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A computational stochastic model of breathing embedded with fractal-like scaling

Faculty Mentor: Brett BuSha

Student: George Banis

While conscious, there is a stochastic feature of cardiorespiratory control that is responsible for natural variability, which can be expressed as a distribution of breath-to-breath (BBI) or heartbeat-to-heartbeat intervals (RRI). The integrative nature of the brain imparts memory into this system, where any present BBI or RRI is related to past system behavior.  The objective of this research was to design and implement a computer model  (SIMOD) that simulates the natural stochastic and integrative behavior of cardiorespiratory activity. Breathing and heart rate data were recorded from 14 human subjects and model probability density functions with 32 bins from the BBI and RRI data were constructed. A sixth order polynomial curve was fit to each of the 28 distributions, and mean curves to describe the sample population-based BBI and RRI distributions were generated. Each distribution was used to generate random BBI and RRI sequences. Cardiorespiratory system memory was quantified for each data set using an autocorrelation function. Memory from two past values of BBI and RRI values were imprinted onto any present BBI or RRI value.  Temporal scaling was used as a measure of system memory, and was quantified using detrended fluctuation analysis. After optimization with real data, the SIMOD artificially generated BBI or RRI sequences with a similar pattern in memory, variability, and distribution profile to that of healthy human breathing and heartbeat rhythm.  This model would provide a non-invasive method for further understanding of the body’s innate control over respiratory function, and can be utilized for other stochastic physiological processes as well.