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

Sleep-stage dependence and co-existence of cardio-respiratory coordination and phase synchronization

Interactions between the cardiac and respiratory systems play a pivotal role in physiological functioning. Nonetheless, the intricacies of cardio-respiratory couplings, such as cardio-respiratory phase synchronization (CRPS) and cardio-respiratory coordination (CRC), remain elusive, and an automated algorithm for CRC detection is lacking. This paper introduces an automated CRC detection algorithm, which allowed us to conduct a comprehensive comparison of CRPS and CRC during sleep for the first time using an extensive database. We found that CRPS is more sensitive to sleep-stage transitions, and intriguingly, there is a negative correlation between the degree of CRPS and CRC when fluctuations in breathing frequency are high. This comparative analysis holds promise in assisting researchers in gaining deeper insights into the mechanics of and distinctions between these two physiological phenomena. Additionally, the automated algorithms we devised have the potential to offer valuable insights into the clinical applications of CRC and CRPS.

Novel metrics of sleep-disordered breathing are associated with outcome after ischemic stroke

OBJECTIVE/BACKGROUND

Standard measures of sleep-disordered breathing (SDB) that rely on count data may not sufficiently capture SDB severity or reflect downstream consequences of SDB. We hypothesized that novel metrics derived from pulse rate, oxygen saturation, and nasal pressure would be associated with stroke outcomes.

PATIENTS/METHODS

Shortly after ischemic stroke, participants in a population-based study were offered ApneaLink Plus testing. Signal analysis was used to generate 166 metrics from the nasal pressure cannula and finger probe, categorized as: autonomic (based on pulse rate variability), oximetry-derived, nasal pressure-derived, and mixed oxygen and nasal pressure-derived measures. Three-month outcome assessments included functional and cognitive outcomes and stroke recurrence. Tobit regression and Cox proportional hazards models were used to examine associations between each sleep apnea metric and the three outcomes, unadjusted and adjusted for multiple potential confounders. Models were adjusted for multiple comparisons.

RESULTS

Of the 530 participants, the median age was 65 (IQR: 57, 73), 49 % were female, and 64 % were Mexican American. Without covariate adjustment, 23 of 166 variables were associated with functional outcome, 43 were associated with cognitive outcome, and 1 was associated with stroke recurrence. After adjustment, 7 mixed, oximetry, or nasal pressure-based metrics and 1 autonomic metric were associated with functional outcome, but none was associated with cognitive outcome or stroke recurrence.

CONCLUSIONS

Many novel metrics of SDB were associated with important stroke outcomes, and 8 novel metrics were associated with functional outcome in adjusted models. This raises hypotheses about pathways by which SDB may negatively impact stroke outcomes.

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders

Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19-82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea-hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland-Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33-0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.

Effect of depression on phase coherence between respiratory sinus arrhythmia and respiration during sleep in patients with obstructive sleep apnea

Introduction: A high prevalence of major depressive disorder (MDD) among Obstructive Sleep Apnea (OSA) patients has been observed in both community and clinical populations. Due to the overlapping symptoms between both disorders, depression is usually misdiagnosed when correlated with OSA. Phase coherence between respiratory sinus arrhythmia (RSA) and respiration (λ RSA-RESP) has been proposed as an alternative measure for assessing vagal activity. Therefore, this study aims to investigate if there is any difference in λ RSA-RESP in OSA patients with and without MDD. Methods: Electrocardiograms (ECG) and breathing signals using overnight polysomnography were collected from 40 OSA subjects with MDD (OSAD+), 40 OSA subjects without MDD (OSAD-), and 38 control subjects (Controls) without MDD and OSA. The interbeat intervals (RRI) and respiratory movement were extracted from 5-min segments of ECG signals with a single apneic event during non-rapid eye movement (NREM) [353 segments] and rapid eye movement (REM) sleep stages [298 segments]. RR intervals (RRI) and respiration were resampled at 10 Hz, and the band passed filtered (0.10-0.4 Hz) before the Hilbert transform was used to extract instantaneous phases of the RSA and respiration. Subsequently, the λ RSA-RESP between RSA and Respiration and Heart Rate Variability (HRV) features were computed. Results: Our results showed that λ RSA-RESP was significantly increased in the OSAD+ group compared to OSAD- group during NREM and REM sleep. This increase was accompanied by a decrease in the low frequency (LF) component of HRV. Discussion: We report that the phase synchronization index between RSA and respiratory movement could provide a useful measure for evaluating depression in OSA patients. Our findings suggest that depression has lowered sympathetic activity when accompanied by OSA, allowing for stronger synchronization between RSA and respiration.

