Early Detection of risk of autism spectrum disorder based on recurrence quantification analysis of electroencephalographic signals
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Early detection of autism spectrum disorder (ASD) in infants is vital in maximizing the impact and potential long-term outcomes of early delivery of rehabilitative therapies. To date no definitive diagnostic test for ASD exists. Electroencephalography is a noninvasive method used to capture underlying electrical changes in brain activity. This proof-of-concept study suggests that recurrence quantification analysis features computed from resting state spontaneous eyes-closed electroencephalographic (EEG) signals may be useful biomarkers for early detection of risk of ASD.
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