Analysis of heart rate variability for predicting cardiorespiratory events in infants

Cardiorespiratory events (CREs), including bradycardia and apnea, in infants are a major concern for physicians and families. Our hypothesis was that there is a difference in the heart rate variability (HRV) of infants who have CREs when compared to normal control infants. The purpose of this study was to develop CRE prediction models based on HRV measured during a polysomnographic (PSG) recording. ANCOVA analysis accounting for factors such as age and sleep state show a relationship between HRV variables and CRE. Prediction models, including neural networks and support vector machines, were developed to predict CRE within either (a) 1-week or (b) 1-month after the PSG. The support vector machine prediction accuracy, for CRE susceptibility one month after the PSG on an independent testing dataset, was 50.0% sensitivity and 82.6% specificity. Although the developed prediction models were not sufficiently accurate for clinical decision making, these results support the potential role of abnormalities in autonomic control of heart rate among infants at risk for CREs.

► A detailed approach of incorporating statistics and computational intelligence for a real world problem. ► A prediction model which achieved 50% sensitivity and 83% specificity for predicting cardiorespiratory events in infants based on heart rate variability. ► The results highlight the importance of the awake state for physiological recordings in infants.