Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results-Reference-Cited by-同舟云学术

Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results

Published:2022-07-11 Issue: Volume: Page:
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Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Manjur Sultan Mohammad¹,Hossain Md-Billal¹,Constable Paul A.²,Thompson Dorothy A.³,Marmolejo-Ramos Fernando⁴,Lee Irene O⁵,Skuse David H⁵,Posada-Quintero Hugo F.¹

Affiliation:

1. University of Connecticut,Department of Biomedical Engineering,Storrs,CT,USA,06269

2. Caring Futures Institute College of Nursing and health Sciences, Flinders University,Adelaide,SA,Australia,5001

3. UCL Great Ormond Street Institute of Child Health, University College London,London,UK

4. Center for Change and Complexity in Learning, University of South Australia,Adelaide,Australia

5. UCL Great Ormond Street Institute of Child Health, University College London,Behavioral and Brain Sciences Unit, Population Policy and Practice Program,London,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871173.pdf?arnumber=9871173

Reference23 articles.

1. Increased prevalence of non-communicable physical health conditions among autistic adults

2. EEG complexity as a biomarker for autism spectrum disorder risk

3. Heart rate variability in individuals with autism spectrum disorders: A meta-analysis

4. Saccadic eye movement abnormalities in autism spectrum disorder indicate dysfunctions in cerebellum and brainstem

5. EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

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