Autonomic nervous system (ANS) activity is altered in autism spectrum disorder (ASD). Heart rate variability (HRV) derived from electrocardiogram (ECG) has been a powerful tool to identify alterations in ANS due to a plethora of pathophysiological conditions, including psychological ones such as depression. ECG-derived HRV thus carries a yet to be explored potential to be used as a diagnostic and follow-up biomarker of ASD. However, few studies have explored this potential. In a cohort of boys (ages 8 - 11 years) with (n=18) and without ASD (n=18), we tested a set of  linear and nonlinear HRV measures, including phase rectified signal averaging (PRSA) , applied to a segment of ECG collected under resting conditions for their predictive properties of ASD . We identified HRV measures derived from time, frequency  and geometric signal-analytical domains which are changed in ASD children relative to peers without ASD and correlate to psychometric scores (p<0.05 for each) . Receiver operating curves area ranged between 0.71 - 0.74 for each HRV measure. Despite being a small cohort lacking external validation, these promising preliminary results warrant larger prospective validation studies.
High-frequency heart rate variability (HRV) has identified parasympathetic nervous system alterations in autism spectrum disorder (ASD). In a cohort of school-aged children with and without ASD , we test a set of alternative linear and nonlinear HRV measures, including phase rectified signal averaging , applied to a segment of resting ECG, for associations with ASD vs. other psychiatric conditions. Using machine learning, we identify HRV measures derived from time, frequency , and geometric signal-analytical domains that (1) identify children with ASD relative to peers with receiver operating curve area of .89, and (2) differentiate such children from those with conduct problems or depression. Despite the small cohort and lack of prospective external validation, these preliminary results warrant larger prospective validation studies.
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Autonomic nervous system (ANS) activity is altered
High-frequency heart rate variability (HRV) has identified parasympathetic nervous system alterations
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Heart rate variability (HRV) derived from electrocardiogram (ECG) has been a powerful tool to identify alterations in ANS due to a plethora of pathophysiological conditions, including psychological ones such as depression. ECG-derived HRV thus carries a yet to be explored potential to be used as a diagnostic and follow-up biomarker of ASD. However, few studies have explored this potential.
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boys (ages 8 - 11 years) with (n=18)
school-aged children with
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(n=18), we tested
, we test
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alternative
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(PRSA)
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ECG collected under resting conditions for their predictive properties of ASD . We identified
resting ECG, for associations with ASD vs. other psychiatric conditions. Using machine learning, we identify
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,
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which are changed in ASD children
that (1) identify children with ASD
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without ASD and correlate to psychometric scores (p<0.05 for each) . Receiver operating curves area ranged between 0.71 - 0.74 for each HRV measure. Despite being a small cohort lacking
with receiver operating curve area of .89, and (2) differentiate such children from those with conduct problems or depression. Despite the small cohort and lack of prospective
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promising