Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects

Abstract

Abstract-We sought to quantify the fractal scaling properties of human respiratory dynamics and determine whether they are altered with healthy aging and gender. Continuous respiratory datasets ͑obtained by inductive plethysmography͒ were collected from 40 healthy adults ͑10 young men, 10 young women, 10 elderly men, and 10 elderly women͒ during 120 min of spontaneous breathing. The interbreath interval ͑IBI͒ time series were extracted by a new algorithm and fractal scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis. Under supine, resting, and spontaneous breathing conditions, both healthy young and elderly subjects had scaling exponents for the IBI time series that indicate long-range ͑fractal͒ correlations across multiple time scales. Furthermore, the scaling exponents ͑mean Ϯ SD͒ for the IBI time series were significantly (pϽ0.03) lower ͑indicating decreased correlations͒ in the healthy elderly male (0.60Ϯ0.08) compared to the young male (0.68Ϯ0.07), young female (0.70Ϯ0.07), and elderly female (0.67Ϯ0.06) subjects. These results provide evidence for fractal organization in physiologic human breathing cycle dynamics, and for their degradation in elderly men. These findings may have implications for modeling integrated respiratory control mechanisms, quantifying their changes in aging or disease, and assessing the outcome of interventions aimed toward restoring normal physiologic respiratory dynamics.

Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration

“…The use of approximate entropy also allowed Akay et al (2003) to describe age-related changes in the complexity of respiratory pattern, and to demonstrate that an hypoxic insult to the brain resulted in a decreased respiratory complexity (Akay and Sekine, 2004). Fractal fluctuations have been described in spontaneously breathing healthy adult humans (Fadel et al, 2004) and a decrease in respiratory complexity with age has also been shown by Peng et al (2002) through the analysis of fractal scaling exponents. Baldwin et al (2004), exploring the role of sigh in respiratory control with a combination of linear and nonlinear approaches (including long-range memory studied from detrended fluctuation analysis) showed that sighs were followed by a "restoration" of respiratory complexity under the form of an increase in the range of points located within a defined attractor.…”

Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking

“…Alternatively, one might suggest that there are short-range correlations in the stride interval such that the current value is influenced by only the most recent stride intervals, but over the long term, the fluctuations are random. A third, less intuitive possibility is that the fluctuations in the stride interval exhibit long-range, fractal-like correlations, as seen in a class of scalefree phenomena including heart rate beat-to-beat fluctuations (Goldberger et al, 2002;Peng et al, 1993;Peng et al, 1998;Peng et al, 1994;Peng et al, 2002). In this case, the stride interval at any instant would 'depend' (in a statistical mechanics sense) on the interval at relatively remote times, and this dependence would decay in a scale-free (fractal-like), powerlaw fashion.…”

Fractal fluctuations in human respiration

“…Peng et al (24) used detrended fluctuation analysis to demonstrate the fractal nature of interbreath interval fluctuations in healthy adult subjects. We have confirmed this finding using a different method of fractal analysis, and, in addition, demonstrated fractal fluctuations of two other respiratory parameters.…”

“…The results indicate that, in most cases, apparently random fluctuations in respiratory pattern are, in fact, correlated over more than one time scale. Moreover, the data suggest that fractal fluctuations in breath number, respiratory period, and breath amplitude are controlled by separate processes.Allan and Fano factors; breath amplitude and frequency; dispersional analysis; Hurst exponent RESPIRATION IN AWAKE, HEALTHY adult humans is characterized by considerable variability in the frequency, duration, and amplitude of breaths (5,8,18,24,31). The aim of the present study was to define the basis for the variability of these respiratory parameters.…”

“…Allan and Fano factors; breath amplitude and frequency; dispersional analysis; Hurst exponent RESPIRATION IN AWAKE, HEALTHY adult humans is characterized by considerable variability in the frequency, duration, and amplitude of breaths (5,8,18,24,31). The aim of the present study was to define the basis for the variability of these respiratory parameters.…”