Causal analysis of short-term cardiovascular variability: state-dependent contribution of feedback and feedforward mechanisms

Concomitant evaluation of cardiovascular and cerebrovascular controls via Geweke spectral causality to assess the propensity to postural syncope

The evaluation of propensity to postural syncope necessitates the concomitant characterization of the cardiovascular and cerebrovascular controls and a method capable of disentangling closed loop relationships and decomposing causal links in the frequency domain. We applied Geweke spectral causality (GSC) to assess cardiovascular control from heart period and systolic arterial pressure variability and cerebrovascular regulation from mean arterial pressure and mean cerebral blood velocity variability in 13 control subjects and 13 individuals prone to develop orthostatic syncope. Analysis was made at rest in supine position and during head-up tilt at 60°, well before observing presyncope signs. Two different linear model structures were compared, namely bivariate autoregressive and bivariate dynamic adjustment classes. We found that (i) GSC markers did not depend on the model structure; (ii) the concomitant assessment of cardiovascular and cerebrovascular controls was useful for a deeper comprehension of postural disturbances; (iii) orthostatic syncope appeared to be favored by the loss of a coordinated behavior between the baroreflex feedback and mechanical feedforward pathway in the frequency band typical of the baroreflex functioning during the postural challenge, and by a weak cerebral autoregulation as revealed by the increased strength of the pressure-to-flow link in the respiratory band. GSC applied to spontaneous cardiovascular and cerebrovascular oscillations is a promising tool for describing and monitoring disturbances associated with posture modification.

A complex systems approach to aging biology

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability

Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of 'instantaneous' effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index of undirected instantaneous causality and (ii) a novel measure of GC including instantaneous effects. An effective procedure to speed up the optimization of parameters in this frame is also presented. After illustrating the proposed formalism in a theoretical example, we apply it to two datasets of cardiovascular and respiratory time series and compare the values obtained within the frequency bands of physiological interest by the proposed total measure of causality with those derived from the standard GC analysis. We find that the inclusion of instantaneous causality allows us to correctly disentangle the baroreflex mechanism from the effects related to cardiorespiratory interactions. Moreover, studying how controlling the respiratory rhythm acts on cardiovascular interactions, we document an increase of the direct (non-baroreflex mediated) influence of respiration on the heart rate in the respiratory frequency band when switching from spontaneous to paced breathing. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Study of the effects of kinesthetic motor imagery in patients with heart failure

OBJECTIVE

The aim was to investigate the acute effect of kinesthetic motor imagery of the 2-minute walk test on hemodynamic and cardiopulmonary variables in patients with heart failure.

METHODS

Twenty participants were recruited for the analysis of these variables before and after the execution and imagination of the 2-minute walk test, with the number of laps executed and imagined being recorded.

RESULTS

The main results observed showed that (1) there was no difference in the number of laps executed and imagined (p=0.41), indicating that the participants actually imagined the test and (2) the motor imagery of the 2-minute walk test immediately increased (p<0.001) the heart and respiratory rates.

CONCLUSION

The motor imagery seems to have acute effects on the cardiopulmonary anticipatory responses of a patient with heart failure.

Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors

Heartbeat regulation is achieved through different routes originating from central autonomic network sources, as well as peripheral control mechanisms. While previous studies successfully characterized cardiovascular regulatory mechanisms during a single stressor, to the best of our knowledge, a combination of multiple concurrent elicitations leading to the activation of different autonomic regulatory routes has not been investigated yet. Therefore, in this study, we propose a novel modeling framework for the quantification of heartbeat regulatory mechanisms driven by different neural routes. The framework is evaluated using two heartbeat datasets gathered from healthy subjects undergoing physical and mental stressors, as well as their concurrent administration. Experimental results indicate that more than 70% of the heartbeat regulatory dynamics is driven by the physical stressor when combining physical and cognitive/emotional stressors. The proposed framework provides quantitative insights and novel perspectives for neural activity on cardiac control dynamics, likely highlighting new biomarkers in the psychophysiology and physiopathology fields. A Matlab implementation of the proposed tool is available online.

