Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time-frequency analysis

Restoring bottom-up communication in brain-heart interplay after trauma-focused psychotherapy in breast cancer patients with post-traumatic stress disorder

BACKGROUND

The psychological impact of breast cancer (BC) is substantial, with a significant number of patients (up to 32 %) experiencing post-traumatic stress disorder (PTSD). Exploring the emotional aspects of PTSD through the functional brain-heart interplay (BHI) offers valuable insights into the condition. BHI examines the functional interactions between cortical and sympathovagal dynamics. This study aims to investigate changes in functional directional BHI after trauma-focused (TF) psychotherapy, specifically Eye Movement Desensitization and Reprocessing (EMDR), in comparison to treatment as usual (TAU) among BC patients with PTSD. To our knowledge, this study represents the first examination of such changes.

METHODS

We enrolled thirty BC patients who met the criteria for a PTSD diagnosis, with fourteen receiving EMDR and fifteen receiving TAU over a two- to three-month period. We analyzed changes in the emotional response during a script-driven imagery setting. Quantification of the functional interplay between EEG and sympathovagal dynamics was achieved using the synthetic data generation model (SDG) on electroencephalographic (EEG) and heartbeat series. Our focus was on the difference in the BHI index extracted at baseline and post-treatment.

RESULTS

We found statistically significant higher coupling in the heart-to-brain direction in patients treated with EMDR compared to controls. This suggests that the flow of information from the autonomic nervous system to the central nervous system is restored following EMDR-induced recovery from PTSD. Furthermore, we observed a significant correlation between improvements in PTSD symptoms and an increase in functional BHI after EMDR treatment.

CONCLUSIONS

TF psychotherapy, particularly EMDR, appears to facilitate the restoration of the bottom-up flow of interoceptive information, which is dysfunctional in patients with PTSD. The application of BHI analysis to the study of PTSD not only aids in identifying biomarkers of the disorder but also enhances our understanding of the changes brought about by TF treatments.

Synchronization of the Processes of Autonomic Control of Blood Circulation in Humans Is Different in the Awake State and in Sleep Stages

The influence of higher nervous activity on the processes of autonomic control of the cardiovascular system and baroreflex regulation is of considerable interest, both for understanding the fundamental laws of the functioning of the human body and for developing methods for diagnostics and treatment of pathologies. The complexity of the analyzed systems limits the possibilities of research in this area and requires the development of new tools. Earlier we propose a method for studying the collective dynamics of the processes of autonomic control of blood circulation in the awake state and in different stages of sleep. The method is based on estimating a quantitative measure representing the total percentage of phase synchronization between the low-frequency oscillations in heart rate and blood pressure. Analysis of electrocardiogram and invasive blood pressure signals in apnea patients in the awake state and in different sleep stages showed a high sensitivity of the proposed measure. It is shown that in slow-wave sleep the degree of synchronization of the studied rhythms is higher than in the awake state and lower than in sleep with rapid eye movement. The results reflect the modulation of the processes of autonomic control of blood circulation by higher nervous activity and can be used for the quantitative assessment of this modulation.

Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure

The question of how much information the photoplethysmogram (PPG) signal contains on the autonomic regulation of blood pressure (BP) remains unsolved. This study aims to compare the low-frequency (LF) and high-frequency components of PPG and BP and assess their correlation with oscillations in interbeat (RR) intervals at similar frequencies. The PPG signal from the distal phalanx of the right index finger recorded using a reflective PPG sensor at green light, the BP signal from the left hand recorded using a Finometer, and RR intervals were analyzed. These signals were simultaneously recorded within 15 min in a supine resting condition in 17 healthy subjects (12 males and 5 females) aged 33 ± 9 years (mean ± SD). The study revealed the high coherence of LF components of PPG and BP with the LF component of RR intervals. The high-frequency components of these signals had low coherence. The analysis of the signal instantaneous phases revealed the presence of high-phase coherence between the LF components of PPG and BP. It is shown that the LF component of PPG is determined not only by local myogenic activity but also reflects the processes of autonomic control of BP.

