Basic cardiovascular variability signals: mutual directed interactions explored in the information domain

Abstract

The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct + indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR → DBP → SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP → RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP → RR interactions, as well as of weaker RR → DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.

Respiratory Sinus Arrhythmia Mechanisms in Young Obese Subjects

“…Applying PID analysis on the raw measured cardiovascular and respiratory time series, the orthostatic stress induced by HUT (but not the cognitive load induced by MA) resulted in an increased involvement of the high-pressure baroreflex as expressed by an increase in unique TE from SBP to RR in control group. This observation is in concert with the results of previous studies where the effect of orthostasis on the strength of the cardiac chronotropic baroreflex arm was analyzed in the frequency domain (Nollo et al, 2005) and using information-theoretic methods (Faes et al, 2013;Javorka et al, 2017). Higher baroreflex influence on heart rate was demonstrated also in both groups during orthostasis by an increase of the redundancy between respiratory and arterial pressure effects on HRV, indicating an increased importance of the indirect pathway RESP → SBP → RR during the unloading of baroreceptors associated with HUT.…”

“…Although in its original formulation PID analyzes the "raw" original time series measured from ECG, arterial pressure, and RESP signals, a recent development based on filtering the time series in order to eliminate the short temporal scales allows to compute the PID measures with reference to the slower oscillations (long time scales) contained in the observed processes (Williams and Beer, 2010;Faes et al, 2017Faes et al, , 2018Krohova et al, 2019). Thus, while interactions between cardiovascular and respiratory time series are dominantly reflected at the short time scales (Faes et al, 2012;Javorka et al, 2017) included in the raw unfiltered time series, the advantage of multiscale PID is that all the above mentioned information measures could be calculated at any assigned time scale τ. In this study, in addition to raw time series analyzed at a time scale τ 1 = 1 which includes all oscillations, we calculated PID measures also for a longer scaleτ 2 determined -for each subject and experimental conditionas the time scale which removes the oscillations in the HF band and thus evidences slower oscillations [we refer to Krohova et al (2019) for more detailed information].…”

“…As the next step, the 300 beats lasing segments of RR interval, the systolic blood pressure (SBP), and respiration volume signal (RESP) were extracted from the continuous recordings. For more detailed information about the protocol and time series extraction see Javorka et al (2017) and Krohova et al (2019).…”

Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

“…Specifically, we compare the abilities of LMSE and RMSE in detecting the multiscale complexity of heart period (HP), systolic arterial pressure (SAP), and respiration (RESP) time series measured from a large group of Complexity 9 healthy subjects in a resting state condition as well as during two types of physiological stress, that is, postural stress and mental stress [35].…”

“…The analyzed time series belong to a database collected to assess the dynamics of HP, SAP, and RESP during two types of physiological stress commonly studied in cardiovascular variability, that is, postural stress induced by head-up tilt (HUT) and mental stress induced by mental arithmetics (MA); we refer to [35,36] for a detailed description of the population and the experimental setup. Briefly, a group of 61 young healthy subjects (37 females, 17.5 ± 2.4 years old) were monitored in a relaxed state in the resting supine position (SU), in the upright position during HUT, and in the supine position during MA, measuring the surface electrocardiogram (ECG), the finger arterial blood pressure collected noninvasively by the photoplethysmographic method, and the respiration signal measured through respiratory inductive plethysmography.…”