Evolution of cardiorespiratory interactions with age

Abstract

We describe an analysis of cardiac and respiratory time series recorded from 189 subjects of both genders aged 16–90. By application of the synchrosqueezed wavelet transform, we extract the respiratory and cardiac frequencies and phases with better time resolution than is possible with the marked events procedure. By treating the heart and respiration as coupled oscillators, we then apply a method based on Bayesian inference to find the underlying coupling parameters and their time dependence, deriving from them measures such as synchronization, coupling directionality and the relative contributions of different mechanisms. We report a detailed analysis of the reconstructed cardiorespiratory coupling function, its time evolution and age dependence. We show that the direct and indirect respiratory modulations of the heart rate both decrease with age, and that the cardiorespiratory coupling becomes less stable and more time-variable.

Information Decomposition in Bivariate Systems: Theory and Application to Cardiorespiratory Dynamics

“…A famous example in neuroscience is given by dynamic causal modeling [5] which explicitly considers the biophysical interactions among inaccessible neural populations as well as their mapping to the measured variables. In cardiac physiology, approaches modeling the temporal dynamics of the amplitude [6] or the phase [7,8] of heart rate and respiration variables have been proposed to assess cardiopulmonary dynamics. The other, apparently unrelated approach relies on explicitly incorporating the flow of time into structural causal modeling, in a way such that cyclic interactions turn into acyclic graphs when the graph nodes are deployed over subsequent time steps [3,9].…”

Time-Frequency Wavelet Based Coherence Analysis of EEG in EC and EO during Resting State

“…To analyze the interrelationships between the times series, we require an appropriate method of time series analysis. In the last decades, a multivariate nature of data has paid much attention including applications such as synchronization analysis [12], wavelet-base time frequency coherence to study the neuronal oscillatory pattern (Rodrigo et al, 2008), interhemispheric, intrahemispheric and distal EEG coherence in Alzheimer's disease (AD) [13], waveletbase phase coherence to investigate the relationship between temperature, blood flow, instantaneous heart rate , harmonic detection in nonsinusodial oscillations and time-localized coherence [14,15], Cardiorespiratory interaction with aging [16], fault diagnosis of rotary machines using wavelet (Yan et al, 2014) and Wavelet Phase coherence analysis of arterial blood pressure and cerebral tissue oxyhemoglobin concentrations signals to assess the dynamic cerebral autoregulation (dCA) in response to posture change (Gao et al, 2015).…”

Time-varying coupling functions: Dynamical inference and cause of synchronization transitions

“…Decomposition of a coupling function can also facilitate a description of the functional contributions from each separate subsystem within the coupling relationship. Different methods for coupling function detection have been applied widely in chemistry [10,11,[14][15][16], in cardiorespiratory physiology [12,13,17], in neuroscience [18][19][20], in mechanical interactions [21], in social sciences [22] and in secure communications [23]. The study of coupling function is a very active and expanding field of research [24].…”

“…For example, the reconstructed coupling function of BelousovZhabotinsky chemical interactions revealed how there could be higher-harmonics and bi-stability of the synchronization state [10], the knowledge of the coupling function of one pairwise interaction was used to predict the synchronization and clustering of a network of electrochemical oscillators [11], and the form of the cardiorespiratory coupling function was linked to respiratory sinus arrythmia (RSA), a known mechanism in physiology [12,13]. The coupling function as a whole can be described in terms of its strength and form.…”