Vascular resistance arm of the baroreflex: methodology and comparison with the cardiac chronotropic arm

Baroreflex response consists of cardiac chronotropic (effect on heart rate), cardiac inotropic (on contractility), venous (on venous return) and vascular (on vascular resistance) arms. Because of the simplicity of its measurement, the cardiac chronotropic arm is most often analyzed. The aim was to introduce a method to assess the vascular baroreflex arm and to characterize its changes during stress. We evaluated the effect of orthostasis and mental arithmetics (MA) in 39 (22 women, 17 men; median age: 18.7 yr) and 36 (21 women, 15 men; 19.2 yr) healthy volunteers, respectively. We recorded systolic (SBP) and mean (MBP) blood pressure by volume-clamp method and R-R interval (RR) by ECG. Cardiac output (CO) was recorded by impedance cardiography. From MBP and CO, peripheral vascular resistance (PVR) was calculated. The directional spectral coupling and gain of cardiac chronotropic (SBP to RR) and vascular (SBP to PVR) arms were quantified. The strength of the causal coupling from SBP to PVR was significantly higher than that of SBP to RR coupling over the whole protocol (P < 0.001). Along both arms, the coupling was higher during orthostasis compared with the supine position (P < 0.001 and P = 0.006); no MA effect was observed. No significant changes in the spectral gain (ratio of RR or PVR change to a unit SBP change) across all phases were found (0.111 ≤ P ≤ 0.907). We conclude that changes in PVR are tightly coupled with SBP oscillations via the baroreflex, providing an approach for baroreflex vascular arm analysis with the potential to reveal new aspects of blood pressure dysregulation.NEW & NOTEWORTHY Baroreflex response consists of several arms, but the cardiac chronotropic arm (blood pressure changes evoking heart rate response) is usually analyzed. This study introduces a method to assess the vascular baroreflex arm with the continuous noninvasive measurement of peripheral vascular resistance as an output considering causality in the interaction between oscillations and slower dynamics of vascular tone changes. We conclude that although vascular baroreflex arm involvement becomes dominant during orthostasis, gain of this interaction is relatively stable.

Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale entropy (MSE), has been proven to be unsuitable in the presence of short multivariate time series to be analyzed at long time scales. This work aims at overcoming these issues via the introduction of a new method for the assessment of the multiscale complexity of multivariate time series. The method first exploits vector autoregressive fractionally integrated (VARFI) models to yield a linear parametric representation of vector stochastic processes characterized by short- and long-range correlations. Then, it provides an analytical formulation, within the theory of state-space models, of how the VARFI parameters change when the processes are observed across multiple time scales, which is finally exploited to derive MSE measures relevant to the overall multivariate process or to one constituent scalar process. The proposed approach is applied on cardiovascular and respiratory time series to assess the complexity of the heart period, systolic arterial pressure and respiration variability measured in a group of healthy subjects during conditions of postural and mental stress. Our results document that the proposed methodology can detect physiologically meaningful multiscale patterns of complexity documented previously, but can also capture significant variations in complexity which cannot be observed using standard methods that do not take into account long-range correlations.

Cross-Wavelet Time-Frequency Analysis Reveals Sympathetic Contribution to Baroreflex Sensitivity as Cause of Variable Phase Delay Between Blood Pressure and Heart Rate

Introduction

Baroreflex sensitivity (BRS) is often presented as a single number, but it is actually a frequency-dependent phenomenon whose value changes constantly due to internal and external stimuli. The standing posture, for instance, necessitates a changeover from vagal to sympathetic predominance for cardiovascular control. We present a wavelet cross-spectral analysis of blood pressure (BP) and interbeat interval (IBI) recordings in the search for variations in gain and phase between these signals. Additionally, we show how the lag in sympathetic response dictates BP-to-IBI phase relations.

Methods

Recordings in supine and head-up tilted (HUT) position, obtained earlier in 10 healthy subjects (4f/6m, aged 27–47 years) were used. BP and IBI were measured from the continuous finger pressure (by Finometer). The cross-wavelet analysis produced time- and frequency dependent gain (wBRS, wavelet derived BRS) and phase, using the MATLAB^® wavelet toolbox. We also applied the wBRS method to model-generated BP- and IBI-data with known interrelations to test the results of this analysis technique. Finally, wBRS values were compared with the xBRS-approach, which is a time domain method for continuous BRS estimation in a sliding 10-s window.

Results

In resting supine conditions, wBRS fluctuates; more at respiratory frequencies than in the 0.1 Hz band. After HUT, wBRS at the respiratory frequency decreases from average 22.7 to 8.5 ms/mmHg, phase between BP and IBI increases from −30° to −54°; in the sympathetic 0.1 Hz range these numbers are 13.3→6.3 ms/mmHg and −54°→−59°. The values found by xBRS are intermediate between wBRS-resp and wBRS-0.1 Hz. The Appendix shows that for the simulated data the BRS and phase values as found by the wavelet technique can be explained from vector additions of vagal and sympathetic BRS contributions.

