Significant differences in physiological noise were observed in respiratory dynamics across various breathing patterns, but did not affect cardiac dynamic noise.
Dynamical Bayesian inference can successfully detect and extract dynamic physiological noise from cardio-respiratory signals during varying breathing patterns.
Absolute Event Rate: 0% vs 0%
The systems in nature are rarely isolated and there are different influences that can perturb their states. Dynamic noise in physiological systems can cause fluctuations and changes on different levels, often leading to qualitative transitions. In this study, we explore how to detect and extract the physiological noise, in terms of dynamic noise, from measurements of biological oscillatory systems. Moreover, because the biological systems can often have deterministic time-varying dynamics, we have considered how to detect the dynamic physiological noise while at the same time following the time-variability of the deterministic part. To achieve this, we use dynamical Bayesian inference for modeling stochastic differential equations that describe the phase dynamics of interacting oscillators. We apply this methodological framework on cardio-respiratory signals in which the breathing of the subjects varies in a predefined manner, including free spontaneous, sine, ramped and aperiodic breathing patterns. The statistical results showed significant difference in the physiological noise for the respiration dynamics in relation to different breathing patterns. The effect from the perturbed breathing was not translated through the interactions on the dynamic noise of the cardiac dynamics. The fruitful cardio-respiratory application demonstrated the potential of the methodological framework for applications to other physiological systems more generally.
Lukarski et al. (Mon,) reported a other. Significant differences in physiological noise were observed in respiratory dynamics across various breathing patterns, but did not affect cardiac dynamic noise.