Causal Cross-Spectral Analysis of Heart Rate and Blood Pressure Variability for describing the Impairment of the Cardiovascular Control in Neurally Mediated Syncope

Poor vitamin D status was associated with increased appendicular fat deposition in US Adults: Data from 2011-2018 National Health and Nutrition Examination Survey

The aim of the study was to explore the relationship between serum 25-hydroxyvitamin D [25(OH)D] concentrations and regional body fat deposition in 2011-2018 National Health and Nutrition Examination Survey participants aged 18 to 59 years. We hypothesized that serum 25(OH)D concentrations were negatively associated with total, appendicular, and truncal fat deposition. Serum 25(OH)D concentration was categorized into sufficient (≥75.0 nM), insufficient (50.0-74.9 nM), and deficient (<50.0 nM) groups. Fat mass (FM) was measured by dual-energy X-ray absorptiometry, and FM index (FMI) was calculated by dividing FM (kg) with height2 (m2). Multivariant linear regression and Granger causal analysis were performed to assess the causal relationship between vitamin D status and regional FMIs. Overall serum 25(OH)D concentrations were negatively associated with total (β = -0.029, standard error [SE] = 0.002), trunk (β = -0.015, SE = 0.001), arms (β = -0.004, SE = 3.09 × 10-4), and legs (β = -0.010, SE = 0.001) FMIs in all participants (P < .001, respectively); however, after stratified by vitamin D status and BMI, the negative associations were only observed in individuals with vitamin D deficiency and obesity. The causal analysis indicated that serum 25(OH)D concentrations may causally reduce the arms (F = 4.917, probability [P] = 0.007), legs (F = 5.783, P = 0.003), and total (F = 3.202, P = 0.041) FMIs except for trunk FMI but not vice versa. In conclusion, poor vitamin D status was associated with increased total and appendicular body fat deposition in US adults, particularly in participants with obesity.

Spectral decomposition of cerebrovascular and cardiovascular interactions in patients prone to postural syncope and healthy controls

We present a framework for the linear parametric analysis of pairwise interactions in bivariate time series in the time and frequency domains, which allows the evaluation of total, causal and instantaneous interactions and connects time- and frequency-domain measures. The framework is applied to physiological time series to investigate the cerebrovascular regulation from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (MAP), and the cardiovascular regulation from the variability of heart period (HP) and systolic arterial pressure (SAP). We analyze time series acquired at rest and during the early and late phase of head-up tilt in subjects developing orthostatic syncope in response to prolonged postural stress, and in healthy controls. The spectral measures of total, causal and instantaneous coupling between HP and SAP, and between MAP and CBFV, are averaged in the low-frequency band of the spectrum to focus on specific rhythms, and over all frequencies to get time-domain measures. The analysis of cardiovascular interactions indicates that postural stress induces baroreflex involvement, and its prolongation induces baroreflex dysregulation in syncope subjects. The analysis of cerebrovascular interactions indicates that the postural stress enhances the total coupling between MAP and CBFV, and challenges cerebral autoregulation in syncope subjects, while the strong sympathetic activation elicited by prolonged postural stress in healthy controls may determine an increased coupling from CBFV to MAP during late tilt. These results document that the combination of time-domain and spectral measures allows us to obtain an integrated view of cardiovascular and cerebrovascular regulation in healthy and diseased subjects.

Directional couplings between the respiration and parasympathetic control of the heart rate during sleep and wakefulness in healthy subjects at different ages

Cardiorespiratory interactions are important, both for understanding the fundamental processes of functioning of the human body and for development of methods for diagnostics of various pathologies. The properties of cardiorespiratory interaction are determined by the processes of autonomic control of blood circulation, which are modulated by the higher nervous activity. We study the directional couplings between the respiration and the process of parasympathetic control of the heart rate in the awake state and different stages of sleep in 96 healthy subjects from different age groups. The detection of directional couplings is carried out using the method of phase dynamics modeling applied to experimental RR-intervals and the signal of respiration. We reveal the presence of bidirectional couplings between the studied processes in all age groups. Our results show that the coupling from respiration to the process of parasympathetic control of the heart rate is stronger than the coupling in the opposite direction. The difference in the strength of bidirectional couplings between the considered processes is most pronounced in deep sleep.

