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Hypoxemia, characterized by low blood oxygen levels is pervasive in preterm infants and is associated with development of multiple adverse cardiovascular morbidities. In clinical practice, it is often quantified using frequency, pattern and time spent in it. A predictive tool of hypoxemia occurrence will aid clinicians in risk stratifying infant oxygenation patterns and improving personalized care. As a first step towards this goal in characterizing the underlying temporal processes, we studied inter-hypoxemia interval distributions in preterm infants on oxygen supplementation. We derived regression relationships of characterizing parameters of the distributions with gestational age and birth weight of infants. The modeling and goodness of fit tests of pooled and individual inter-hypoxemia intervals indicated that the inverse Gaussian and Birnbaum Saunders distributions fit well over short time scales and the lognormal at longer time scales. Information from distribution modeling may provide insights into hypoxemia recurrence times and be helpful in developing models to predict severe hypoxemic events that may be translated to personalized care in clinical settings. Clinical relevance - Understanding the stochastic nature of temporal processes underlying hypoxemia in preterm infants is a critical step towards developing predictive models for their occurrence. This may potentially aid in the neonatal care and treatment of these vulnerable infants.

Accurate estimation of heart rate dynamics in preterm infants is important for predicting recurrent episodes of severe bradycardia. We hypothesize that estimation of heart rate can be improved by including respiration as a state variable, based on mechanisms that underlie cardio-respiratory coherence. For ten preterm infants, we demonstrate that including respiration as a covariate improves estimation accuracy by an average of 11% across bradycardia severity, and reduces the maximum error by 8%. We also find that cardio-respiratory coherence increases in low frequency content just prior to severe bradycardia. Thus, incorporating respiratory information may improve models of heart rate dynamics and narrow potential features for bradycardia prediction.

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.

In neonatal research, physiological signals are often degraded by an artifact generated by movement of the infant. Portions of these movement embedded signals are commonly excluded in the analysis of the relevant physiological signal. However, movement may be a significant marker of physiological development of the infant. Here we present results from a wavelet-based algorithm that quantifies neonatal movement, using recordings from the pulse plethysmograph. We suggest that movement-induced artifactual signal can yield important physiological information regarding neonatal physiology.

Interbreath interval (IBI), the time interval between breaths, and its variations in time around the mean, the IBI variability, are important measures associated with irregularity of breathing. The IBI histogram generally follows a power law distribution with its characterizing parameters changing with maturation. To assess the dynamics of breathing we propose a point process model of IBI with a lognormal parametric structure to appropriately represent the stochastic nature of the IBI distribution. We estimate the time varying evolution of the characterizing parameters to represent the dynamic nature of breathing, and thereby provide a time-varying measure of irregularity in breathing. The reliability of the model to capture the data is assessed using Kolmogorov-Smirnov (KS) and independence tests. Our results validate the novel approach in the assessment of the irregularity of breathing by analyzing respiratory recordings from newborn rats and preterm infants.

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.