Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. We apply a set of linear and nonlinear HRV measures-including phase rectified signal averaging-to segments of resting ECG data collected from school-age children with ASD, age-matched typically developing controls, and children with other psychiatric conditions characterized by altered HRV (conduct disorder, depression). We use machine learning to identify time, frequency, and geometric signal-analytical domains that are specific to ASD (receiver operating curve area = 0.89). This is the first study to differentiate children with ASD from other disorders characterized by altered HRV. Despite a small cohort and lack of external validation, results warrant larger prospective studies.

中文翻译：

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简要报告：复合心率变异生物标志物能否为学龄儿童自闭症谱系障碍提供新的见解？


多项研究表明自闭症谱系障碍 (ASD) 的心率变异性 (HRV) 发生改变，但研究结果既不具有普遍性，也不针对 ASD。我们将一组线性和非线性 HRV 测量（包括相位校正信号平均）应用于从患有 ASD 的学龄儿童、年龄匹配的典型发育对照以及患有以 HRV 改变为特征的其他精神疾病的儿童收集的静息心电图数据片段。行为障碍、抑郁症）。我们使用机器学习来识别 ASD 特有的时间、频率和几何信号分析域（接收器工作曲线面积 = 0.89）。这是第一项将患有 ASD 的儿童与以 HRV 改变为特征的其他疾病区分开来的研究。尽管队列规模较小且缺乏外部验证，但结果仍值得进行更大规模的前瞻性研究。