Discussion

During supine rest parasympathetic control of heart rate dominates BRS; after HUT this is diminished and less effective. Due to the reaction times of the autonomic effectors, the phase relations between the signals depend on the relative contribution of the sympathetics, which explains the larger phase shift.

Conclusion

Cross wavelet analysis allows to follow fast BRS changes in time and frequency, while the computed phase relations help understand sympathetic participation.

Quantification of Different Regulatory Pathways Contributing to Heartbeat Dynamics during Multiple Stimuli: a Proof of the Concept

The dynamical interplay between brain and heart is mediated by several feedback mechanisms including the central autonomic network and baroreflex loop at a peripheral level, also for a short-term regulation. State of the art focused on the characterization of each regulatory pathway through a single stressor elicitation. However, no studies targeted the actual quantification of different mediating routes leading to the generation of heartbeat dynamics, particularly in case of combined exogenous stimuli. In this study, we propose a new approach based on computational modeling to quantify the contribution of multiple concurrent stimuli in modulating cardiovascular dynamics. In this preliminary attempt, the model estimates the high-frequency power of heartbeat dynamics, and derives disentangling coefficients quantifying the effect of multiple elicitations. Model evaluation is performed on healthy rate variability (HRV) series from fourteen healthy subjects undergoing physical (tilt-table) and mental stressors (aritmetics), as well as their combined administration. Results indicate that, at a group-wise level, in base of concurrent physical and mental elicitations, the physical stressor contributes for the 85% of the resulting heartbeat dynamics. These findings are in agreement with the current knowledge on heartbeat regulatory systems, providing valuable perspectives on the quantification of underlying generative mechanisms of HRV.

Time-Frequency Analysis of Cardiovascular and Cardiorespiratory Interactions During Orthostatic Stress by Extended Partial Directed Coherence

In this study, the linear method of extended partial directed coherence (ePDC) was applied to establish the temporal dynamic behavior of cardiovascular and cardiorespiratory interactions during orthostatic stress at a 70° head-up tilt (HUT) test on young age-matched healthy subjects and patients with orthostatic intolerance (OI), both male and female. Twenty 5-min windows were used to analyze the minute-wise progression of interactions from 5 min in a supine position (baseline, BL) until 18 min of the orthostatic phase (OP) without including pre-syncopal phases. Gender differences in controls were present in cardiorespiratory interactions during OP without compromised autonomic regulation. However in patients, analysis by ePDC revealed considerable dynamic alterations within cardiovascular and cardiorespiratory interactions over the temporal course during the HUT test. Considering the young female patients with OI, the information flow from heart rate to systolic blood pressure (mechanical modulation) was already increased before the tilt-up, the information flow from systolic blood pressure to heart rate (neural baroreflex) increased during OP, while the information flow from respiration to heart rate (respiratory sinus arrhythmia) decreased during the complete HUT test. Findings revealed impaired cardiovascular interactions in patients with orthostatic intolerance and confirmed the usefulness of ePDC for causality analysis.

Multiscale information storage of linear long-range correlated stochastic processes

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then its complexity is evaluated in terms of conditional entropy. Within this framework, our approach makes use of linear fractionally integrated autoregressive (ARFI) models to derive analytical expressions for the information storage computed at multiple timescales. Specifically, we exploit state space models to provide the representation of lowpass filtered and downsampled ARFI processes, from which information storage is computed at any given timescale relating the process variance to the prediction error variance. This enhances the practical usability of multiscale information storage, as it enables a computationally reliable quantification of a complexity measure which incorporates the effects of LRC together with that of short-term dynamics. The proposed measure is first assessed in simulated ARFI processes reproducing different types of autoregressive dynamics and different degrees of LRC, studying both the theoretical values and the finite sample performance. We find that LRC alter substantially the complexity of ARFI processes even at short timescales, and that reliable estimation of complexity can be achieved at longer timescales only when LRC are properly modeled. Then, we assess multiscale information storage in physiological time series measured in humans during resting state and postural stress, revealing unprecedented responses to stress of the complexity of heart period and systolic arterial pressure variability, which are related to the different role played by LRC in the two conditions.

Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof

Fitness level, fatigue and adaptation are important factors for determining the optimal training schedule and predicting future performance. We think that adding analysis of the mutual relationships between cardiac and respiratory activity enables better athlete profiling and feedback for improving training. Therefore, the main objectives were (1) to apply several methods for temporal causality analysis to cardiorespiratory data; (2) to establish causal links between the signals; and (3) to determine how parameterized connections differed across various subgroups. One hundred elite athletes (31 female) and a control group of 20 healthy students (6 female) took part in the study. All were asked to follow a protocol comprising two 5-min sessions of free breathing - once supine, once standing. The data were collected using Pneumonitor 2. Respiratory-related curves were obtained through impedance pneumography, along with a single-lead ECG. Several signals (e.g., tidal volume, instantaneous respiratory rate, and instantaneous heart rate) were derived and stored as: (1) raw data down-sampled to 25Hz; (2) further down-sampled to 2.5Hz; and (3) beat-by-beat sequences. Granger causality frameworks (pairwise-conditional, spectral or extended), along with Time Series Models with Independent Noise (TiMINo), were studied. The connections enabling the best distinctions were found using recursive feature elimination with a random forest kernel. Temporal causal links are the most evident between tidal volume and instantaneous heart rate signals. Predictions of the “effect” variable were improved by adding preceding “cause” samples, by medians of 20.3% for supine and 14.2% for standing body positions. Parameterized causal link structures and directions distinguish athletes from non-athletes with 83.3% accuracy on average. They may also be used to supplement standard analysis and enable classification into groups exhibiting different static and dynamic components during performance. Physiological markers of training may be extended to include cardiorespiratory data, and causality analysis may improve the resolution of training profiling and the precision of outcome prediction.

Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes

Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well-established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean heart rate, the root-mean-square difference of successive RR intervals (RMSSD), its natural logarithm (lnRMSSD), the mean respiratory rate (RR), the breathing activity coefficients, and the resulting breathing regularity (BR) were estimated. Several causal discovery frameworks were applied, comprising Generalized Correlations (GC), Causal Additive Modeling (CAM), Fast Greedy Equivalence Search (FGES), Greedy Fast Causal Inference (GFCI), and two score-based Bayesian network learning algorithms: Hill-Climbing (HC) and Tabu Search. The discovery of cardiorespiratory paths appears ambiguous. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes average heart activity, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training. In the next step, we plan to expand the study using time-related causality analyses.

Comparison between probabilistic and Wiener-Granger causality in assessing modifications of the cardiac baroreflex control with age

BACKGROUND

Probabilistic causality (PC) is a framework for checking that the occurrence of a cause raises the probability of the effect by comparing the probability of the effect conditioned and unconditioned to the cause. Even though it is less frequently utilized with respect to the more traditional model-based Wiener-Granger causality (WGC) that is based on the predictability improvement of an effect resulting from the inclusion of the presumed cause in the multivariate linear regression model, PC has the advantage of being model-free.

OBJECTIVE

The aim of the study is to apply the PC framework to assess the evolution of cardiac baroreflex control with age from spontaneous fluctuations of heart period (HP) and systolic arterial pressure (SAP) and to compare it to the more common WGC approach.

APPROACH

We studied 100 healthy humans (54 males, age: 21-70 years, 20 subjects for each 10 years bin). HP and SAP were extracted on a beat-to-beat basis from 5 min recordings of electrocardiogram and plethysmographic arterial pressure at rest in supine position (REST) and during active standing (STAND) under spontaneous breathing. The WGC ratio (WGCR) was computed as the log ratio of the prediction error variance of the autoregressive model on HP to that on HP with exogenous SAP. The PC ratio (PCR) was computed as the probability of observing an HP ramp given an associated parallel SAP variation divided by the probability of observing an HP ramp.

MAIN RESULTS

The WGCR and PCR suggested the gradual impairment of cardiac baroreflex with age, especially during STAND. Moreover, they were significantly associated both at REST and during STAND but the degree of the PCR-WGCR association was weak.

SIGNIFICANCE

PC can be effectively exploited to assess modification of the cardiovascular control during senescence even though a limited agreement was observed with WGC.

