Krešimir Milas

Heart rate variability (HRV) has been analysed using linear and nonlinear methods. In the framework of a controlled neonatal stress model, we applied tone-entropy (T-E) analysis at multiple lags to understand the influence of external stressors on healthy term neonates. Forty term neonates were included in the study. HRV was analysed using multi-lag T-E at two resting and two stress phases (heel stimulation and a heel stick blood drawing phase). Higher mean entropy values and lower mean tone values when stressed showed a reduction in randomness with increased sympathetic and reduced parasympathetic activity. A ROC analysis was used to estimate the diagnostic performances of tone and entropy and combining both features. Comparing the resting and simulation phase separately, the performance of tone outperformed entropy, but combining the two in a quadratic linear regression model, neonates in resting as compared to stress phases could be distinguished with high accuracy. This raises the possibility that when applied across short time segments, multi-lag T-E becomes an additional tool for more objective assessment of neonatal stress.

We aim at detecting stress in newborns by observing heart rate variability (HRV). The HRV features nonlinearities. Fractal dynamics is a usual way to model them and the Hurst exponent summarizes the fractal information. In our framework, we have observations of short duration, for which usual estimators of the Hurst exponent, like detrended fluctuation analysis (DFA), are not adapted. Moreover, we observe that the Hurst exponent does not vary much between stress and rest phases, but its decomposition in memory and underlying properties of the probability distribution leads to satisfactory diagnostic tools. This decomposition of the Hurst exponent is in addition embedded in a mean-reverting model. The resulting model is a mean-reverting fractional Lévy stable motion (FLSM). We estimate it and use its parameters as diagnostic tools of neonatal stress. Indeed, the value of the speed of reversion parameter is a significant indicator of stress. The evolution of both parameters in which the Hurst exponent is decomposed provides us with significant indicators as well. On the contrary, the Hurst exponent itself does not bear useful information.

Abstract Objective : To detect stress in newborns by observing heart rate (HR) variability utilizing an asymmetric detrended fluctuation analysis (ADFA), we sought to determine the fractal structure of the series of inter-beat intervals, so as to distinguish the periods of acceleration of the HR from decelerations. Thus, two scaling exponents, α + and α − , representing decelerations and accelerations respectively, are obtained. Approach : Forty healthy term newborns were included in this study, undergoing two different types of stress stimuli: routine heel lance blood sampling for metabolic screening purposes, and its simulation by applying dull pressure on the heel. Main results : It appears that when newborns face stress, the scaling exponent related to accelerations significantly increases and becomes higher than the deceleration scaling exponent. To test the diagnostic properties of the scaling exponents, an ROC curve analysis was applied; α − showed good diagnostic performance with an AUC between 0.626 and 0.826, depending on the length of the time series. The joint use of α + and α − further increased the diagnostic performance, in particular for shorter series of RR intervals, with an AUC between 0.691 and 0.833. Significance : ADFA, particularly of the acceleration scaling exponent, may be a useful clinical diagnostic tool for monitoring neonatal stress.