Cerebrovascular Impedance During Hemodynamic Change in Rabbits: A Pilot Study

INTRODUCTION

Cerebrovascular impedance describes the relationship between pulsatile changes in arterial blood pressure (ABP) and cerebral blood flow (CBF). It is commonly defined by modulus and phase shift derived from Fourier spectra of ABP and CBF velocity (CBFV) signals under mostly steady-state conditions. The aim of this work was to assess heartbeat-to-heartbeat cerebrovascular impedance at heart rate frequency during controlled changes in mean ABP and intracranial pressure (ICP).

MATERIAL AND METHODS

Recordings of ABP in the femoral artery, transcranial Doppler CBFV in the basilar artery, and subarachnoid ICP were obtained from anesthetized rabbits with induced arterial hypotension (n = 8 rabbits), arterial hypertension (n = 5), or intracranial hypertension (n = 7). Modulus of cerebrovascular impedance (|Z|) was estimated from amplitudes of ABP and CBFV. Phase shift of cerebrovascular impedance (PS) was estimated from time-frequency (TF) representations of phase shift between ABP and CBFV overlaid with a time-variant mask based on the fundamental frequency of ABP.

RESULTS

Both |Z| and PS increased with increasing mean ABP. |Z| decreased with increasing mean ICP, but no change was observed in PS.

CONCLUSIONS

The combined beat-to-beat and TF approach allows for the estimation of cerebrovascular impedance during transient hemodynamic changes. |Z| and PS follow the pattern of changes in CPP.

Two-Tiered Response of Cardiorespiratory-Cerebrovascular Network to Orthostatic Challenge

Dynamic interdependencies within and between physiological systems and subsystems are key for homeostatic mechanisms to establish an optimal state of the organism. These interactions mediate regulatory responses elicited by various perturbations, such as the high-pressure baroreflex and cerebral autoregulation, alleviating the impact of orthostatic stress on cerebral hemodynamics and oxygenation. The aim of this study was to evaluate the responsiveness of the cardiorespiratory-cerebrovascular networks by capturing linear and nonlinear interdependencies to postural changes. Ten young healthy adults participated in our study. Non-invasive measurements of arterial blood pressure (from that cardiac cycle durations were derived), breath-to-breath interval, cerebral blood flow velocity (BFV, recorded by transcranial Doppler sonography), and cerebral hemodynamics (HbT, total hemoglobin content monitored by near-infrared spectroscopy) were performed for 30-min in resting state, followed by a 1-min stand-up and a 1-min sit-down period. During preprocessing, noise was filtered and the contribution of arterial blood pressure was regressed from BFV and HbT signals. Cardiorespiratory-cerebrovascular networks were reconstructed by computing pair-wise Pearson-correlation or mutual information between the resampled signals to capture their linear and/or nonlinear interdependencies, respectively. The interdependencies between cardiac, respiratory, and cerebrovascular dynamics showed a marked weakening after standing up persisting throughout the sit-down period, which could mainly be attributed to strikingly attenuated nonlinear coupling. To summarize, we found that postural changes induced topological changes in the cardiorespiratory-cerebrovascular network. The dissolution of nonlinear networks suggests that the complexity of key homeostatic mechanisms maintaining cerebral hemodynamics and oxygenation is indeed sensitive to physiological perturbations such as orthostatic stress.

Heart Rate Variability Synchronizes When Non-experts Vocalize Together

Singing and chanting are ubiquitous across World cultures. It has been theorized that such practices are an adaptive advantage for humans because they facilitate bonding and cohesion between group members. Investigations into the effects of singing together have so far focused on the physiological effects, such as the synchronization of heart rate variability (HRV), of experienced choir singers. Here, we study whether HRV synchronizes for pairs of non-experts in different vocalizing conditions. Using time-frequency coherence (TFC) analysis, we find that HRV becomes more coupled when people make long (> 10 s) sounds synchronously compared to short sounds (< 1 s) and baseline measurements (p < 0.01). Furthermore, we find that, although most of the effect can be attributed to respiratory sinus arrhythmia, some HRV synchronization persists when the effect of respiration is removed: long notes show higher partial TFC than baseline and breathing (p < 0.05). In addition, we observe that, for most dyads, the frequency of the vocalization onsets matches that of the peaks in the TFC spectra, even though these frequencies are above the typical range of 0.04–0.4 Hz. A clear correspondence between high HRV coupling and the subjective experience of “togetherness" was not found. These results suggest that since autonomic physiological entrainment is observed for non-expert singing, it may be exploited as part of interventions in music therapy or social prescription programs for the general population.