System dynamics of active and passive postural changes: Insights from principal dynamic modes analysis of baroreflex loop

The baroreflex being a key modulator of cardiovascular control ensures adequate blood pressure regulation under orthostatic stress which otherwise may cause severe hypotension. Contrary to conventional baroreflex sensitivity indices derived across a-priori traditional frequency bands, the present study is aimed at proposing new indices for the assessment of baroreflex drive which follows active (supine to stand-up) and passive (supine to head-up tilt) postural changes. To achieve this, a novel system identification approach of principal dynamic modes (PDM) was utilized to extract data-adaptive frequency components of closed-loop interactions between beat-to-beat interval and systolic blood pressure recorded from 10 healthy humans. We observed that the gain of low-pass global PDM of cardiac arm (:feedback reflex loop, mediated by pressure sensors to adjust heart rate in response to arterial blood pressure), and 0.2 Hz global PDM of mechanical arm (:feed-forward pathways, originating changes in arterial blood pressure in response to heart rate variations) may function as potential markers to distinguish active and passive orthostatic tests in healthy subjects.

Comparison of Autonomic Control of Blood Pressure During Standing and Artificial Gravity Induced via Short-Arm Human Centrifuge

Autonomic control of blood pressure is essential toward maintenance of cerebral perfusion during standing, failure of which could lead to fainting. Long-term exposure to microgravity deteriorates autonomic control of blood pressure. Consequently, astronauts experience orthostatic intolerance on their return to gravitational environment. Ground-based studies suggest sporadic training in artificial hypergravity can mitigate spaceflight deconditioning. In this regard, short-arm human centrifuge (SAHC), capable of creating artificial hypergravity of different g-loads, provides an auspicious training tool. Here, we compare autonomic control of blood pressure during centrifugation creating 1-g and 2-g at feet with standing in natural gravity. Continuous blood pressure was acquired simultaneously from 13 healthy participants during supine baseline, standing, supine recovery, centrifugation of 1-g, and 2-g, from which heart rate (RR) and systolic blood pressure (SBP) were derived. The autonomic blood pressure regulation was assessed via spectral analysis of RR and SBP, spontaneous baroreflex sensitivity, and non-linear heart rate and blood pressure causality (RR↔SBP). While majority of these blood pressure regulatory indices were significantly different (p < 0.05) during standing and 2-g centrifugation compared to baseline, no change (p > 0.05) was observed in the same indices during 2-g centrifugation compared to standing. The findings of the study highlight the capability of artificial gravity (2-g at feet) created via SAHC toward evoking blood pressure regulatory controls analogous to standing, therefore, a potential utility toward mitigating deleterious effects of microgravity on cardiovascular performance and minimizing post-flight orthostatic intolerance in astronauts.

Towards understanding the complexity of cardiovascular oscillations: Insights from information theory

Cardiovascular complexity is a feature of healthy physiological regulation, which stems from the simultaneous activity of several cardiovascular reflexes and other non-reflex physiological mechanisms. It is manifested in the rich dynamics characterizing the spontaneous heart rate and blood pressure variability (HRV and BPV). The present study faces the challenge of disclosing the origin of short-term HRV and BPV from the statistical perspective offered by information theory. To dissect the physiological mechanisms giving rise to cardiovascular complexity in different conditions, measures of predictive information, information storage, information transfer and information modification were applied to the beat-to-beat variability of heart period (HP), systolic arterial pressure (SAP) and respiratory volume signal recorded non-invasively in 61 healthy young subjects at supine rest and during head-up tilt (HUT) and mental arithmetics (MA). Information decomposition enabled to assess simultaneously several expected and newly inferred physiological phenomena, including: (i) the decreased complexity of HP during HUT and the increased complexity of SAP during MA; (ii) the suppressed cardiorespiratory information transfer, related to weakened respiratory sinus arrhythmia, under both challenges; (iii) the altered balance of the information transferred along the two arms of the cardiovascular loop during HUT, with larger baroreflex involvement and smaller feedforward mechanical effects; and (iv) an increased importance of direct respiratory effects on SAP during HUT, and on both HP and SAP during MA. We demonstrate that a decomposition of the information contained in cardiovascular oscillations can reveal subtle changes in system dynamics and improve our understanding of the complexity changes during physiological challenges.