Efficient syncope prediction from resting state clinical data using wavelet bispectrum and multilayer perceptron neural network

Neurally mediated syncope (NMS) is the most common type of syncope, and head up tilt test (HUTT) is, so far, the most appropriate tool to identify NMS. In this work, an effort to predict the NMS before performing the HUTT is attempted. To achieve this, the heart rate variability (HRV) at rest and during the first minutes of tilting position during HUTT was analyzed using both time and frequency domains. Various features from HRV regularity and complexity, along with wavelet higher-order spectrum (WHOS) analysis in low-frequency (LF) and high-frequency (HF) bands were examined. The experimental results from 26 patients with history of NMS have shown that at rest, a time domain entropy measure and WHOS-based features in LF band exhibit significant differences between positive and negative HUTT as well as among 10 healthy subjects and NMS patients. The best performance of multilayer perceptron neural network (MPNN) was achieved by using an input vector consisted of WHOS-based HRV features in the LF zone and systolic blood pressure from the resting period, yielding an accuracy of 89.7%, assessed by 5-fold cross-validation. The promising results presented here pave the way for an early prediction of the HUTT outcome from resting state, contributing to the identification of patients at higher risk NMS. The HRV analysis along with systolic blood pressure at rest predict NMS using a multilayer perceptron neural network.

A Novel Framework for Estimating Time-Varying Multivariate Autoregressive Models and Application to Cardiovascular Responses to Acute Exercise

OBJECTIVE

We present a novel modeling framework for identifying time-varying (TV) couplings between time-series of biomedical relevance.

METHODS

The proposed methodology is based on multivariate autoregressive (MVAR) models, which have been extensively used to study couplings between biosignals. Contrary to the standard estimation methods that assume time-invariant relationships, we propose a modified recursive Kalman filter (KF) to track changes in the model parameters. We perform model order selection and hyperparameter optimization simultaneously using Genetic Algorithms, greatly improving accuracy and computation time. In addition, we address the effect of residual heteroscedasticity, possibly associated with event-related changes or phase transitions during a given experimental protocol, on the TV-MVAR coupling measures by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to fit the TV-MVAR residuals.

RESULTS

Using simulated data, we show that the proposed framework yields more accurate parameter estimates compared to the conventional KF, particularly when the true system parameters exhibit different rate of variations over time. Furthermore, by accounting for heteroskedasticity, we obtain more accurate estimates of the strength and directionality of the underlying couplings. We also use our approach to investigate TV hemodynamic interactions during exercise in young and old healthy adults, as well as individuals with chronic stroke. We extract TV coupling patterns that reflect well known exercise-induced effects on the underlying regulatory mechanisms with excellent time resolution.

CONCLUSION AND SIGNIFICANCE

The proposed modeling framework can be used to efficiently quantify TV couplings between biosignals. It is fully automated and does not require prior knowledge of the system TV characteristics.

Revisiting the Sequence Method for Baroreflex Analysis

The sequence method is an important approach to assess the baroreflex function, mainly because it is based on the spontaneous fluctuations of beat-by-beat arterial pressure (for example, systolic arterial pressure or SAP) and pulse interval (PI). However, some studies revealed that the baroreflex effectiveness index (BEI), calculated through the sequence method, shows an intriguing oscillatory pattern as function of the delay between SAP and PI. It has been hypothesized that this pattern is related to the respiratory influence on SAP and/or PI variability, limiting the SAP ramps to 3 or 4 beats of length. In this study, this hypothesis was tested by assessing the sequence method using raw (original) and filtered series. Results were contrasted to the well-established transfer function, estimated between SAP and PI. Continuous arterial pressure recordings were obtained from healthy rats (N = 61) and beat-by-beat series of SAP and PI were generated. Low-pass (LP) and high-pass (HP) filtered series of SAP and PI were created by filtering the original series with a cutoff frequency of 0.8 Hz. Original series were analyzed by either the sequence method or cross-spectral analysis (transfer function) at low- (LF) and high- (HF) frequency bands, while filtered series were evaluated only by the sequence method. Baroreflex sensitivity (BRS) and BEI of original series, calculated by sequence method, was highly (85–90%) determined by HP series, with no significant association between original and LP series. A high correlation (>0.7) was found between the BRS estimated from original series (sequence method) and HF band (transfer function), as well as for LP series (sequence method) and LF band (transfer function). These findings confirmed the hypothesis that the sequence method quantifies only the high-frequency components of the baroreflex, neglecting the low-frequency influences, such as the Mayer waves. Therefore, we propose using both the original and LP filtered time series for a broader assessment of the baroreflex function using the sequence method.

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.

Robust Nonlinear Causality Analysis of Nonstationary Multivariate Physiological Time Series

GOAL

An important research area in biomedical signal processing is that of quantifying the relationship between simultaneously observed time series and to reveal interactions between the signals. Since biomedical signals are potentially nonstationary and the measurements may contain outliers and artifacts, we introduce a robust time-varying generalized partial directed coherence (rTV-gPDC) function.

METHODS

The proposed method, which is based on a robust estimator of the time-varying autoregressive (TVAR) parameters, is capable of revealing directed interactions between signals. By definition, the rTV-gPDC only displays the linear relationships between the signals. We therefore suggest to approximate the residuals of the TVAR process, which potentially carry information about the nonlinear causality by a piece-wise linear time-varying moving-average model.

