Bidirectional Cardio-Respiratory Interactions in Heart Failure

The Impact of the Nervous System on Arteries and the Heart: The Neuroimmune Cardiovascular Circuit Hypothesis

Three systemic biological systems, i.e., the nervous, the immune, and the cardiovascular systems, form a mutually responsive and forward-acting tissue network to regulate acute and chronic cardiovascular function in health and disease. Two sub-circuits within the cardiovascular system have been described, the artery brain circuit (ABC) and the heart brain circuit (HBC), forming a large cardiovascular brain circuit (CBC). Likewise, the nervous system consists of the peripheral nervous system and the central nervous system with their functional distinct sensory and effector arms. Moreover, the immune system with its constituents, i.e., the innate and the adaptive immune systems, interact with the CBC and the nervous system at multiple levels. As understanding the structure and inner workings of the CBC gains momentum, it becomes evident that further research into the CBC may lead to unprecedented classes of therapies to treat cardiovascular diseases as multiple new biologically active molecules are being discovered that likely affect cardiovascular disease progression. Here, we weigh the merits of integrating these recent observations in cardiovascular neurobiology into previous views of cardiovascular disease pathogeneses. These considerations lead us to propose the Neuroimmune Cardiovascular Circuit Hypothesis.

Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy

The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (GC), Transfer Entropy (TE) and Cross Entropy (CE), to quantify the directed coupling and causality between cardiac (RR interval) and respiratory (Resp) time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the RR (high number of ventricular extrasystoles) and in the Resp time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that GC as a linear model measure is not sensitive to both nonlinear components and only model free measures as TE and CE may quantify them. CRT responders mainly exhibit unchanged asymmetry in the TE values, with statistically significant dominance of the information flow from Resp to RR over the opposite flow from RR to Resp, before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.

Exploring the use of Granger causality for the identification of chemical exposure based on physiological data

Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as for differentiating between the two. Here, we investigated how exposure to these different chemicals affects the interactions between ECG and respiration parameters as determined by Granger causality (GC). Features reflecting such interactions may provide additional information and improve models differentiating between chemical agents. Traditional respiration and ECG features, as well as GC features, were extracted from data of 120 guinea pigs exposed to VX (n = 61) or fentanyl (n = 59). Data were divided in a training set (n = 99) and a test set (n = 21). Minimum Redundancy Maximum Relevance (mRMR) and Support Vector Machine (SVM) algorithms were used to, respectively, perform feature selection and train a model to discriminate between the two chemicals. We found that ECG and respiration parameters are Granger-related under healthy conditions, and that exposure to fentanyl and VX affected these relationships in different ways. SVM models discriminated between chemicals with accuracy of 95% or higher on the test set. GC features did not improve the classification compared to traditional features. Respiration features (i.e., peak inspiratory and expiratory flow) were the most important to discriminate between different chemical’s exposure. Our results indicate that it may be feasible to discriminate between chemical exposure when using traditional physiological respiration features from wearable sensors. Future research will examine whether GC features can contribute to robust detection and differentiation between chemicals when considering other factors, such as generalizing results across species.

Two Operational Modes of Cardio-Respiratory Coupling Revealed by Pulse-Respiration Quotient

Due to the fact that respiratory breath-to-breath and cardiac intervals between two successive R peaks (BBI and RRI, respectively) are not temporally concurrent, in a previous paper, we proposed a method to calculate both the integer and non-integer parts of the pulse respiration quotient (PRQ = BBI/RRI = PRQ_int + b1 + b2), b1 and b2 being parts of the border RRIs for each BBI. In this work, we study the correlations between BBI and PRQ, as well as those between BBI and mean RRI within each BBI (mRRI), on a group of twenty subjects in four conditions: in supine and standing positions, in combination with spontaneous and slow breathing. Results show that the BBI vs. PRQ correlations are positive; whereas the breathing regime had little or no effect on the linear regression slopes, body posture did. Two types of scatter plots were obtained with the BBI vs. mRRI correlations: one showed points aggregated around the concurrent PRQ_int lines, while the other showed randomly distributed points. Five out of six of the proposed aggregation measures confirmed the existence of these two cardio-respiratory coupling regimes. We also used b1 to study the positions of R pulses relative to the respiration onsets and showed that they were more synchronous with sympathetic activation. Overall, this method should be used in different pathological states.