IDENTIFYING DEEP BREATH EFFECT ON CARDIOVASCULAR SIGNALS USING CONDITIONAL ENTROPY: AN INFORMATION DOMAIN APPROACH

This quantitative study identifies the coupling changes occurring among cardiac (RR), vascular (SBP) and respiratory (RESP) signals during deep breathing. The deep breathing measures the dysfunction of the parasympathetic autonomic nervous system. The traditional methods based on cross-correlation and coherence analysis lack to measure nonlinear structures and unpredictability of physiological subsystems. Therefore, information domain coupling method based on conditional entropy is proposed to detect the coupling changes. Thirty healthy volunteers were examined for 5[Formula: see text]min at normal breathing and 5[Formula: see text]min during deep breathing (6[Formula: see text]cycles/min). The reduction in respiration rate detects a significant increase in information flow from RESP to RR, RESP to SBP and SBP to RR. The increased interaction from RESP to RR and RESP to SBP at reduced respiration rate indicates the enhancement of respiratory sinus arrhythmia that results in the activation of the parasympathetic nervous system. Also, the balanced cardiovascular interaction observed on normal breathing from RR to SBP disappears, but interaction occurring in baroreflex direction (SBP to RR) increases that helps in the reduction of blood pressure during deep breathing. This detected direction of information flow helps in identifying the coupling changes occurring during parasympathetic nerve activity.

Role of respiration in the cardiovascular response to orthostatic and mental stress

The objective of this study was to determine the response of heart rate and blood pressure variability (respiratory sinus arrhythmia, baroreflex sensitivity) to orthostatic and mental stress, focusing on causality and the mediating effect of respiration. Seventy-seven healthy young volunteers (46 women, 31 men) aged 18.4 ± 2.7 yr underwent an experimental protocol comprising supine rest, 45° head-up tilt, recovery, and a mental arithmetic task. Heart rate variability and blood pressure variability were analyzed in the time and frequency domain and modeled as a multivariate autoregressive process where the respiratory volume signal acted as an external driver. During head-up tilt, tidal volume increased while respiratory rate decreased. During mental stress, breathing rate increased and tidal volume was elevated slightly. Respiratory sinus arrhythmia decreased during both interventions. Baroreflex function was preserved during orthostasis but was decreased during mental stress. While sex differences were not observed during baseline conditions, cardiovascular response to orthostatic stress and respiratory response to mental stress was more prominent in men compared with women. The respiratory response to the mental arithmetic tasks was more prominent in men despite a significantly higher subjectively perceived stress level in women. In conclusion, respiration shows a distinct response to orthostatic versus mental stress, mediating cardiovascular variability; it needs to be considered for correct interpretation of heart rate and blood pressure phenomena.

An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis

Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool.

Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS.

Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations.

Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

Non-linear Heart Rate and Blood Pressure Interaction in Response to Lower-Body Negative Pressure

Early detection of hemorrhage remains an open problem. In this regard, blood pressure has been an ineffective measure of blood loss due to numerous compensatory mechanisms sustaining arterial blood pressure homeostasis. Here, we investigate the feasibility of causality detection in the heart rate and blood pressure interaction, a closed-loop control system, for early detection of hemorrhage. The hemorrhage was simulated via graded lower-body negative pressure (LBNP) from 0 to -40 mmHg. The research hypothesis was that a significant elevation of causal control in the direction of blood pressure to heart rate (i.e., baroreflex response) is an early indicator of central hypovolemia. Five minutes of continuous blood pressure and electrocardiogram (ECG) signals were acquired simultaneously from young, healthy participants (27 ± 1 years, N = 27) during each LBNP stage, from which heart rate (represented by RR interval), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were derived. The heart rate and blood pressure causal interaction (RR↔SBP and RR↔MAP) was studied during the last 3 min of each LBNP stage. At supine rest, the non-baroreflex arm (RR→SBP and RR→MAP) showed a significantly (p < 0.001) higher causal drive toward blood pressure regulation compared to the baroreflex arm (SBP→RR and MAP→RR). In response to moderate category hemorrhage (-30 mmHg LBNP), no change was observed in the traditional marker of blood loss i.e., pulse pressure (p = 0.10) along with the RR→SBP (p = 0.76), RR→MAP (p = 0.60), and SBP→RR (p = 0.07) causality compared to the resting stage. Contrarily, a significant elevation in the MAP→RR (p = 0.004) causality was observed. In accordance with our hypothesis, the outcomes of the research underscored the potential of compensatory baroreflex arm (MAP→RR) of the heart rate and blood pressure interaction toward differentiating a simulated moderate category hemorrhage from the resting stage. Therefore, monitoring baroreflex causality can have a clinical utility in making triage decisions to impede hemorrhage progression.

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

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.