A Stochastic and Mathematically Integrative Model of the Gender Modulation of Cardiorespiratory Activity

Area of Science:

• Cardiorespiratory Physiology
• Biomathematics
• Nonlinear Dynamics

Background:

• Breath-to-breath interval (BBI) and heartbeat-to-heartbeat interval (RRI) variability exhibit complex random and temporally scaled characteristics.
• Understanding gender-based differences in cardiorespiratory variability at rest is crucial for physiological research.

Purpose of the Study:

• To design and validate a stochastic and mathematically integrative model (SIM) capable of replicating gender-specific variations in breathing and heart rate variability.
• To assess the model's ability to reproduce fractal scaling properties observed in human cardiorespiratory data.

Main Methods:

• Recorded BBI and RRI sequences from 12 healthy subjects.
• Estimated inter-breath and inter-beat memory using autocorrelation functions and discrete probability density functions (PDFs).
• Generated artificial BBI/RRI sequences using the SIM, integrating random interval values from PDFs with autocorrelation parameters. Quantified fractal scaling via detrended fluctuation analysis.

Main Results:

• A significant gender difference was observed in the autocorrelation coefficients of BBI.
• The SIM successfully generated artificial BBI and RRI sequences exhibiting significant fractal scaling compared to surrogate data (p < 0.001).
• The model-generated BBI sequences mirrored the significant gender-based differences found in the original human data (p < 0.01).

Conclusions:

• The developed stochastic integrative model effectively replicates gender-based differences in fractal scaling of resting human breathing patterns.
• This model provides a valuable tool for studying cardiorespiratory dynamics and associated physiological variations.