Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Abstract

Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP → RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP → RR ), nonbaroreflex ( U RESP → RR ) and baroreflex-mediated ( R RESP , SBP → RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP → RR ), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.

Respiratory Sinus Arrhythmia Mechanisms in Young Obese Subjects

“…As a second step, we applied a recently proposed method, framed in information theory, to dissect causal interactions in multivariate time series according to the so-called PID (Williams and Beer, 2010;Faes et al, 2017Faes et al, , 2018Krohova et al, 2019). PID was used in order to dissect the information Values are expressed as mean (SD).…”

“…PID enables also to separate redundant TE from the synergistic TE (S RESP,SBP→RR , related to the excess of information that two sources transfer to the target when they are considered together compared to the sum of the information transferred by both sources separately) -in this study analysis of synergy was not included in the results. The computation of these measures is based on a linear parametric modeling of the three time series which is described in detail elsewhere (Williams and Beer, 2010;Faes et al, 2017Faes et al, , 2018Krohova et al, 2019). From a physiological point of view, these measures represent various phenomena: the unique TE U SBP→RR can be thought as reflecting the strength of the effects of SBP on RR unrelated to RESP occurring along the cardiac chronotropic baroreflex arm, while the unique TE U RESP→RR represents the baroreflexindependent effect of RESP on RR [i.e., the non-baroreflex (mostly central) mechanism of RSA].…”

“…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.…”

“…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).…”