Evolution of cardiorespiratory interactions with age.

Area of Science:

• Physiology
• Biomedical Engineering
• Data Science

Background:

• Cardiorespiratory interactions are crucial for physiological regulation.
• Previous methods for analyzing these interactions have limitations in time resolution.
• Understanding age-dependent changes in cardiorespiratory coupling is important for health monitoring.

Purpose of the Study:

• To analyze cardiorespiratory time series from a diverse age group.
• To investigate the time-varying nature of cardiorespiratory coupling.
• To quantify age-related changes in cardiorespiratory synchronization and modulation.

Main Methods:

• Utilized the synchrosqueezed wavelet transform for precise extraction of cardiac and respiratory frequencies and phases.
• Employed Bayesian inference to model heart and respiration as coupled oscillators.
• Derived measures of synchronization, coupling directionality, and underlying mechanisms.

Main Results:

• Successfully extracted cardiorespiratory frequencies and phases with enhanced time resolution.
• Quantified time-dependent coupling parameters, revealing significant age-related decreases in respiratory modulation of heart rate.
• Observed that cardiorespiratory coupling becomes less stable and more time-variable with increasing age.

Conclusions:

• The synchrosqueezed wavelet transform and Bayesian inference provide a robust framework for analyzing cardiorespiratory dynamics.
• Cardiorespiratory coupling weakens and becomes more dynamic with advancing age.
• These findings have implications for understanding age-related physiological changes and developing personalized health interventions.