Background:The study aims to describe heart rate variability (HRV) in neonatal hypoxic ischemic encephalopathy (HIE) and correlate HRV with electroencephalographic (EEG) grade of HIE and neurodevelopmental outcome. Methods: Multichannel EEG and electrocardiography (ECG) were assessed at 12-48 h after birth in healthy and encephalopathic full-term neonates. EEGs were graded (normal, mild, moderate, and severe). Neurodevelopmental outcome was assessed at 2 y of age. Seven HRV features were calculated using normalized-RR (NN) interval. The correlation of these features with EEG grade and outcome were measured using Spearman's correlation coefficient. results: HRV was significantly associated with HIE severity (P < 0.05): standard deviation of NN interval (SDNN) (r = −0.62), triangular interpolation of NN interval histogram (TINN) (r = −0.65), mean NN interval (r = −0.48), and the very low frequency (VLF) (r = −0.60), low frequency (LF) (r = −0.67) and high frequency (HF) components of the NN interval (r = −0.60). SDNN at 24 and 48 h were significantly associated (P < 0.05) with neurodevelopmental outcome (r = −0.41 and −0.54, respectively). conclusion: HRV is associated with EEG grade of HIE and neurodevelopmental outcome. HRV has potential as a prognostic tool to complement EEG.

The HF component of HRV is increased in infants with moderate HIE undergoing TH.

HighlightsMachine learning approach enables accurate detection of bursts in preterm EEG.Features of amplitude and spectral shape capture discriminating information.Improves reliability of estimates of inter-burst intervals.

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