The Effect of Emotional Valence on Ventricular Repolarization Dynamics Is Mediated by Heart Rate Variability: A Study of QT Variability and Music-Induced Emotions

Background

Emotions can affect cardiac activity, but their impact on ventricular repolarization variability, an important parameter providing information about cardiac risk and autonomic nervous system activity, is unknown. The beat-to-beat variability of the QT interval (QTV) from the body surface ECG is a non-invasive marker of repolarization variability, which can be decomposed into QTV related to RR variability (QTVrRRV) and QTV unrelated to RRV (QTVuRRV), with the latter thought to be a marker of intrinsic repolarization variability.

Aim

To determine the effect of emotional valence (pleasant and unpleasant) on repolarization variability in healthy volunteers by means of QTV analysis.

Methods

75 individuals (24.5 ± 3.2 years, 36 females) without a history of cardiovascular disease listened to music-excerpts that were either felt as pleasant (n = 6) or unpleasant (n = 6). Excerpts lasted about 90 s and were presented in a random order along with silent intervals (n = 6). QTV and RRV were derived from the ECG and the time-frequency spectrum of RRV, QTV, QTVuRRV and QTVrRRV as well as time-frequency coherence between QTV and RRV were estimated. Analysis was performed in low-frequency (LF), high frequency (HF) and total spectral bands.

Results

The heart rate-corrected QTV showed a small but significant increase from silence (median 347/interquartile range 31 ms) to listening to music felt as unpleasant (351/30 ms) and pleasant (355/32 ms). The dynamic response of QTV to emotional valence showed a transient phase lasting about 20 s after the onset of each musical excerpt. QTV and RRV were highly correlated in both HF and LF (mean coherence ranging 0.76–0.85). QTV and QTVrRRV decreased during listening to music felt as pleasant and unpleasant with respect to silence and further decreased during listening to music felt as pleasant. QTVuRRV was small and not affected by emotional valence.

Conclusion

Emotional valence, as evoked by music, has a small but significant effect on QTV and QTVrRRV, but not on QTVuRRV. This suggests that the interaction between emotional valence and ventricular repolarization variability is mediated by cycle length dynamics and not due to intrinsic repolarization variability.

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.

Baroreflex Sensitivity Measured by Pulse Photoplethysmography

Novel methods for assessing baroreflex sensitivity (BRS) using only pulse photoplethysmography (PPG) signals are presented. Proposed methods were evaluated with a data set containing electrocardiogram (ECG), blood pressure (BP), and PPG signals from 17 healthy subjects during a tilt table test. The methods are based on a surrogate of α index, which is defined as the power ratio of RR interval variability (RRV) and that of systolic arterial pressure series variability (SAPV). The proposed α index surrogates use pulse-to-pulse interval series variability (PPV) as a surrogate of RRV, and different morphological features of the PPG pulse which have been hypothesized to be related to BP, as series surrogates of SAPV. A time-frequency technique was used to assess BRS, taking into account the non-stationarity of the protocol. This technique identifies two time-varying frequency bands where RRV and SAPV (or their surrogates) are expected to be coupled: the low frequency (LF, inside 0.04-0.15 Hz range), and the high frequency (HF, inside 0.15-0.4 Hz range) bands. Furthermore, time-frequency coherence is used to identify the time intervals when the RRV and SAPV (or their surrogates) are coupled. Conventional α index based on RRV and SAPV was used as Gold Standard. Spearman correlation coefficients between conventional α index and its PPG-based surrogates were computed and the paired Wilcoxon statistical test was applied in order to assess whether the indices can find significant differences (p < 0.05) between different stages of the protocol. The highest correlations with the conventional α index were obtained by the α-index-surrogate based on PPV and pulse up-slope (PUS), with 0.74 for LF band, and 0.81 for HF band. Furthermore, this index found significant differences between rest stages and tilt stage in both LF and HF bands according to the paired Wilcoxon test, as the conventional α index also did. These results suggest that BRS changes induced by the tilt test can be assessed with high correlation by only a PPG signal using PPV as RRV surrogate, and PPG morphological features as SAPV surrogates, being PUS the most convenient SAPV surrogate among the studied ones.

