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A Stochastic and Integrative Model of Human Breathing

The integration of central drive and respiratory‐related feedback imparts stochastic and temporally‐correlated behaviors into the breath‐to‐breath (BBI) interval. The objective of this study was to design a stochastic and integrative model of breathing (SIMB) that replicated the natural random and fractal‐like pattern of BBI. During quiet wakefulness and two progressive levels of exercise, 7 female and 7 male subjects wore a resistive belt to record changes in chest circumference. BBI data were identified using a locally designed peak detection algorithm. For each BBI sequence, a 28 bin BBI histogram was constructed, and then modeled with a 6th order polynomial curve. A computational memory function was optimized using autocorrelation analysis of the original BBI sequences. SIMB sequences were generated by randomly sampling intervals from each distribution model, and then applying the computational memory function. Temporal correlations were quantified using detrended fluctuation analysis (DFA). The SIMB generated BBI sequences that contained significant temporal scaling that were validated through the random shuffling of the BBI sequences (p < 0.001). Additionally, the SIMB and human data were quantitatively similar in fractal‐scaling. In conclusion, the new SIMB successfully reproduced the distribution and fractal properties found in normal adult breathing during rest and exercise.