Abstract
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.
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Supported in part by CIHR and FRQS (MGF) and Sloan (HTW). Some data analyzed in the current study were presented previously (e.g., Neuhaus et al. 2014) as described in the text.
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MGF and HTW Study conceptualization; Data processing/analysis; Manuscript draft, revision, and final approval. CS and AM Data processing/analysis; Manuscript revision and final approval. EN Study conceptualization; Data collection; Data processing/analysis; Manuscript draft, revision, and final approval. RAB Study conceptualization; Manuscript revision and final approval. DK Data processing; Data processing/analysis; Manuscript revision and final approval. TPB Study conceptualization; Data processing/analysis; Manuscript revision and final approval.
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Frasch, M.G., Shen, C., Wu, HT. et al. Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?. J Autism Dev Disord 51, 346–356 (2021). https://doi.org/10.1007/s10803-020-04467-7
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DOI: https://doi.org/10.1007/s10803-020-04467-7