A quantitative comparison of different methods to detect cardiorespiratory coordination during night-time sleep

Characterization of Multiple Regimes of Cardiorespiratory Phase Synchronization in Athletes Undergoing Inspiratory Muscle Training

Cardiorespiratory phase synchronization (CRPS) is defined as the stable occurrence of n heartbeats within m respiratory cycles according to the n:m phase locking ratio (PLR). Since CRPS is an intermittent phenomenon where different phase synchronization regimes and epochs of phase unlocking can alternate within the same recording, an index of CRPS ideally should assess all potential PLRs present in the recording. However, traditional approaches compute the synchronization index (SYNC%) over a single n:m PLR, namely the one that maximizes CRPS. In the present work, we tested a synchronization index assessing the total percentage of heartbeats coupled to the inspiratory onset regardless of phase locking regimes (SYNC%sum) and we compared its efficacy to the more traditional SYNC%. Analysis was carried out in a cohort of 25 male amateur cyclists (age: 20-40 yrs) undergoing inspiratory muscle training (IMT) at different intensities. CRPS was assessed before and after the IMT protocol, during an experimental condition known to modify CRPS, namely active standing (STAND). We found that after a moderate intensity IMT at 60% of the maximal inspiratory pressure, SYNC%sum could detect the decrease in CRPS following STAND. This result was not visible using the more traditional SYNC%. Therefore, we stress the significant presence of different phase locking regimes in athletes and the importance of accounting for multiple PLRs in CRPS analysis.Clinical Relevance- Multiple phase locking regimes contribute significantly to cardiorespiratory control in amateur cyclists especially after inspiratory muscle training of moderate intensity.

Cardiorespiratory coupling in mechanically ventilated patients studied via synchrogram analysis

Respiration and cardiac activity are strictly interconnected with reciprocal influences. They act as weakly coupled oscillators showing varying degrees of phase synchronization and their interactions are affected by mechanical ventilation. The study aims at differentiating the impact of three ventilatory modes on the cardiorespiratory phase coupling in critically ill patients. The coupling between respiration and heartbeat was studied through cardiorespiratory phase synchronization analysis carried out via synchrogram during pressure control ventilation (PCV), pressure support ventilation (PSV), and neurally adjusted ventilatory assist (NAVA) in critically ill patients. Twenty patients were studied under all the three ventilatory modes. Cardiorespiratory phase synchronization changed significantly across ventilatory modes. The highest synchronization degree was found during PCV session, while the lowest one with NAVA. The percentage of all epochs featuring synchronization regardless of the phase locking ratio was higher with PCV (median: 33.9%, first-third quartile: 21.3-39.3) than PSV (median: 15.7%; first-third quartile: 10.9-27.8) and NAVA (median: 3.7%; first-third quartile: 3.3-19.2). PCV induces a significant amount of cardiorespiratory phase synchronization in critically ill mechanically ventilated patients. Synchronization induced by patient-driven ventilatory modes was weaker, reaching the minimum with NAVA. Findings can be explained as a result of the more regular and powerful solicitation of the cardiorespiratory system induced by PCV. The degree of phase synchronization between cardiac and respiratory activities in mechanically ventilated humans depends on the ventilatory mode.

Optimizing phase variability threshold for automated synchrogram analysis of cardiorespiratory interactions in amateur cyclists

We propose a procedure suitable for automated synchrogram analysis for setting the threshold below which phase variability between two marker event series is of such a negligible amount that the null hypothesis of phase desynchronization can be rejected. The procedure exploits the principle of maximizing the likelihood of detecting phase synchronization epochs and it is grounded on a surrogate data approach testing the null hypothesis of phase uncoupling. The approach was applied to assess cardiorespiratory phase interactions between heartbeat and inspiratory onset in amateur cyclists before and after 11-week inspiratory muscle training (IMT) at different intensities and compared to a more traditional approach to set phase variability threshold. The proposed procedure was able to detect the decrease in cardiorespiratory phase locking strength during vagal withdrawal induced by the modification of posture from supine to standing. IMT had very limited effects on cardiorespiratory phase synchronization strength and this result held regardless of the training intensity. In amateur athletes training, the inspiratory muscles did not limit the decrease in cardiorespiratory phase synchronization observed in the upright position as a likely consequence of the modest impact of this respiratory exercise, regardless of its intensity, on cardiac vagal control. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Characterizing cardiorespiratory interaction in preterm infants across sleep states using visibility graph analysis

