Uncovering statistical features of bradycardia severity in premature infants using a point process model

Premature infants are susceptible to a variety of life-threatening events. Underdeveloped cardiovascular control due to an immature autonomic nervous system can lead to recurrent bradycardias that reduce blood flow and oxygen to critical organs, and result in long-term developmental disabilities or sudden death. In this study, we investigate the use of a novel point process framework to model heart rate dynamics in premature infants, including the full range of bradycardia severity. We find that the lognormal distribution accurately models the R-R interval time series, due to the long-tail nature of the distribution. We also find that the degree of bradycardia severity is correlated with distinct clustering features of the point-process indices in regions encompassing and adjacent to bradycardias. This underlying property in heart rate dynamics may provide valuable statistical information for quantifying the vulnerability of premature infants to develop bradycardia.