Sample entropy analysis of neonatal heart rate variability

TLDR

Neonatal sepsis is associated with early heart‑rate variability reductions and transient decelerations, and sample entropy quantifies the likelihood that similar short‑term epochs remain similar. The study aimed to evaluate sample entropy dynamics in neonatal sepsis, optimize parameters m and r, assess missing‑data effects, and reassess prior interpretations of approximate entropy. Researchers calculated sample entropy on 89 NICU admissions (21 sepsis cases), performed numerical simulations, and investigated optimal m, r selection and missing‑data tolerance. Entropy values dropped before clinical sepsis onset, were robust to missing points, and the decline was driven by spikes rather than reduced regularity.

Abstract

Abnormal heart rate characteristics of reduced variability and transient decelerations are present early in the course of neonatal sepsis. To investigate the dynamics, we calculated sample entropy, a similar but less biased measure than the popular approximate entropy. Both calculate the probability that epochs of window length m that are similar within a tolerance r remain similar at the next point. We studied 89 consecutive admissions to a tertiary care neonatal intensive care unit, among whom there were 21 episodes of sepsis, and we performed numerical simulations. We addressed the fundamental issues of optimal selection of m and r and the impact of missing data. The major findings are that entropy falls before clinical signs of neonatal sepsis and that missing points are well tolerated. The major mechanism, surprisingly, is unrelated to the regularity of the data: entropy estimates inevitably fall in any record with spikes. We propose more informed selection of parameters and reexamination of studies where approximate entropy was interpreted solely as a regularity measure.