RESULTS

The performance of the proposed method is assessed via extensive simulations. To illustrate the method's applicability to real-world problems, it is applied to a neurophysiological study that involves intracranial pressure, arterial blood pressure, and brain tissue oxygenation level (PtiO2) measurements.

CONCLUSION AND SIGNIFICANCE

The rTV-gPDC reveals causal patterns that are in accordance with expected cardiosudoral meachanisms and potentially provides new insights regarding traumatic brain injuries. The rTV-gPDC is not restricted to the above problem but can be useful in revealing interactions in a broad range of applications.

Measuring postural-related changes of spontaneous baroreflex sensitivity after repeated long-duration diving: frequency domain approaches

Sustained water immersion is thought to modulate orthostatic tolerance to an extent dependent on the duration and repetition over consecutive days of the diving sessions. We tested this hypothesis investigating in ten healthy subjects the potential changes in the cardiovascular response to head-up tilt induced by single and multiple resting air dives. Parametric cross-spectral analysis of spontaneous RR interval and systolic arterial pressure variability was performed in three experimental sessions: before diving (BD), after single 6-hour dive (ASD), and after multiple 6-hour dives (AMD, 5 consecutive days with 18-hour surface interval). From this analysis, baroreflex sensitivity (BRS) was computed as spectral power ratio (αBRS), non-causal transfer function gain (tfBRS) and causal transfer function gain (γBRS) evaluated at low frequency (0.04-0.14Hz) in the supine position (su) as well as in the standing upright position in the early tilt (et) and late tilt (lt) epochs. We found that, while αBRS decreased significantly in et and lt compared to su during all sessions, tfBRS and γBRS decreased during ASD and AMD but not during BD; moreover γBRS evidenced a progressive decrease from BD to ASD and to AMD in both et and lt epochs. These results indicate the necessity of following a causal approach for the estimation of BRS in the frequency domain, and suggest a progressive impairment of the baroreflex response to postural stress after single and multiple dives, which may reflect symptoms of increasing orthostatic intolerance.

Mechanisms of causal interaction between short-term RR interval and systolic arterial pressure oscillations during orthostatic challenge

The transition from the supine to the upright position requires a reorganization of the mechanisms of cardiovascular control that, if not properly accomplished, may lead to neurally mediated syncope. We investigated how the patterns of causality between systolic arterial pressure (SAP) and cardiac RR interval were modified by prolonged head-up tilt using a novel nonlinear approach based on corrected conditional entropy (CCE) compared with the standard approach exploiting the cross-correlation function (CCF). Measures of coupling strength and delay of the causal interactions from SAP to RR and from RR to SAP were obtained in 10 patients with recurrent, neurally mediated syncope (RNMS) and 10 healthy control (CO) subjects in the resting supine position (su) and after head-up tilting during early (et; ~2 min) and late (lt; ~15 min or before presyncope) epochs of upright posture. Main results were that 1) the coupling strength from SAP to RR increased significantly from su to et in both groups; by contrast, upon lt, the coupling strength was kept high in CO subjects and dropped to low values in RNMS patients; 2) in RNMS patients, the delay from SAP to RR was higher than in healthy controls and increased moving from et to lt. Although these trends were evident using the CCE approach, statistical significance was not attained using the CCF approach. These results indicate the necessity of dissecting causality between RR and SAP to properly assess directional mechanisms from the closed-loop cardiovascular regulation and suggest a weakened and slowed baroreflex as a major mechanism involved in the cardiovascular impairment associated to neurally mediated syncope.

Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings

The physiological mechanisms related to cardio-vascular (CV), cardio-pulmonary (CP), and vasculo-pulmonary (VP) regulation may be probed through multivariate time series analysis tools. This study applied an information domain approach for the evaluation of non-linear causality to the beat-to-beat variability series of heart period (t), systolic arterial pressure (s), and respiration (r) measured during tilt testing and paced breathing (PB) protocols. The approach quantifies the causal coupling from the series i to the series j (C_ij) as the amount of information flowing from i to j. A measure of directionality is also obtained as the difference between two reciprocal causal couplings (D_i,j = C_ij − C_ji). Significant causal coupling and directionality were detected respectively when the median of C_ij over subjects was positive (C_ij > 0), and when D_i,j was statistically different from zero (D_i,j > 0 or D_i,j < 0). The method was applied on t, s, and r series measured in 15 healthy subjects (22–32 years, 8 males) in the supine (su) and upright (up) positions, and in further 15 subjects (21–29 years, 7 males) during spontaneous (sp) and paced (pa) breathing. In the control condition (su, sp), a significant causal coupling was observed for C_rs, C_rt, C_st, and C_ts, and significant directionality was present only from r to t (D_r,t > 0). During head-up tilt (up, sp), C_rs was preserved, C_rt decreased to zero median, and C_st and C_ts increased significantly; directionality vanished between r and t (D_r,t = 0) and raised from s to t (D_s,t > 0). During PB (su, pa), C_rs increased significantly, C_rt and C_ts were preserved, and C_st decreased to zero median; directionality was preserved from r to t (D_r,t > 0), and raised from r to s (D_r,s > 0). These results suggest that the approach may reflect modifications of CV, CP, and VP mechanisms consequent to altered physiological conditions, such as the baroreflex engagement and the dampening of respiratory sinus arrhythmia induced by tilt, or the respiratory driving on arterial pressure induced by PB. Thus, it could be suggested as a tool for the non-invasive monitoring of CV and cardiorespiratory control systems in normal and impaired conditions.