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland-Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R² = 0.86) and MSi (R² = 0.97); the Bland-Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

A Novel Broadband Forcecardiography Sensor for Simultaneous Monitoring of Respiration, Infrasonic Cardiac Vibrations and Heart Sounds

The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High-Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland–Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R² > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R² > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R² > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients.

Effects of Cardiac Resynchronization Therapy on Cardio-Respiratory Coupling

In this study, the effect of cardiac resynchronization therapy (CRT) on the relationship between the cardiovascular and respiratory systems in heart failure subjects was examined for the first time. We hypothesized that alterations in cardio-respiratory interactions, after CRT implantation, quantified by signal complexity, could be a marker of a favorable CRT response. Sample entropy and scaling exponents were calculated from synchronously recorded cardiac and respiratory signals 20 min in duration, collected in 47 heart failure patients at rest, before and 9 months after CRT implantation. Further, cross-sample entropy between these signals was calculated. After CRT, all patients had lower heart rate and CRT responders had reduced breathing frequency. Results revealed that higher cardiac rhythm complexity in CRT non-responders was associated with weak correlations of cardiac rhythm at baseline measurement over long scales and over short scales at follow-up recording. Unlike CRT responders, in non-responders, a significant difference in respiratory rhythm complexity between measurements could be consequence of divergent changes in correlation properties of the respiratory signal over short and long scales. Asynchrony between cardiac and respiratory rhythm increased significantly in CRT non-responders during follow-up. Quantification of complexity and synchrony between cardiac and respiratory signals shows significant associations between CRT success and stability of cardio-respiratory coupling.

Multiscale Entropy Analysis: Application to Cardio-Respiratory Coupling

It is known that in pathological conditions, physiological systems develop changes in the multiscale properties of physiological signals. However, in real life, little is known about how changes in the function of one of the two coupled physiological systems induce changes in function of the other one, especially on their multiscale behavior. Hence, in this work we aimed to examine the complexity of cardio-respiratory coupled systems control using multiscale entropy (MSE) analysis of cardiac intervals MSE (RR), respiratory time series MSE (Resp), and synchrony of these rhythms by cross multiscale entropy (CMSE) analysis, in the heart failure (HF) patients and healthy subjects. We analyzed 20 min of synchronously recorded RR intervals and respiratory signal during relaxation in the supine position in 42 heart failure patients and 14 control healthy subjects. Heart failure group was divided into three subgroups, according to the RR interval time series characteristics (atrial fibrillation (HFAF), sinus rhythm (HFSin), and sinus rhythm with ventricular extrasystoles (HFVES)). Compared with healthy control subjects, alterations in respiratory signal properties were observed in patients from the HFSin and HFVES groups. Further, mean MSE curves of RR intervals and respiratory signal were not statistically different only in the HFSin group (p = 0.43). The level of synchrony between these time series was significantly higher in HFSin and HFVES patients than in control subjects and HFAF patients (p < 0.01). In conclusion, depending on the specific pathologies, primary alterations in the regularity of cardiac rhythm resulted in changes in the regularity of the respiratory rhythm, as well as in the level of their asynchrony.

Sleep-Dependent Directional Interactions of the Central Nervous System-Cardiorespiratory Network

OBJECTIVE

We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep.

METHODS

Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied.

RESULTS

Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions.

CONCLUSION

These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms.

SIGNIFICANCE

Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.

Investigation of brain-heart network during sleep

Interactions between brain and heart play an important role for sleep quality and control. However, the influence mechanism was still unclear. This study aimed to further investigate this mechanism according to build an information transfer network of brain-heart coupling. This study included 24 healthy individuals and both of them underwent overnight polysomnography. The relative spectral powers of five frequency bands and the high frequency power of heart rate variability were extracted from six electroencephalogram (EEG) channels and electrocardiography (ECG) respectively. For each EEG channel, brain-heart interaction networks were built and a directionality analysis was conducted by using multivariate transfer entropy. Results revealed the bidirectionality of information transfer between brain and heart during sleep, and the information was dominantly transfer from heart to brain. The information transfer strength between brain and heart were significantly stronger than which between frequency bands in each EEG channels. Besides, the frequency bands and EEG channels had evident influence on these interactions. This study exposed more detailed characteristics of brain-heart interaction, which will facilitate the future study about the sleep control and the diagnose of sleep related disease.

