Early Detection of risk of autism spectrum disorder based on recurrence quantification analysis of electroencephalographic signals

T. Pistorius, C. Aldrich, L. Auret, J. Pineda
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引用次数: 9

Abstract

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.
基于脑电图信号复发量化分析的自闭症谱系障碍风险早期检测
早期发现婴儿自闭症谱系障碍(ASD)对于最大限度地发挥早期康复治疗的影响和潜在的长期结果至关重要。到目前为止,还没有明确的ASD诊断测试。脑电图是一种非侵入性方法,用于捕捉大脑活动中潜在的电变化。这项概念验证研究表明,从静息状态自发闭眼脑电图(EEG)信号中计算的复发量化分析特征可能是早期检测ASD风险的有用生物标志物。
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