A stochastic and integrative model of breathing

Human breathing patterns contain both temporal scaling characteristics, and an innately random component. A stochastic and mathematically integrative model of breathing (SIMB) that simulated the natural random and fractal-like pattern of human breathing was designed using breath-to-breath interval (BBI) data recorded from 14 healthy subjects. Respiratory system memory was estimated with autocorrelation, and a probability density function (PDF) was created by fitting a polynomial curve to each normalized BBI sequence histogram. SIMB sequences were produced by randomly selecting BBI values using a PDF and imparting memory with an autocorrelation-based function. Temporal scaling was quantified with detrended fluctuation analysis. The SIMB BBI sequences were embedded with significant fractal scaling (p < 0.001) that was similar to the human data (p > 0.05), and increasing SIMB output length did not alter the temporal scaling (p > 0.05). This study demonstrated a new computational model that can reproduce the inherent stochastic and time scaling characteristics of human breathing.