EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach

Abstract Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the ne...

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Autores principales:	William J. Bosl, Helen Tager-Flusberg, Charles A. Nelson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/edb9e9d855be46d6bb6989150a76e1ca
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spelling	oai:doaj.org-article:edb9e9d855be46d6bb6989150a76e1ca2021-12-02T11:40:45ZEEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach10.1038/s41598-018-24318-x2045-2322https://doaj.org/article/edb9e9d855be46d6bb6989150a76e1ca2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-24318-xhttps://doaj.org/toc/2045-2322Abstract Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.William J. BoslHelen Tager-FlusbergCharles A. NelsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-20 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q William J. Bosl Helen Tager-Flusberg Charles A. Nelson EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
description	Abstract Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.
format	article
author	William J. Bosl Helen Tager-Flusberg Charles A. Nelson
author_facet	William J. Bosl Helen Tager-Flusberg Charles A. Nelson
author_sort	William J. Bosl
title	EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
title_short	EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
title_full	EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
title_fullStr	EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
title_full_unstemmed	EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
title_sort	eeg analytics for early detection of autism spectrum disorder: a data-driven approach
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/edb9e9d855be46d6bb6989150a76e1ca
work_keys_str_mv	AT williamjbosl eeganalyticsforearlydetectionofautismspectrumdisorderadatadrivenapproach AT helentagerflusberg eeganalyticsforearlydetectionofautismspectrumdisorderadatadrivenapproach AT charlesanelson eeganalyticsforearlydetectionofautismspectrumdisorderadatadrivenapproach
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EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach

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