Horizontal visibility graph analysis reveals developmental patterns in heart rate dynamics from neonates to adults

Characterizing the developmental trajectory of cardiac autonomic regulation from birth to adulthood remains a methodological challenge, as conventional heart rate variability (HRV) metrics are limited in their ability to capture the nonlinear and structural complexity of cardiac dynamics. Here we propose the application of horizontal visibility graph (HVG) analysis, a graph-theoretic transformation of time series that encodes temporal ordering as network topology, to quantify developmental signatures of autonomic maturation across eight age groups spanning neonates to adults. Three HVG-derived descriptors (average degree, clustering coefficient, and transitivity) and the short-term detrended fluctuation analysis exponent (DFA- α 1 ) were extracted from RR interval series of 138 healthy subjects. All four metrics exhibited significant, monotonically increasing age-related trends from infancy to adolescence, with large effect sizes for transitivity and clustering coefficient, and strong correlations with established HRV indices. No significant sex differences were detected, and robustness analyses confirmed metric stability across a wide range of series lengths and noise levels. These results establish HVG analysis as a computationally efficient and physiologically interpretable framework for studying cardiac autonomic development, complementing fractal and time-domain approaches and opening avenues for its application in pediatric cardiology.