Cardiorespiratory interaction (CRI) has been intensively studied in adult sleep, yet not in preterm infants, in particular across different sleep states including wake (W), active sleep (AS), and quiet sleep (QS). The aim of this study was to quantify the interaction between cardiac and respiratory activities in different sleep states of preterm infants. The postmenstrual age (PMA) of preterm infants was also taken into consideration. The CRI during sleep was analyzed using a visibility graph (VG) method, enabling the nonlinear analysis of CRI in a complex network. For each sleep state, parameters quantifying various aspects of the CRI characteristics from constructed VG network including mean degree (D_m) and its variability (D_sd), clustering coefficient (CC_m) and its variability (CC_sd), assortativity coefficient (AC), and complexity (D_SE) were extracted from the CRI networks. The interaction effect of sleep state and PMA was found to be statistically significant on all CRI parameters except for AC and D_SE. The main effect between sleep state and CRI parameters was statistically significant except for CC_m, and that between PMA and CRI parameters was statistically significant except for D_SE. In conclusion, the CRI of preterm infants is associated with sleep states and PMA in general. For preterm infants with a larger PMA, CRI has a more clustered pattern during different sleep states, where QS shows a more regular, stratified, and stronger CRI than other states. In the future, these parameters can be potentially used to separate sleep states in preterm infants.NEW & NOTEWORTHY The interaction between cardiac and respiratory activities is investigated in preterm infant sleep using an advanced nonlinear method (visibility graph) and some important characteristics are shown to be significantly different across sleep states, which has not been studied before.

Effect of Stress on Cardiorespiratory Synchronization of Ironman Athletes

The aim of this paper is to investigate the cardiorespiratory synchronization in athletes subjected to extreme physical stress combined with a cognitive stress tasks. ECG and respiration were measured in 14 athletes before and after the Ironman competition. Stroop test was applied between the measurements before and after the Ironman competition to induce cognitive stress. Synchrogram and empirical mode decomposition analysis were used for the first time to investigate the effects of physical stress, induced by the Ironman competition, on the phase synchronization of the cardiac and respiratory systems of Ironman athletes before and after the competition. A cognitive stress task (Stroop test) was performed both pre- and post-Ironman event in order to prevent the athletes from cognitively controlling their breathing rates. Our analysis showed that cardiorespiratory synchronization increased post-Ironman race compared to pre-Ironman. The results suggest that the amount of stress the athletes are recovering from post-competition is greater than the effects of the Stroop test. This indicates that the recovery phase after the competition is more important for restoring and maintaining homeostasis, which could be another reason for stronger synchronization.

Wearable monitoring of sleep-disordered breathing: estimation of the apnea-hypopnea index using wrist-worn reflective photoplethysmography

A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea-hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen's kappa = 0.51) and screen for OSA (ROC-AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring.

Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, which results in daytime symptoms, a reduced quality of life as well as long-term negative health consequences. OSA diagnosis and severity rating is typically based on the apnea-hypopnea index (AHI) retrieved from overnight poly(somno)graphy. However, polysomnography is costly, obtrusive and not suitable for long-term recordings. Here, we present a method for unobtrusive estimation of the AHI using ECG-based features to detect OSA-related events. Moreover, adding ECG-based sleep/wake scoring yields a fully automatic method for AHI-estimation. Importantly, our algorithm was developed and validated on a combination of clinical datasets, including datasets selectively including OSA-pathology but also a heterogeneous, “real-world” clinical sleep disordered population (262 participants in the validation set). The algorithm provides a good representation of the current gold standard AHI (0.72 correlation, estimation error of 0.56 ± 14.74 events/h), and can also be employed as a screening tool for a large range of OSA severities (ROC AUC ≥ 0.86, Cohen’s kappa ≥ 0.53 and precision ≥70%). The method compares favourably to other OSA monitoring strategies, showing the feasibility of cardiovascular-based surrogates for sleep monitoring to evolve into clinically usable tools.

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen's k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

Cardiorespiratory coordination reveals training-specific physiological adaptations

PURPOSE

To compare the effects of high-intensity interval training (HIIT) and moderate-intensity training (CONT), matched for total work, on cardiorespiratory coordination and aerobic fitness.

METHODS

This is a two-arm parallel group single-blind randomised study. Twenty adults were assigned to 6 weeks of HIIT or volume-matched CONT. Participants completed a progressive maximal cycling test before and after the training period. Principal component (PC) analysis was performed on the series of cardiorespiratory variables to evaluate dimensionality of cardiorespiratory coordination, before and after lactate turnpoint. PC₁ eigenvalues were compared.

RESULTS

Both HIIT and CONT improved aerobic fitness (main effects of time, p < 0.001, [Formula: see text] ≥ 0.580), with no differences between groups. CONT decreased the number of PCs from two to one at intensities both below and above the lactate turnpoint; PC₁ eigenvalues increased after CONT both below (Z = 2.08; p = 0.04; d = 0.94) and above the lactate turnpoint (Z = 2.10; p = 0.04; d = 1.37). HIIT decreased the number of PCs from two to one after the lactate turnpoint only; PC₁ eigenvalues increased after HIIT above the lactate turnpoint (Z = 2.31; p = 0.02; d = 0.42).