Pulse Arrival Time and Pulse Interval as Accurate Markers to Detect Mechanical Alternans

Mechanical alternans (MA) is a powerful predictor of adverse prognosis in patients with heart failure and cardiomyopathy, but its use remains limited due to the need of invasive continuous arterial pressure recordings. This study aims to assess novel cardiovascular correlates of MA in the intact human heart to facilitate affordable and non-invasive detection of MA and advance our understanding of the underlying pathophysiology. Arterial pressure, respiration, and ECG were recorded in 12 subjects with healthy ventricles during voluntarily controlled breathing at different respiratory rate, before and after administration of beta-blockers. MA was induced by ventricular pacing. A total of 67 recordings lasting approximately 90 s each were analyzed. Mechanical alternans (MA) was measured in the systolic blood pressure. We studied cardiovascular correlates of MA, including maximum pressure rise during systole (dPdtmax), pulse arrival time (PAT), pulse wave interval (PI), RR interval (RRI), ECG QRS complexes and T-waves. MA was detected in 30% of the analyzed recordings. Beta-blockade significantly reduced MA prevalence (from 50 to 11%, p < 0.05). Binary classification showed that MA was detected by alternans in dPdtmax (100% sens, 96% spec), PAT (100% sens, 81% spec) and PI (80% sens, 81% spec). Alternans in PAT and in PI also showed high degree of temporal synchronization with MA (80 ± 33 and 73 ± 40%, respectively). These data suggest that cardiac contractility is a primary factor in the establishment of MA. Our findings show that MA was highly correlated with invasive measurements of PAT and PI. Since PAT and PI can be estimated using non-invasive technologies, these markers could potentially enable affordable MA detection for risk-prediction.

Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia

Baroreflex is a mechanism of short-term neural control responsible for maintaining stable levels of arterial blood pressure (ABP) in an ABP-heart rate negative feedback loop. Its function is assessed by baroreflex sensitivity (BRS)—a parameter which quantifies the relationship between changes in ABP and corresponding changes in heart rate (HR). The effect of postural change as well as the effect of changes in blood O₂ and CO₂ have been the focus of multiple previous studies on BRS. However, little is known about the influence of the combination of these two factors on dynamic baroreflex response. Furthermore, classical methods used for BRS assessment are based on the assumption of stationarity that may lead to unreliable results in the case of mostly nonstationary cardiovascular signals. Therefore, we aimed to investigate BRS during repeated transitions between squatting and standing in normal end-tidal CO₂ (EtCO₂) conditions (normocapnia) and conditions of progressively increasing EtCO₂ with a decreasing level of O₂ (hypercapnia with hypoxia) using joint time and frequency domain (TF) approach to BRS estimation that overcomes the limitation of classical methods. Noninvasive continuous measurements of ABP and EtCO₂ were conducted in a group of 40 healthy young volunteers. The time course of BRS was estimated from TF representations of pulse interval variability and systolic pressure variability, their coherence, and phase spectra. The relationship between time-variant BRS and indices of ABP and HR was analyzed during postural change in normocapnia and hypercapnia with hypoxia. In normocapnia, observed trends in all measures were in accordance with previous studies, supporting the validity of presented TF method. Similar but slightly attenuated response to postural change was observed in hypercapnia with hypoxia. Our results show the merits of the nonstationary methods as a tool to study the cardiovascular system during short-term hemodynamic changes.

