj: Autism Spectrum Disorder Diagnosis Machine Learning Methods Electroencephalography Utilization Electromagnetic Fields Utilization Biological Markers Human Child, Preschool Child Sensitivity and Specificity Descriptive Statistics Autism Spectrum Disorder Physiopathology Diagnosis, Neurologic Child, Preschool: 2-5 years Child: 6-12 years ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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