CONCLUSIONS

Although CONT and HIIT improved aerobic fitness to a similar extent, there were different patterns of change for cardiorespiratory coordination. These changes appear training-intensity specific and could be sensitive to investigate the individual response to endurance training.

Increased cardiorespiratory synchronization evoked by a breath controller based on heartbeat detection

BACKGROUND

The cardiovascular and respiratory systems are functionally related to each other, but the underlying physiologic mechanism of cardiorespiratory coupling (CRC) is unclear. Cardiopulmonary phase synchronization is a form of cardiorespiratory coupling. However, it is difficult to study in experimental data which are very often inherently nonstationary and thus contain only quasiperiodic oscillations. So how to enhance cardiopulmonary synchronization and quantify cardiopulmonary synchronization, the changes in cardiac function under the conditions of cardiopulmonary synchronization, and the physiological mechanisms behind them are the main issues to be discussed in this paper.

RESULTS

The results showed that the cardiorespiratory synchronization significantly increased when breathing was controlled by heartbeat detection (p < 0.001). And the respiratory sinus arrhythmia (RSA) obviously decreased (p < 0.01) in the 2/2 mode and increased (p < 0.001) in the 4/4 mode. During the 2/2 breathing pattern compared with spontaneous breathing, systolic blood pressure (SBP) decreased (p < 0.05), and diastolic blood pressure (DBP), mean arterial blood pressure (MBP), and SV decreased significantly (p < 0.01). During the 4/4 breathing pattern compared to 2/2 breathing patterns, DBP, MBP, and cardiac output (CO) increased (p < 0.05), and stroke volume (SV) increased significantly (p < 0.01). When analyzing the relationships among these parameters, the RSA was found to be associated with the respiration rate in all respiratory patterns.

CONCLUSIONS

We demonstrated that voluntary cardiorespiratory synchronization (VCRS) can effectively enhance cardiopulmonary phase synchronization, but cardiopulmonary phase synchronization and RSA represent different aspects of the cardiorespiratory interaction. It is found that cardiac function parameters such as the blood pressure and output per stroke could be affected by the number of heartbeats contained in the exhalation and inspiratory phase regulated through VCRS. So we can study cardiopulmonary phase synchronization by VCRS. It can be used to study in experimental data for the physiological mechanism of cardiopulmonary coupling.

Characterization of cardiorespiratory phase synchronization and directionality in late premature and full term infants

OBJECTIVE

Though the mutual influence of cardiovascular and respiratory rhythms in healthy newborns has been documented, its full characterization is still pending. In general, the activity of many physiological subsystems has a well-expressed rhythmic character, and often an interdependency between physiological rhythms emerges early in development. Traditional methods of data analysis only address the quantification of the strength of subsystem interactions. In this work, we will investigate system interrelationships in terms of the possible presence of causal or directional interplays.

APPROACH

In this paper, we propose a methodological application that quantifies phase coupling and its directionality in a population of newborn infants born between 35 and 40 weeks of gestational age (GA). The aim is to assess whether GA at birth significantly influences the development of phase synchronization and the directionality of the coupling between the cardiovascular and respiratory system activity. Several studies indicating irregular cardiorespiratory coupling as a leading cause of several pathologies underscore the need to investigate this phenomenon in this at-risk population.

MAIN RESULTS

Results from our investigation show a different directionality profile as a function of GA and sleep state.

SIGNIFICANCE

These findings are a contribution to the understanding of higher risk for the documented negative outcomes in the late preterm population. Moreover, these parameters could provide a tool for the development of early markers of cardiorespiratory dysregulation in infants.

Quantifying cardio-respiratory phase synchronization-a comparison of five methods using ECGs of post-infarction patients

OBJECTIVE

Phase synchronization between two weakly coupled oscillators occurs in many natural systems. Since it is difficult to unambiguously detect such synchronization in experimental data, several methods have been proposed for this purpose. Five popular approaches are systematically optimized and compared here.

APPROACH

We study and apply the automated synchrogram method, the reduced synchrogram method, two variants of a gradient method, and the Fourier mode method, analyzing 24h data records from 1455 post-infarction patients, the same data with artificial inaccuracies, and corresponding surrogate data generated by Fourier phase randomization.

MAIN RESULTS

We find that the automated synchrogram method is the most robust of all studied approaches when applied to records with missing data or artifacts, whereas the gradient methods should be preferred for noisy data and low-accuracy R-peak positions. We also show that a strong circadian rhythm occurs with much more frequent phase synchronization episodes observed during night time than during day time by all five methods.

SIGNIFICANCE

In specific applications, the identified characteristic differences as well as strengths and weaknesses of each method in detecting episodes of cardio-respiratory phase synchronization will be useful for selecting an appropriate method with respect to the type of systematic and dynamical noise in the data.

