Frequency-Dependent Contribution Topology of ECG-Derived Intervals and Respiration Using a Multivariate Autoregressive Model: Toward a Network Physiology Assessment of Cardiorespiratory Variability

Conventional heart rate variability (HRV) analysis primarily characterizes R–R interval (RRI) variability, whereas the dynamic contribution structure among multiple electrocardiogram (ECG)-derived intervals and respiration remains less well understood. This study investigated the frequency-dependent organization of P–P interval (PPI), RRI, T–T interval (TTI), and respiration during postural change. Thirteen healthy young participants underwent simultaneous ECG and respiratory recording in the supine and standing positions. A four-variable multivariate autoregressive (MVAR) model was constructed, and relative power contribution (RPC) was quantified in the very-low-frequency, low-frequency (LF), and high-frequency (HF) bands and visualized as directed network topologies. Conventional HRV analysis confirmed marked autonomic modulation during standing, including shortened RRI and pronounced attenuation of HF power. RPC analysis identified 14 posture-related differences after false discovery rate correction across all 32 LF- and HF-band Source–Target comparisons. In the LF band, several TTI-related contributions increased during standing, including TTI→PPI and TTI→RRI. In the HF band, multiple PPI- and RRI-related cross-variable contributions and RRI self-contribution decreased, whereas TTI self-contribution increased markedly. Seven of the eight significant HF findings remained significant after excluding participants whose respiratory peak frequency entered the LF band. Furthermore, nine of the 14 significant RPC components remained significant across all tested P- and T-wave timing perturbations up to ±10 ms. These findings indicate that postural change reorganizes the frequency-dependent contribution structure among ECG-derived intervals and respiration. From a Network Physiology perspective, the proposed framework extends conventional RRI-centered HRV analysis by characterizing the multivariate structure and state-dependent reorganization of cardiac–respiratory dynamics.