  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.
