Predictive Modeling of Heart Rate from Respiratory Signals at Rest in Young Healthy Humans.

Area of Science:

• Physiology
• Biomedical Engineering
• Computational Biology

Background:

• Biological signals like respiration (RSP) and heart rate (HR) are oscillatory and coupled, essential for maintaining homeostasis.
• Understanding the dynamic interplay between RSP and HR is key to comprehending cardiorespiratory regulation.

Purpose of the Study:

• To model the dynamic relationship between RSP and HR in healthy adults at rest.
• To investigate the time lag and modulation of HR by RSP signals.

Main Methods:

• Cross-correlation analysis of continuous RSP and HR signals.
• Regression analysis to predict HR from RSP and its time derivative.
• Simulation modeling to replicate the observed RSP-HR relationship.

Main Results:

• A significant time lag (849.21 ms ± 344.84) was found between RSP and HR signals.
• HR modulation by RSP was consistently mediated by RSP amplitude and its first-order time derivative.
• A simulation model successfully replicated the physiological RSP-HR dynamics.

Conclusions:

• Cardiorespiratory coupling at rest exhibits a short time lag and HR modulation by RSP's derivative.
• Findings support incorporating these dynamics into models of resting cardiopulmonary coupling.
• Suggests a mechanism for optimizing alveolar gas exchange through cardiorespiratory synchrony.