Mechanical ventilatory modes and cardioventilatory phase synchronization in acute respiratory failure patients

Cardioventilatory phase synchronization was studied in ten critically ill patients admitted in intensive care unit (ICU) for acute respiratory failure under two mechanical ventilatory modes: (i) pressure controlled ventilation (PCV); (ii) pressure support ventilation (PSV). The two modalities were administered to the same patient in different times in a random order. Cardioventilatory phase interactions were typified by plotting the relative position of a heartbeat, detected from the electrocardiogram and collected in n groups, within m ventilatory cycles as a function of the progressive cardiac beat number via the synchrogram. n:m phase synchronized patterns were detected by computing the variability of each phase group. The percent duration of the recording featuring phase synchronization was assessed as a measure of the strength of phase synchrony and tested against situations of full phase desynchronization between cardiac and ventilatory rhythms. Indexes quantifying the variability of the cardiac and ventilatory activities were computed as well. Findings proved that: (i) a significant presence of n:m phase synchronized patterns was detected in PCV; (ii) the strength of n:m phase synchronization was stronger during PCV than PSV; (iii) different strengths of cardioventilatory phase synchronization detected during PCV and PSV were found in presence of similar heart and ventilatory rates and alike variability. We conclude that mechanical ventilation can induce a significant presence of cardioventilatory phase synchronized patterns and this amount depends on the mode of mechanical ventilation. Future studies should test the eventual link of the level of phase coordination between heart and mechanical ventilation to a clinical outcome to understand whether featuring a certain degree of cardioventilatory phase synchronization is beneficial for the critical patient in ICU.

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave).

Cardiorespiratory Phase Synchronization in OSA subjects during wake and sleep states

Cardiorespiratory Phase Synchronization (CRPS) is a manifestation of coupling between cardiac and respiratory systems complementary to Respiratory Sinus Arrhythmia. In this work, we investigated CRPS during wake and sleep stages in Polysomnographic (PSG) recordings of 30 subjects suspected from Obstructive Sleep Apnea (OSA). The population was classified into three severity groups according to the Apnea Hypopnea Index (AHI): G1 (AHI<;15), G2 (15<;=AHI<;30) and G3 (AHI>30). The synchrogram between single lead ECG and respiratory abdominal band signals from PSG was computed with the Hilbert transform technique. The different phase locking ratios (PLR) m:n were monitored throughout the night. Ratio 4:1 was the most frequent and it became more dominant as OSA severity increased. CRPS was characterized by the percentage of synchronized time (%Sync) and the average duration of synchronized epochs (AvDurSync) using three different thresholds. Globally, we observed that %Sync significantly decreased and AvDurSync slightly increased with OSA severity. A high synchronization threshold enhanced these population differences. %Sync was significantly higher in NREM than in REM sleep in G2 and G3 groups. Population differences observed during sleep did not translate to the initial wake state. Reduced CRPS could be an early marker of OSA severity during sleep, but further studies are needed to determine whether CRPS is also present during wakefulness.

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

This paper explores the probabilistic properties of sleep stage sequences and transitions to improve the performance of sleep stage detection using cardiorespiratory features. A new classifier, based on conditional random fields, is used in different sleep stage detection tasks (N3, NREM, REM, and wake) in night-time recordings of electrocardiogram and respiratory inductance plethysmography of healthy subjects. Using a dataset of 342 polysomnographic recordings of healthy subjects, among which 135 with regular sleep architecture, it outperforms hidden Markov models and Bayesian linear discriminants in all tasks, achieving an average accuracy of 87.38% and kappa of 0.41 (87.27% and 0.49 for regular subjects) for N3 detection, 78.71% and 0.55 (80.34% and 0.56 for regular subjects) for NREM detection, 88.49% and 0.51 (87.35% and 0.57 for regular subjects) for REM, and 85.69% and 0.51 (90.42% and 0.52 for regular subjects) for wake. In comparison with the state of the art, and having been tested on a much larger dataset, the classifier was found to outperform most of the work reported in the literature for some of the tasks, in particular for subjects with regular sleep architecture. It achieves a comparable accuracy for N3, higher accuracy and kappa for REM, and higher accuracy and comparable kappa for NREM than the best performing classifiers described in the literature.

Modeling heart rate variability including the effect of sleep stages

We propose a model for heart rate variability (HRV) of a healthy individual during sleep with the assumption that the heart rate variability is predominantly a random process. Autonomic nervous system activity has different properties during different sleep stages, and this affects many physiological systems including the cardiovascular system. Different properties of HRV can be observed during each particular sleep stage. We believe that taking into account the sleep architecture is crucial for modeling the human nighttime HRV. The stochastic model of HRV introduced by Kantelhardt et al. was used as the initial starting point. We studied the statistical properties of sleep in healthy adults, analyzing 30 polysomnographic recordings, which provided realistic information about sleep architecture. Next, we generated synthetic hypnograms and included them in the modeling of nighttime RR interval series. The results of standard HRV linear analysis and of nonlinear analysis (Shannon entropy, Poincaré plots, and multiscale multifractal analysis) show that-in comparison with real data-the HRV signals obtained from our model have very similar properties, in particular including the multifractal characteristics at different time scales. The model described in this paper is discussed in the context of normal sleep. However, its construction is such that it should allow to model heart rate variability in sleep disorders. This possibility is briefly discussed.