A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects

This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (r_opt) corresponding to ApEn_max has been used for RQA calculation. The experimental data length (N) of RR interval series (RR_i) is optimized by taking r_opt as key parameter. r_opt is found to be lying within the recommended range of 0.1 to 0.25 times the standard deviation of the RR_i, when N ≥ 300. Consequently, RQA is applied to the age stratified RR_i and indices such as percentage recurrence (%REC), percentage laminarity (%LAM), and percentage determinism (%DET) are calculated along with ApEn_max, [Formula: see text], [Formula: see text], and an index namely the radius differential (R_D). Certain standard HRV statistical indices such as mean RR, standard deviation of RR (or NN) interval (SDNN), and the square root of the mean squared differences of successive RR intervals (RMSSD) (Eur Hear J 17:354-381, 1996) are also found for comparison. It is observed that (i) R_D can discriminate between the elderly and young subjects with a value of 0.1151 ± 0.0236 (mean ± SD) and 0.0533 ± 0.0133 (mean ± SD) respectively for the elderly and young subjects and is found to be statistically significant with p < 0.05. (ii) Similar significant discrimination was obtained using [Formula: see text] with a value of 0.1827 ± 0.0382 (mean ± SD) and 0.2248 ± 0.0320 (mean ± SD) (iii) other significant indices were found to be %REC, %DET, %LAM, SDNN, and RMSSD; however, ApEn_max was found to be insignificant with p > 0.05. The above features of RR_i time series were tested for classification using support vector machine (SVM) and multilayer perceptron neural network (MLPNN). Higher classification accuracy was achieved using SVM with a maximum value of 99.71%. Graphical abstract.

A Time-Varying Nonparametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

OBJECTIVE

To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in nonstationary conditions.

METHODS

Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the nonstationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging nonstationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed.

RESULTS

In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the nonstationary spectra. The correlation coefficient between theoretical and estimated patterns was even for extremely noisy recordings (signal to noise ratio (SNR) in QTV dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands ( for most pairwise comparisons).

CONCLUSION

The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV.

SIGNIFICANCE

The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death.

Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics

OBJECTIVE

Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions.

METHODS

We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov-Smirnov distance.

RESULTS AND CONCLUSION

Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling.

SIGNIFICANCE

This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain-heart or, more in general, brain-body interactions).

Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia

OBJECTIVE

Classic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment.

METHODS

Continuous recording of CBFV, ABP, ECG, and end-tidal CO2 were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time-frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02-0.07 Hz (VLF), 0.07-0.20 Hz (LF), and 0.20-0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed.

RESULTS

The hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF.

CONCLUSION

The time-frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.

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.

A multivariate time-frequency approach for tracking QT variability changes unrelated to heart rate variability

The beat-to-beat variability of the QT interval (QTV) is a marker of ventricular repolarization (VR) dynamics and it has been suggested as an index of sympathetic ventricular outflow and cardiac instability. However, QTV is also affected by RR (or heart rate) variability (RRV), and QTV due to RRV may reduce QTV specificity as a VR marker. Therefore, it would be desirable to separate QTV due to VR dynamics from QTV due to RRV. To do that, previous work has mainly focused on heart rate corrections or time-invariant autoregressive models. This paper describes a novel framework that extends classical multiple inputs/single output theory to the time-frequency (TF) domain to quantify QTV and RRV interactions. Quadratic TF distributions and TF coherence function are utilized to separate QTV into two partial (conditioned) spectra representing QTV related and unrelated to RRV, and to provide an estimates of intrinsic VR dynamics. In a simulation study, a time-varying ARMA model was used to generate signals representing realistic RRV and VR dynamics with controlled instantaneous frequencies and powers. The results demonstrated that the proposed methodology is able to accurately track changes in VR dynamics, with a correlation between theoretical and estimated patterns higher than 0.88. Data from healthy volunteers undergoing a tilt table test were analyzed and representative examples are discussed. Results show that the QTV unrelated to RRV dynamics quickly increased during orthostatic challenge.

