A Multimodal Approach for Identifying Autism Spectrum Disorders in Children

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper...

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Vydáno v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 30; s. 1
Hlavní autoři:	Han, Junxia, Jiang, Guoqian, Ouyang, Gaoxiang, Li, Xiaoli
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Autism Autism spectrum disorders (ASD) Behavioral sciences Brain modeling Children Classification Complementarity Deep learning Diagnosis EEG Electroencephalogram (EEG) Electroencephalography Eye movements Eye-tracking (ET) Feature extraction Heterogeneity Machine learning Multimodal fusion Neuroimaging Pediatrics Stacked denoising autoencoders Variable speed drives
ISSN:	1534-4320, 1558-0210, 1558-0210
On-line přístup:	Získat plný text
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