Study of phase estimation methods to analyse cardiorespiratory synchronization in OSA patients

Obstructive Sleep Apnea (OSA) is a sleep disorder highly prevalent in the general population. Cardiorespiratory Phase Synchronization (CRPS) is a form of non-linear interaction between respiratory and cardiovascular systems that was found to be reduced in severe OSA patients. The Hilbert Transform (HT) method was the recommended choice for estimating the respiratory phase in CRPS studies. But we have noticed that HT provides a phase that is aligned to the transition between the exhalation and the inhalation parts of different breathing cycles, instead of being aligned to the breathing onsets. In this work we proposed a Realigned HT phase estimation method (RHT) and we compared it to the conventional HT and to the Linear Phase (LP) approximation for estimating CRPS in a database of 28 patients with different OSA severity levels. RHT provided similar synchronization percentages (%Sync) as HT, and it enhanced the significant differences in %Sync between mild and severe OSA patients. %Sync showed the highest negative correlation with the Apnea-Hypopnea Index (AHI) when using RHT (r_AHI=-0.692, p<;0.001), which only had an 10% extra computational cost. On the other hand, LP method significantly overestimated %Sync especially in the more severe patients, because it was unable to track the phase non-linearities that can be observed during sleep disordered breathing. Therefore, the newly proposed RHT can be the preferred alternative over the conventional HT or the LP approximation for estimating CRPS in OSA patients.

Dynamics of AV coupling during human atrial fibrillation: role of atrial rate

The causal relationship between atrial and ventricular activities during human atrial fibrillation (AF) is poorly understood. This study analyzed the effects of an increase in atrial rate on the link between atrial and ventricular activities during AF. Atrial and ventricular time series were determined in 14 patients during the spontaneous acceleration of the atrial rhythm at AF onset. The dynamic relationship between atrial and ventricular activities was quantified in terms of atrioventricular (AV) coupling by AV synchrogram analysis. The technique identified n: m coupling patterns ( n atrial beats in m ventricular cycles), quantifying their percentage, maximal length, and conduction ratio (= m/ n). Simulations with a difference-equation AV model were performed to correlate the observed dynamics to specific atrial/nodal properties. The atrial rate increase significantly affected AV coupling and ventricular response during AF. The shortening of atrial intervals from 185 ± 32 to 165 ± 24 ms ( P < 0.001) determined transitions toward AV patterns with progressively decreasing m/ n ratios (from conduction ratio = 0.34 ± 0.09 to 0.29 ± 0.08, P < 0.01), lower occurrence (from percentage of coupled beats = 27.1 ± 8.0 to 21.8 ± 6.9%, P < 0.05), and higher instability (from maximal length = 3.9 ± 1.5 to 2.8 ± 0.7 s, P < 0.01). Advanced levels of AV block and coupling instability at higher atrial rates were associated with increased ventricular interval variability (from 123 ± 52 to 133 ± 55 ms, P < 0.05). AV pattern transitions and coupling instability in patients were predicted, assuming the filtering of high-rate irregular atrial beats by the slow recovery of nodal excitability. These results support the role of atrial rate in determining AV coupling and ventricular response and may have implications for rate control in AF.

Joint symbolic dynamics for the assessment of cardiovascular and cardiorespiratory interactions

Beat-to-beat variations in heart period provide information on cardiovascular control and are closely linked to variations in arterial pressure and respiration. Joint symbolic analysis of heart period, systolic arterial pressure and respiration allows for a simple description of their shared short-term dynamics that are governed by cardiac baroreflex control and cardiorespiratory coupling. In this review, we discuss methodology and research applications. Studies suggest that analysis of joint symbolic dynamics provides a powerful tool for identifying physiological and pathophysiological changes in cardiovascular and cardiorespiratory control.

Effects of acute hypoxia on heart rate variability, sample entropy and cardiorespiratory phase synchronization

BACKGROUND

Investigating the responses of autonomic nervous system (ANS) in hypoxia may provide some knowledge about the mechanism of neural control and rhythmic adjustment. The integrated cardiac and respiratory system display complicated dynamics that are affected by intrinsic feedback mechanisms controlling their interaction. To probe how the cardiac and respiratory system adjust their rhythms in different simulated altitudes, we studied heart rate variability (HRV) in frequency domain, the complexity of heartbeat series and cardiorespiratory phase synchronization (CRPS) between heartbeat intervals and respiratory cycles.