Instantaneous transfer entropy for the study of cardio-respiratory dynamics

Measures of transfer entropy have been proposed to quantify the directional coupling and strength between two complex physiological variables. Particular attention has been given to nonlinear interactions within cardiovascular and respiratory dynamics as influenced by the autonomic nervous system. However, standard transfer entropy estimates have shown major limitations in dealing with issues concerning stochastic system modeling, limited observations in time, and the assumption of stationarity of the considered physiological variables. Moreover, standard estimates are unable to track time-varying changes in nonlinear coupling with high resolution in time. Here, we propose a novel definition of transfer entropy linked to inhomogeneous point-process theory. Heartbeat and respiratory dynamics are characterized through discrete time series, and modeled through probability density functions (PDFs) which characterize and predict the time until the occurrence of the next physiological event as a function of the past history. As the derived measures of entropy are instantaneously defined through continuos PDFs, a novel index (the Instantaneous point-process Transfer Entropy, ipT ransfEn) is able to provide instantaneous tracking of the information transfer. The new measure is tested on experimental data gathered from 16 healthy subjects undergoing postural changes, showing fast tracking of the tilting events and low variability during the standing phase.

Cardiorespiratory interactions: Noncontact assessment using laser Doppler vibrometry

The application of a noncontact physiological recording technique, based on the method of laser Doppler vibrometry (LDV), is described. The effectiveness of the LDV method as a physiological recording modality lies in the ability to detect very small movements of the skin, associated with internal mechanophysiological activities. The method is validated for a range of cardiovascular variables, extracted from the contour of the carotid pulse waveform as a function of phase of the respiration cycle. Data were obtained from 32 young healthy participants, while resting and breathing spontaneously. Individual beats were assigned to four segments, corresponding with inspiration and expiration peaks and transitional periods. Measures relating to cardiac and vascular dynamics are shown to agree with the pattern of effects seen in the substantial body of literature based on human and animal experiments, and with selected signals recorded simultaneously with conventional sensors. These effects include changes in heart rate, systolic time intervals, and stroke volume. There was also some evidence for vascular adjustments over the respiration cycle. The effectiveness of custom algorithmic approaches for extracting the key signal features was confirmed. The advantages of the LDV method are discussed in terms of the metrological properties and utility in psychophysiological research. Although used here within a suite of conventional sensors and electrodes, the LDV method can be used on a stand-alone, noncontact basis, with no requirement for skin preparation, and can be used in harsh environments including the MR scanner.

Assessing real-time RR-QT frequency-domain measures of coupling and causality through inhomogeneous point-process bivariate models

Ventricular repolarization instability is known to be related to arrhythmogenesis and increased risk of sudden cardiac death. These repolarization dynamics are linked to the distance between T-wave and Q-wave occurrences (QT) on the ECG, and they are coupled with R-wave to R-wave interval variability (RRV). Several efforts have been dedicated to the analysis of QT-RR interactions in order to provide both a quantification of the coupling and estimates of intrinsic repolarization dynamics. However, a methodology able to quantify dynamic changes in repolarization variability unrelated to RRV dynamics is still needed. In this study, we propose a bivariate model embedded within a multiple inhomogeneous point-process framework to obtain time-varying tracking of (causal) interactions between QT variability (QTV), a marker of repolarization variability, and RRV. Data from 15 healthy subjects undergoing a tilt table test were analyzed. Our results demonstrate that the model effectively captures the time-varying mutual QTV-RRV interactions. The analysis of time-varying coherence confirms that head-up tilt is associated with a decrease in linear QTV-RRV coupling, while time-varying directed coherence shows that intrinsic QTV becomes more prominent during head-up tilt.

Heart rate variability: a tool to explore the sleeping brain?

Sleep is divided into two main sleep stages: (1) non-rapid eye movement sleep (non-REMS), characterized among others by reduced global brain activity; and (2) rapid eye movement sleep (REMS), characterized by global brain activity similar to that of wakefulness. Results of heart rate variability (HRV) analysis, which is widely used to explore autonomic modulation, have revealed higher parasympathetic tone during normal non-REMS and a shift toward sympathetic predominance during normal REMS. Moreover, HRV analysis combined with brain imaging has identified close connectivity between autonomic cardiac modulation and activity in brain areas such as the amygdala and insular cortex during REMS, but no connectivity between brain and cardiac activity during non-REMS. There is also some evidence for an association between HRV and dream intensity and emotionality. Following some technical considerations, this review addresses how brain activity during sleep contributes to changes in autonomic cardiac activity, organized into three parts: (1) the knowledge on autonomic cardiac control, (2) differences in brain and autonomic activity between non-REMS and REMS, and (3) the potential of HRV analysis to explore the sleeping brain, and the implications for psychiatric disorders.

