Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep

Abstract

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

Fractal Physiology, Breath-to-Breath Variability and Respiratory Diseases: An Introduction to Complex Systems Theory Application in Pulmonary and Critical Care Medicine

“…PSD of breathing signals can determine which fraction of variational activity is oscillatory at a particular frequency on a breath-to-breath basis. It has been proposed that the standard deviation (SD) for each breath component can be considered as a measure of gross breath-to-breath variability [13,17].…”

Nonlinear systems identification: autocorrelation vs. autoskewness

“…The classical view of stable homeostasis is that, via bilateral negative feedback, a physiological control system seeks to maintain its trajectory within a neighborhood of a single X. In this case, the dynamics local to X depend primarily on the linearization of f: dX t /dX t Ϫ d for d ϭ 1,...,D; although the assumption is often unstated, many analysts proceed on the assumption that higher-order terms on X t are negligible, i.e., d n X t /d X t Ϫ d n Ϸ 0 for all d Ͼ 0 and n Ͼ 1 (1,13,14,23,26,36). Inasmuch as this can result in biased parameter estimates for physiological systems with questionable local stability (e.g., patients with pathological homeostasis), a simple model-independent statistical method is needed to quantify deviations from linearity.…”

“…For nonlinear systems with nonzero second derivatives, r 2 depends also on the SNR, with system memory generally increasing with level of noise (23). Less familiar, however, is the concept that the mean and skewness of the output signal can also be viewed in terms of deterministic and random components.…”

“…For example, many investigators have found that variables such as depth or rate of breathing exhibit a positive breath-to-breath autocorrelation (1,13,23,26,36), a result that has been interpreted as evidence of ''memory'' within the central pattern generator (1) or ''noise'' within the peripheral feedback control loop (23). Regardless of physiological interpretations, nonzero autocorrelation implies that a fraction of the signal variance has a nonrandom component (see APPENDIX A).…”

Variability of End-Expiratory Lung Volume in Premature Infants

“…Excessive variability has also been reported as a sign of deleterious instability [6,7] , and an equilibrium is probably necessary [8] . Using time-series analysis, it has been shown that the magnitude of a component of one breath is positively correlated with the magnitude of the component of the preceding breath [9][10][11][12] . This observation that a given breath is followed by breaths with similar characteristics has been called 'shortterm memory' [10] , despite the fact that it does not necessarily originate from a neural mechanism.…”