METHODS

In this study, twelve male subjects were exposed to simulated altitude of sea level, 3000 m and 4000 m in a hypobaric chamber. HRV was assessed by power spectral analysis. The complexity of heartbeat series was quantified by sample entropy (SampEn). CRPS was determined by cardiorespiratory synchrogram.

RESULTS

The power spectral HRV indices at all frequency bands depressed according to the increase of altitude. The SampEn of heartbeat series increased significantly with the altitude (P < 0.01). The duration of CRPS epochs at 3000 m was not significantly different from that at sea level. However, it was significantly longer at 4000 m (P < 0.01).

CONCLUSIONS

Our results suggest the phenomenon of CRPS exists in normal subjects when they expose to acute hypoxia. Further, the autonomic regulation has a significantly stronger influence on CRPS in acute hypoxia. The changes of CRPS and HRV parameters revealed the different regulatory mechanisms of the cardiac and respiratory system at high altitude.

Cardio-respiratory coordination increases during sleep apnea

Cardiovascular diseases are the main source of morbidity and mortality in the United States with costs of more than $170 billion. Repetitive respiratory disorders during sleep are assumed to be a major cause of these diseases. Therefore, the understanding of the cardio-respiratory regulation during these events is of high public interest. One of the governing mechanisms is the mutual influence of the cardiac and respiratory oscillations on their respective onsets, the cardio-respiratory coordination (CRC). We analyze this mechanism based on nocturnal measurements of 27 males suffering from obstructive sleep apnea syndrome. Here we find, by using an advanced analysis technique, the coordigram, not only that the occurrence of CRC is significantly more frequent during respiratory sleep disturbances than in normal respiration (p-value<10⁻⁵¹) but also more frequent after these events (p-value<10⁻¹⁵). Especially, the latter finding contradicts the common assumption that spontaneous CRC can only be observed in epochs of relaxed conditions, while our newly discovered epochs of CRC after disturbances are characterized by high autonomic stress. Our findings on the connection between CRC and the appearance of sleep-disordered events require a substantial extension of the current understanding of obstructive sleep apneas and hypopneas.

Impact of colored light on cardiorespiratory coordination

Background. Light exposure to the eye can influence different physiological functions, for example, the suprachiasmatic nucleus (SCN). By affecting the autonomic nervous system, the SCN may influence the heart rate variability (HRV). Standardized colored light exposure alters HRV but the results are inconsistent. In this study we investigated the effects of nonstandardized red light (approx. 640 nm) and blue (approx. 480 nm) light (approx. 50 lx) on cardiorespiratory coordination and HRV. Methods. 17 healthy subjects (7 males, age: 26.5 ± 6.2 years) were exposed to the following sequence (10 minutes each): daylight-red light-daylight-blue light-daylight. Red and blue lights were created by daylight passing through colored glass panes. Spectral measures of HRV (LF: low frequency, HF: high frequency oscillations, and sympathovagal balance LF/HF) and measures of cardiorespiratory coordination (HRR: heart respiration ratio, PCR: phase coordination ratio) were analyzed. Results. The LF component increased and the HF component decreased after red light. Consequently, LF/HF increased after red light. Furthermore, during red light HRR and PCR confined to 4 : 1, that is, 4 heartbeats during one respiratory cycle. Conclusion. Nonstandardized red and blue lights are able to alter the autonomic control reflected by HRV as well as cardiorespiratory coordination.

Investigating the beat by beat phase synchronization between maternal and fetal heart rates

The development of the fetal cardiovascular system plays a crucial role in fetal health. The evolution of the relationship between fetal and maternal cardiac systems during fetal maturation is a characterizing feature for fetal cardiac development. This paper aims to evaluate this relationship by investigating the beat-to-beat synchronization between fetal and maternal heart rates and its variation at different stages of pregnancy. Synchronization epochs and phase locking patterns are analyzed at certain synchronization ratios (SRs) for three gestational age groups (16-26 weeks, 27-33 weeks, 34-40 weeks). Results show that the normalized synchronization epoch is significantly different for three age groups with the p-value of 6.72*10(-6) and 2.89*10(-4) at SR of 1:2 and 4:5 respectively. The variance of phase locking also shows significant difference for three groups with the p-value less than 10(-7) at four SRs. Results also suggest that synchronization may be the force behind the increase in the maternal heart rate to maintain the fetal development and provide supplies for the fetus. Overall, the findings propose new clinical markers for evaluating the antenatal development.