Oscillatory behavior of ventricular action potential duration in heart failure patients at respiratory rate and low frequency

Oscillations of arterial pressure occur spontaneously at a frequency of approximately 0.1 Hz coupled with synchronous oscillations of sympathetic nerve activity (“Mayer waves”). This study investigated the extent to which corresponding oscillations may occur in ventricular action potential duration (APD). Fourteen ambulatory (outpatient) heart failure patients with biventricular pacing devices were studied while seated upright watching movie clips to maintain arousal. Activation recovery intervals (ARI) as a measure of ventricular APD were obtained from unipolar electrograms recorded from the LV epicardial pacing lead during steady state RV pacing from the device. Arterial blood pressure was measured non-invasively (Finapress) and respiration monitored. Oscillations were quantified using time frequency and coherence analysis. Oscillatory behavior of ARI at the respiratory frequency was observed in all subjects. The magnitude of the ARI variation ranged from 2.2 to 6.9 ms (mean 5.0 ms). Coherence analysis showed a correlation with respiratory oscillation for an average of 43% of the recording time at a significance level of p < 0.05. Oscillations in systolic blood pressure in the Mayer wave frequency range were observed in all subjects for whom blood pressure was recorded (n = 13). ARI oscillation in the Mayer wave frequency range was observed in 6/13 subjects (46%) over a range of 2.9 to 9.2 ms. Coherence with Mayer waves at the p < 0.05 significance level was present for an average of 29% of the recording time. In ambulatory patients with heart failure during enhanced mental arousal, left ventricular epicardial APD (ARI) oscillated at the respiratory frequency (approximately 0.25 Hz). In 6 patients (46%) APD oscillated at the slower Mayer wave frequency (approximately 0.1 Hz). These findings may be important in understanding sympathetic activity-related arrhythmogenesis.

Kernel based support vector machine for the early detection of syncope during head-up tilt test

This study aims to analyze the autonomic nervous system response during head-up tilt test (HUTT), by exploring the changes in dynamic properties of heart rate variability in subjects with and without syncopes, to predict the outcome of HUTT. Baroreflex response, as well as linear and non-linear parameters of RR-interval time series, have been extracted from the ECG of 66 subjects: 35 with and 31 without syncope during HUTT. The results show that, when considering the first 15 min of tilting position, the total power spectrum, the standard deviation, the long-term fractal scale of RR-interval and ΔRR-interval of time series increase, while the sample entropy decreases in the positive group compared to the negative one. These indices may be good predictors of positive response in patients with reflex syncope. Additionally, an analysis of the first 15 min of tilting position using kernel support vector machines leads to a correct classification of 85% of patients, within negative and positive response groups (specificity = 80.6% and sensitivity = 88.5%). In medical applications, it is important to avoid false negative diagnosis of syncopes during HUTT. Taking this into account, an overall accuracy of 72.1% can be obtained in the same window allowing the reduction of the examination time in the clinical domain.

Estimation of instantaneous complex dynamics through Lyapunov exponents: a study on heartbeat dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the Hénon map and Rössler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations.