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

Assessing causality in normal and impaired short-term cardiovascular regulation via nonlinear prediction methods

We investigated the ability of mutual nonlinear prediction methods to assess causal interactions in short-term cardiovascular variability during normal and impaired conditions. Directional interactions between heart period (RR interval of the ECG) and systolic arterial pressure (SAP) short-term variability series were quantified as the cross-predictability (CP) of one series given the other, and as the predictability improvement (PI) yielded by the inclusion of samples of one series into the prediction of the other series. Nonlinear prediction was performed through global approximation (GA), approximation with locally constant models (LA0) and approximation with locally linear models (LA1) of the nonlinear function linking the samples of the two series, on patients with neurally mediated syncope and control subjects. Causality measures were evaluated in the two directions (from SAP to RR and from RR to SAP) in the supine (SU) position, in the upright position after head-up tilt (early tilt, ET) and after prolonged upright posture (late tilt, LT). While the trends for the GA, LA0 and LA1 methods were substantially superimposable, PI elicited better than CP the prevalence of causal coupling from RR to SAP during SU. Both CP and PI noted a marked decrease in coupling in both causal directions in syncope subjects during LT, documenting the impairment of cardiovascular regulation in the minutes just preceding syncope.

Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of the PI was assessed using a surrogate data technique. The proposed method was tested with simulation examples involving short realizations of linear stochastic processes and nonlinear deterministic signals in which either unidirectional or bidirectional coupling and varying strengths of interactions were imposed. It was found that the OPS-based NARX model was accurate and sensitive in detecting imposed Granger causality conditions. In addition, the OPS-based NARX model was more accurate than the least squares method. Application to the systolic blood pressure and heart rate variability signals demonstrated the feasibility of the method. In particular, we found a bilateral causal relationship between the two signals as evidenced by the significant reduction in the PI values with the NARX model prediction compared to the NAR model prediction, which was also confirmed by the surrogate data analysis. Furthermore, we found significant reduction in the complexity of the dynamics of the two causal pathways of the two signals as the body position was changed from the supine to upright. The proposed is a general method, thus, it can be applied to a wide variety of physiological signals to better understand causality and coupling that may be different between normal and diseased conditions.

Characterization of interdependency between intracranial pressure and heart variability signals: a causal spectral measure and a generalized synchronization measure

Causal coherence and generalized synchronization (GS) index were extracted from beat-to-beat mean intracranial pressure (ICP) and intervals between consecutive normal sinus heart beats (RR interval) that were recorded from 12 patients undergoing normal pressure hydrocephalus diagnosis. Data were organized into two groups including an ICP B-Wave group and a baseline control group. Maximal classic coherence (CC) between ICP and RR interval within [0.04, 0.15] Hz was found to be significantly greater than zero for both B-Wave and control groups with B-Wave CC greater than that of the baseline group. Causal coherence analysis further revealed that feedforward coherence due to RR interval's effect on ICP always exists for both B-Wave and baseline ICP state and no significant difference exists between two groups. On the other hand, feedback coherence from ICP to RR interval was enhanced during the occurrence of B-Wave. This finding regarding the enhanced directional, from ICP to RR interval, coupling between ICP and RR interval was also confirmed by a modified GS measure.

Integrative analysis of intracranial pressure and R-R interval signals: a study of ICP B-wave using causal coherence

Causal coherence analysis was applied to beat-to-beat mean intracranial pressure (ICP) and RR interval signals that were recorded from twelve normal pressure hydrocephalus patients. Data were organized into two groups including an ICP B-Wave group and a control baseline group. Maximal classic coherence between ICP and RR interval within [0.04-0.15] Hz was found to be significantly greater than zero for both B-wave and control groups. Causal coherence analysis further revealed that feedforward coherence due to RR interval's effect on ICP always exists for both B-wave and baseline ICP state while feedback coherence from ICP to RR interval was enhanced during the occurrence of B-wave.