title: A stochastic and integrative model of breathing.
abstract: 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.
authors.affiliation: 2000 Pennington Road, Department of Biomedical Engineering, School of Engineering, The College of New Jersey, Ewing, NJ 08628, United States. Electronic address: busha@tcnj.edu.
authors.forename: Brett F
authors.initials: BF
authors.lastname: BuSha
authors.affiliation: 2201 J.M. Patterson Building, University of Maryland, College Park, MD 20742, United States. Electronic address: gbanis@umd.edu.
authors.forename: George
authors.initials: G
authors.lastname: Banis
mesh_terms: D000293:Adolescent; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D008962:Models, Theoretical; D012119:Respiration; D013269:Stochastic Processes; D055815:Young Adult
keywords: Respiratory model; Stochastic system; Temporal correlations