Slow 0.1 Hz Breathing and Body Posture Induced Perturbations of RRI and Respiratory Signal Complexity and Cardiorespiratory Coupling

Objective: We explored the physiological background of the non-linear operating mode of cardiorespiratory oscillators as the fundamental question of cardiorespiratory homeodynamics and as a prerequisite for the understanding of neurocardiovascular diseases. We investigated 20 healthy human subjects for changes using electrocardiac RR interval (RRI) and respiratory signal (Resp) Detrended Fluctuation Analysis (DFA, α1RRI, α2RRI, α1Resp, α2Resp), Multiple Scaling Entropy (MSERRI1-4, MSERRI5-10, MSEResp1-4, MSEResp5-10), spectral coherence (CohRRI-Resp), cross DFA (ρ1 and ρ2) and cross MSE (XMSE1-4 and XMSE5-10) indices in four physiological conditions: supine with spontaneous breathing, standing with spontaneous breathing, supine with 0.1 Hz breathing and standing with 0.1 Hz breathing. Main results: Standing is primarily characterized by the change of RRI parameters, insensitivity to change with respiratory parameters, decrease of CohRRI-Resp and insensitivity to change of in ρ1, ρ2, XMSE1-4, and XMSE5-10. Slow breathing in supine position was characterized by the change of the linear and non-linear parameters of both signals, reflecting the dominant vagal RRI modulation and the impact of slow 0.1 Hz breathing on Resp parameters. CohRRI-Resp did not change with respect to supine position, while ρ1 increased. Slow breathing in standing reflected the qualitatively specific state of autonomic regulation with striking impact on both cardiac and respiratory parameters, with specific patterns of cardiorespiratory coupling. Significance: Our results show that cardiac and respiratory short term and long term complexity parameters have different, state dependent patterns. Sympathovagal non-linear interactions are dependent on the pattern of their activation, having different scaling properties when individually activated with respect to the state of their joint activation. All investigated states induced a change of α1 vs. α2 relationship, which can be accurately expressed by the proposed measure-inter-fractal angle θ. Short scale (α1 vs. MSE1-4) and long scale (α2 vs. MSE5-10) complexity measures had reciprocal interrelation in standing with 0.1 Hz breathing, with specific cardiorespiratory coupling pattern (ρ1 vs. XMSE1-4). These results support the hypothesis of hierarchical organization of cardiorespiratory complexity mechanisms and their recruitment in ascendant manner with respect to the increase of behavioral challenge complexity. Specific and comprehensive cardiorespiratory regulation in standing with 0.1 Hz breathing suggests this state as the potentially most beneficial maneuver for cardiorespiratory conditioning.

Differentiation of Heart Failure Patients by the Ratio of the Scaling Exponents of Cardiac Interbeat Intervals

Heart failure (HF) is one of the most frequent heart diseases. It is usually characterized with structural and functional cardiac abnormalities followed by dysfunction of autonomic cardiac control. Current methods of heartbeat interval analysis are not capable to differentiate HF patients and some new differentiation of HF patients could be useful in the determination of the direction of their treatment. In this study, we examined potential of the ratio of the short-term and long-term scaling exponents (α 1 and α 2) to separate HF patients with similar level of reduced cardiac autonomic nervous system control and with no significant difference in age, left ventricular ejection fraction (LVEF) and NYHA class. Thirty-five healthy control subjects and 46 HF patients underwent 20 min of continuous supine resting ECG recording. The interbeat interval time series were analyzed using standardized power spectrum analysis, detrended fluctuation analysis method and standard Poincaré plot (PP) analysis with measures of asymmetry of the PP. Compared with healthy control group, in HF patients linear measures of autonomic cardiac control were statistically significantly reduced (p < 0.05), heart rate asymmetry was preserved (C up > C down, p < 0.01), and long-term scaling exponent α 2 was significantly higher. Cluster analysis of the ratio of short- and long-term scaling exponents showed capability of this parameter to separate four clusters of HF patients. Clusters were determined by interplay of presence of short-term and long-term correlations in interbeat intervals. Complementary measure, commonly accepted ratio of the PP descriptors, SD2/SD1, showed tendency toward statistical significance to separate HF patients in obtained clusters. Also, heart rate asymmetry was preserved only in two clusters. Finally, a multiple regression analysis showed that the ratio α 1/α 2 could be used as an integrated measure of cardiac dynamic with complex physiological background which, besides spectral components as measures of autonomic cardiac control, also involves breathing frequency and mechanical cardiac parameter, left ventricular ejection fraction.

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.