Comparative evaluation of methodologies for T-wave alternans mapping in electrograms

Electrograms (EGM) recorded from the surface of the myocardium are becoming more and more accessible. T-wave alternans (TWA) is associated with increased vulnerability to ventricular tachycardia/fibrillation and it occurs before the onset of ventricular arrhythmias. Thus, accurate methodologies for time-varying alternans estimation/detection in EGM are needed. In this paper, we perform a simulation study based on epicardial EGM recorded in vivo in humans to compare the accuracy of four methodologies: the spectral method (SM), modified moving average method, laplacian likelihood ratio method (LLR), and a novel method based on time-frequency distributions. A variety of effects are considered, which include the presence of wide band noise, respiration, and impulse artifacts. We found that 1) EGM-TWA can be detected accurately when the standard deviation of wide-band noise is equal or smaller than ten times the magnitude of EGM-TWA. 2) Respiration can be critical for EGM-TWA analysis, even at typical respiratory rates. 3) Impulse noise strongly reduces the accuracy of all methods, except LLR. 4) If depolarization time is used as a fiducial point, the localization of the T-wave is not critical for the accuracy of EGM-TWA detection. 5) According to this study, all methodologies provided accurate EGM-TWA detection/quantification in ideal conditions, while LLR was the most robust, providing better detection-rates in noisy conditions. Application on epicardial mapping of the in vivo human heart shows that EGM-TWA has heterogeneous spatio-temporal distribution.

Cardiorespiratory dynamic response to mental stress: a multivariate time-frequency analysis

Mental stress is a growing problem in our society. In order to deal with this, it is important to understand the underlying stress mechanisms. In this study, we aim to determine how the cardiorespiratory interactions are affected by mental arithmetic stress and attention. We conduct cross time-frequency (TF) analyses to assess the cardiorespiratory coupling. In addition, we introduce partial TF spectra to separate variations in the RR interval series that are linearly related to respiration from RR interval variations (RRV) that are not related to respiration. The performance of partial spectra is evaluated in two simulation studies. Time-varying parameters, such as instantaneous powers and frequencies, are derived from the computed spectra. Statistical analysis is carried out continuously in time to evaluate the dynamic response to mental stress and attention. The results show an increased heart and respiratory rate during stress and attention, compared to a resting condition. Also a fast reduction in vagal activity is noted. The partial TF analysis reveals a faster reduction of RRV power related to (3 s) than unrelated to (30 s) respiration, demonstrating that the autonomic response to mental stress is driven by mechanisms characterized by different temporal scales.

Cross time-frequency analysis for combining information of several sources: application to estimation of spontaneous respiratory rate from photoplethysmography

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.

Bivariate point process modeling and joint non-stationary analysis of pulse transit time and heart period

Pulse transit time (PTT) is strictly related to pulse wave velocity and may be used for non-invasive monitoring of arterial stiffness and pressure, whose assessment is fundamental to detect cardiovascular dysfunctions. We propose a new model to characterize instantaneous PTT dynamics, and the interactions between PTT and R-R interval (RRI). In this model, PTT is described as a point process whose probability function depends on previous PTT and RRI values. From the model coefficients, instantaneous powers, coherence and directed coherence of each spectral component are estimated. We used this framework to study the changes that tilt table test provoked in PTT and RRI dynamics in 17 healthy subjects. Time-varying spectral and coherence analysis revealed that, although PTT and RRI were locally correlated, direct contribution of RRI on PTT was low during the entire test in high frequency band, and just after postural changes in low frequency band. We conclude that PTT may add valuable information for a more accurate characterization of cardiovascular regulation.

Heart rate variability in patients with atrial septal defect and healthy children

OBJECTIVE

Heart rate variability (HRV) measures are altered in various cardiac and non-cardiac situations in children. The autonomic nervous system is assumed to have a role in the pathophysiology of atrial septal defect (ASD). In this study, we evaluated the autonomic system by measuring HRV in children with ASD.

METHODS

Twenty-eight patients with ASD and 32 healthy children (mean ages: 6.6±2.1 years and 6.4±2.2 years, respectively) were enrolled in the study. Twenty-four-hour ambulatory electrocardiographic recordings were obtained and the seven time-domain (SDNN, SDANN, rMSSD, SD, SDNN index, PNN50, and mean RR) and four frequency-domain (VLF, LF, HF, and LF/HF ratio) indices of HRV were analyzed.

RESULTS

A significant decrease in calculated HRV variables was observed in children with ASD as compared to controls. The HRV alteration was found in both time-domain and frequency-domain parameters.

CONCLUSIONS

Our results indicate that HRV is decreased in children with ASD, which implies parasympathetic withdrawal and sympathetic predominance.