Comparing Generalized and Phase Synchronization in Cardiovascular and Cardiorespiratory Signals

Abstract

We made use of multivariate nonlinear analysis methods to study the interdependence between the cardiac interval variability and both the respiratory activity and the systolic pressure in rats. The study was carried out in basal conditions and after the application of different drugs affecting the cardiovascular system. The results showed that there are changes both in the extent and in the directionality of such interdependences because of the drugs. The inhibition of the NO and the parasympathetic blockade changed the cardiovascular coordination, with the latter one also modifying the interdependence between the cardiac interval and the respiratory signal. This suggests that the nonlinear approach might be very helpful to explore the interaction between subsystems of the cardiovascular control system.

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

“…Meanwhile, respiration pattern or waveform morphology also carries information about health and diseases [23,30], especially for research on cardio-respiratory interaction and evaluation of autonomic nervous system activity, which has been an area of interest for years [12,27]. Compared with low-pass filters, adaptive filters are more likely used to recover the original shape of the respiratory waveform, and more accurate information can be acquired from the dynamically filtered respiratory signals.…”

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure

“…The change of phase dynamics during PB procedure, however, has rarely been explored simultaneously. As coupled oscillators interact both through their phases and amplitudes, analysis of phase dynamics has been used to investigate the strength and direction of CPC [3,27,30,34]. Compared with amplitude-based coupling analysis, phase dynamics are more sensitive to the subtle change of coupling strength [25].…”

Assessment of Granger Causality by Nonlinear Model Identification: Application to Short-term Cardiovascular Variability

“…Applications are ubiquitous in fields ranging from economics 11 to climatology 20 and to the analysis of biological systems, with particular emphasis on neural 26 and cardiac signals. In the latter application, a large body of work has been developed to assess causality in both cardiovascular 8,9,22,23,25 and cardiorespiratory 25,32 interactions.…”

“…2,5,10 Nonlinear Granger causality methods are appropriate for studying the complex cardiovascular and cardiorespiratory systems, since couplings are most likely to be manifested by nonlinear dynamics. 23,25,29,33 The main problem with the current nonlinear Granger causality methods is that they require long data records which preclude their application to short-term cardiovascular data.…”