Modulation of the Sympatho-Vagal Balance during Sleep: Frequency Domain Study of Heart Rate Variability and Respiration

Sleep is a complex state characterized by important changes in the autonomic modulation of the cardiovascular activity. Heart rate variability (HRV) greatly changes during different sleep stages, showing a predominant parasympathetic drive to the heart during non-rapid eye movement (NREM) sleep and an increased sympathetic activity during rapid eye movement (REM) sleep. Respiration undergoes important modifications as well, becoming deeper and more regular with deep sleep and shallower and more frequent during REM sleep. The aim of the present study is to assess both autonomic cardiac regulation and cardiopulmonary coupling variations during different sleep stages in healthy subjects, using spectral and cross-spectral analysis of the HRV and respiration signals. Polysomnographic sleep recordings were performed in 11 healthy women and the HRV signal and the respiration signal were obtained. The spectral and cross-spectral parameters of the HRV signal and of the respiration signal were computed at low frequency and at breathing frequency (high frequency, HF) during different sleep stages. Results attested a sympatho-vagal balance shift toward parasympathetic modulation during NREM sleep and toward sympathetic modulation during REM sleep. Spectral analysis of the HRV signal and of the respiration signal indicated a higher respiration regularity during deep sleep, and a higher parasympathetic drive was also confirmed by an increase in the coherence between the HRV and the respiration signal in the HF band during NREM sleep. Our findings about sleep stage-dependent variations in the HRV signal and in the respiratory activity are in line with previous evidences and confirm spectral analysis of the HRV and the respiration signal to be a suitable tool for investigating cardiac autonomic modulation and cardio-respiratory coupling during sleep.

Time delay correction of the synchrogram for optimized detection of cardiorespiratory coordination

The cardiorespiratory synchrogram, a graphical tool based on the stroboscopic technique, is an established method for evaluating phase-locking between cardiac and respiratory oscillators. In the original method, the phase of the respiratory oscillator is observed at the instants of time when the phase of the cardiac oscillator attains a certain value. In this article, we introduced an additional adaptive delay in the cardiac oscillator based on the maximisation of the cross-correlation or symbolic coupling traces between the phases of respiration and the delayed R-R intervals. We then investigated phase coordination in thirteen normal subjects (five males, eight females; age: 19-24 years) for different body postures. Cardiorespiratory coordination was observed to be significantly reduced in the upright position (supine vs. upright: 11.9 ± 5.1 vs. 6.9 ± 3.6, P < 0.05). Compared to the original algorithm we observed an increase in the detection of average cardiorespiratory coordination (supine original vs. delay: 11.9 vs. 18.9%), together with a decrease in standard deviation of the percentage of coordination in all the subjects, after introducing the heart rate delay (supine original vs. delay: 5.1 vs. 4.4%). In conclusion, the performance of the synchrogram technique was improved by including an adaptive delay in the cardiac oscillator.

Cardiorespiratory phase-coupling is reduced in patients with obstructive sleep apnea

Cardiac and respiratory rhythms reveal transient phases of phase-locking which were proposed to be an important aspect of cardiorespiratory interaction. The aim of this study was to quantify cardio-respiratory phase-locking in obstructive sleep apnea (OSA). We investigated overnight polysomnography data of 248 subjects with suspected OSA. Cardiorespiratory phase-coupling was computed from the R-R intervals of body surface ECG and respiratory rate, calculated from abdominal and thoracic sensors, using Hilbert transform. A significant reduction in phase-coupling was observed in patients with severe OSA compared to patients with no or mild OSA. Cardiorespiratory phase-coupling was also associated with sleep stages and was significantly reduced during rapid-eye-movement (REM) sleep compared to slow-wave (SW) sleep. There was, however, no effect of age and BMI on phase coupling. Our study suggests that the assessment of cardiorespiratory phase coupling may be used as an ECG based screening tool for determining the severity of OSA.

Impact of movement on cardiorespiratory coordination in conscious rats

In this study we assessed the impact of movement on the interaction between the heart rhythm and respiration in rats while they were conscious and freely moving. In eight male adult Sprague-Dawley (SD, n=4) and Hooded Wistar (HW, n=4) rats, we recorded respiratory rate using whole-body plethysmography with a piezoelectric sensor attached to simultaneously monitor body movement. Heart rate was recorded using a radio-telemetry transmitter. For the assessment of cardiorespiratory coordination, we analysed the phase-locking between heart rate and respiration, estimating the instantaneous phases using Hilbert transform. For statistical analysis, the piezoelectric signal was dichotomized into low-intensity (LIm) and high-intensity (HIm) movement. The R-R intervals, respiratory intervals and cardiorespiratory coordination between LIm and HIm of each rat were assessed with Student's t-test. A significant decrease in the mean values for respiratory interval (0.34 ± 0.1 vs. 0.23 ± 0.1 s, p < 0.01 in HW rats) and R-R interval (0.19 ± 0.01 vs. 0.17 ± 0.01 s, p < 0.001 in SD rats) was observed during HIm. The phase-locking between the cardiac and respiratory signals also decreased significantly during HIm (overall coordination during LIm vs. HIm: 89.3 ± 3.3% vs. 8.7 ± 1.7%, p < 0.001). In conclusion the interaction between the cardiac and respiratory oscillators is affected by voluntary movements in rats.

