Age-dependent patterns of cardiac complexity unveiled by topological data analysis of pediatric heart rate variability

Abstract

Heart rate variability (HRV) is a well-established marker of autonomic regulation and undergoes profound maturation during early human development. In this study, topological data analysis (TDA) is applied to investigate the evolving geometric complexity of HRV across pediatric developmental stages. Using persistent homology in homological dimension 1, we extracted topological descriptors from time-delay embedded RR interval series of 127 individuals aged 1 month to 17 years. We identified statistically significant, age-dependent transformations in the topological structure of HRV signals. Neonates and infants exhibited a greater number and strength of persistent features, reflecting highly heterogeneous cardiac control dynamics during early autonomic maturation. In contrast, adolescents displayed reduced topological complexity and increased entropy, suggesting a shift toward more uniform and structured physiological control. Topological measures correlated with conventional HRV indices, confirming their physiological relevance. Furthermore, pairwise distances between persistence landscapes revealed an inverse relationship between intra-group topological variability and classical HRV measures. Collectively, our findings demonstrate that persistent homology provides a powerful, multiscale-aware framework to capture developmental trajectories in cardiac autonomic regulation, with potential applications in pediatric monitoring, developmental physiology, and early detection of dysautonomia.