Effects of mental tasks on the cardiorespiratory synchronization

The cardiovascular and respiratory systems are functionally related to each other, but it is unclear if the cerebral cortex can affect their interaction. The effect of a mental task on the synchronization between cardiovascular and respiratory systems was investigated in the article. Electroencephalogram (EEG), electrocardiogram (ECG) and respiratory signal (RES) were collected from 29 healthy male subjects during the mental arithmetic (MA) task and the synchrogram was used to estimate the strength of cardiorespiratory synchronization. Our results showed that MA task significantly increased the breath rate, the heart rate and the EEG power spectral energy in theta band at FC3, FC4 and C4 electrodes (p<0.01), decreased the duration of cardiorespiratory synchronization epochs (p<0.05). Moreover the duration of cardiorespiratory synchronization epochs during MA task was negatively correlated with the EEG power spectral energy in theta band at FC3, FC4 and C4 electrodes and the sympathetic activity (p<0.05). The results demonstrated that ANS and cerebral cortex are implicated in the changes of cardiorespiratory synchronization during MA task.

Testing foetal-maternal heart rate synchronization via model-based analyses

The investigation of foetal reaction to internal and external conditions and stimuli is an important tool in the characterization of the developing neural integration of the foetus. An interesting example of this is the study of the interrelationship between the foetal and the maternal heart rate. Recent studies have shown a certain likelihood of occasional heart rate synchronization between mother and foetus. In the case of respiratory-induced heart rate changes, the comparison with maternal surrogates suggests that the evidence for detected synchronization is largely statistical and does not result from physiological interaction. Rather, they simply reflect a stochastic, temporary stability of two independent oscillators with time-variant frequencies. We reanalysed three datasets from that study for a more local consideration. Epochs of assumed synchronization associated with short-term regulation of the foetal heart rate were selected and compared with synchronization resulting from white noise instead of the foetal signal. Using data-driven modelling analysis, it was possible to identify the consistent influence of the heartbeat duration of maternal beats preceding the foetal beats during epochs of synchronization. These maternal beats occurred approximately one maternal respiratory cycle prior to the affected foetal beat. A similar effect could not be found in the epochs without synchronization. Simulations based on the fitted models led to a higher likelihood of synchronization in the data segments with assumed foetal-maternal interaction than in the segment without such assumed interaction. We conclude that the data-driven model-based analysis can be a useful tool for the identification of synchronization.

Day-to-night time differences in the relationship between cardiorespiratory coordination and heart rate variability

Heart rate variability (HRV) and cardiorespiratory coordination, i.e. the temporal interplay between oscillations of heartbeat and respiration, reflect information related to the cardiovascular and autonomic nervous system. The purpose of this study was to investigate the relationship between spectral measures of HRV and measures of cardiorespiratory coordination. In 127 subjects from a normal population a 24 h Holter ECG was recorded. Average heart rate (HR) and the following HRV parameters were calculated: very low (VLF), low (LF) and high frequency (HF) oscillations and LF/HF. Cardiorespiratory coordination was quantified using average respiratory rate (RespR), the ratio of heart rate and respiratory rate (HRR), the phase coordination ratio (PCR) and the extent of cardiorespiratory coordination (PP). Pearson's correlation coefficient r was used to quantify the relationship between each pair of the variables across all subjects. HR and HRR correlated strongest during daytime (r = 0.89). LF/HF and PP showed a negative correlation to a reasonable degree (r = -0.69). During nighttime sleep these correlations decreased whereas the correlation between HRR and RespR (r = -0.47) as well as between HRR and PCR (r = 0.73) increased substantially. In conclusion, HRR and PCR deliver considerably different information compared to HRV measures whereas PP is partially linked reciprocally to LF/HF.

Cardiovascular and respiratory dynamics during normal and pathological sleep

Sleep is an active and regulated process with restorative functions for physical and mental conditions. Based on recordings of brain waves and the analysis of characteristic patterns and waveforms it is possible to distinguish wakefulness and five sleep stages. Sleep and the sleep stages modulate autonomous nervous system functions such as body temperature, respiration, blood pressure, and heart rate. These functions consist of a sympathetic tone usually related to activation and to parasympathetic (or vagal) tone usually related to inhibition. Methods of statistical physics are used to analyze heart rate and respiration to detect changes of the autonomous nervous system during sleep. Detrended fluctuation analysis and synchronization analysis and their applications to heart rate and respiration during sleep in healthy subjects and patients with sleep disorders are presented. The observed changes can be used to distinguish sleep stages in healthy subjects as well as to differentiate normal and disturbed sleep on the basis of heart rate and respiration recordings without direct recording of brain waves. Of special interest are the cardiovascular consequences of disturbed sleep because they present a risk factor for cardiovascular disorders such as arterial hypertension, cardiac ischemia, sudden cardiac death, and stroke. New derived variables can help to find indicators for these health risks.