Age-dependent cardiorespiratory directional coupling in wake-resting state

Objective.Cooperation in the cardiorespiratory system helps maintain internal stability. Various types of system interactions have been investigated; however, the characteristics of the interactions have mostly been studied using data collected in well-defined physiological states, such as sleep. Furthermore, most analyses provided general information about the interaction, making it difficult to quantify how the systems influenced one another.Approach.Cardiorespiratory directional coupling was investigated in different age groups (20 young and 19 elderly subjects) in a wake-resting state. The directionality index (DI) was calculated using instantaneous phases from the heartbeat interval and respiratory signal to provide information about the strength and direction of interaction between the systems. Statistical analysis was performed between the groups on the DI and independent measures of directionality (nc_r: influence from cardiac system to respiratory system, and nc_c: influence from the respiratory system to the cardiac system).Main results.The values of DI were -0.52 and -0.17 in the young and elderly groups, respectively (p< 0.001). Furthermore, the values of nc_rand nc_cwere found to be significantly different between the groups (p< 0.001), respectively.Significance.Changes in both directions between the systems influence different aspects of cardiorespiratory coupling between the groups. This observation could be linked to different levels of autonomic modulation associated with ageing. Our approach could aid in quantitatively tracking and comprehending how systems interact in response to physiological and environmental changes. It could also be used to understand how abnormal interaction characteristics influence physiological system dysfunctions and disorders.

Central apnea detection in premature infants using machine learning

BACKGROUND AND OBJECTIVE

Apnea of prematurity is one of the most common diagnosis in neonatal intensive care units. Apneas can be classified as central, obstructive or mixed. According to the current international standards, minimal fluctuations or absence of fluctuations in the chest impedance (CI) suggest a central apnea (CA). However, automatic detection of reduced CI fluctuations leads to a high number of central apnea-suspected events (CASEs), the majority being false alarms. We aim to improve automatic detection of CAs by using machine learning to optimize detection of CAs among CASEs.

METHODS

Using an optimized algorithm for automated detection, all CASEs were detected in a population of 10 premature infants developing late-onset sepsis and 10 age-matched control patients. CASEs were inspected by two clinical experts and annotated as CAs or rejections in two rounds of annotations. A total of 47 features were extracted from the ECG, CI and oxygen saturation signals considering four 30 s-long moving windows, from 30 s before to 15 s after the onset of each CASE, using a moving step size of 5 s. Consecutively, new CA detection models were developed based on logistic regression with elastic net penalty, random forest and support vector machines. Performance was evaluated using both leave-one-patient-out and 10-fold cross-validation considering the mean area under the receiver-operating-characteristic curve (AUROC).

RESULTS

The CA detection model based on logistic regression with elastic net penalty returned the highest mean AUROC when features extracted from all four time windows were included, both using leave-one-patient-out and 10-fold cross-validation (mean AUROC of 0.88 and 0.90, respectively). Feature relevance was found to be the highest for features derived from the CI. A threshold for the false positive rate in the mean receiver-operating-characteristic curve equal to 0.3 led to a high percentage of correct detections for all CAs (78.2%) and even higher for CAs followed by a bradycardia (93.4%) and CAs followed by both a bradycardia and a desaturation (95.2%), which are more critical for the well-being of premature infants.

CONCLUSIONS

Models based on machine learning can lead to improved CA detection with fewer false alarms.

Triboelectric Patch Based on Maxwell Displacement Current for Human Energy Harvesting and Eye Movement Monitoring

The forthcoming wearable health care devices garner considerable attention because of their potential for monitoring, treatment, and protection applications. Herein, a self-powered triboelectric patch was developed using polytetrafluoroethylene rubbed with nylon fabric. The triboelectric patch can maintain a stable electrostatic field, due to the excess electrification on the surface of the triboelectric layer. The designed triboelectric nanogenerator (TENG) output watt density can reach about 485 mW/m² with added resistance of 11 kΩ. Additionally, the performance of the triboelectric patch allowed eye movement monitoring. The maximum voltage could reach 80 V at the vertical distance of 20 mm between the frictional layer and collector. The triboelectric patch not only can power a digital watch for potential wearable applications but also can be integrated to monitor eye movements during sleep. This work proposed a mechanism for human movement energy harvesting, which may be used for self-powered smart wearable health equipment and Maxwell displacement current wireless sensors.

Cardiorespiratory coupling strength in athletes and non-athletes

PURPOSE

Despite the relevant presence of nonlinear components on heart period (HP) likely due to cardiorespiratory coupling (CRC), the HP is frequently analyzed in absence of concomitant recordings of respiratory movements (RESP). This study aims to assess the cardiovascular dynamics and CRC during postural challenge in athletes and non-athletes via joint symbolic analysis (JSA).

METHODS

A cross-sectional study was conducted in 50 men, aged between 20 and 40 yrs, divided into athletes (n = 25) and non-athletes (n = 25) groups. The electrocardiogram, blood pressure and RESP signals were recorded during 15 min in both supine position (REST) and after active postural maneuver (STAND). From the beat-to-beat series of HP, systolic arterial pressure (SAP) and RESP, we computed the time and frequency domain indexes and baroreflex sensitivity. The JSA was based on the definition of symbolic HP and RESP patterns and on the evaluation of the rate of their simultaneous occurrence in both HP and RESP series.

RESULTS

The JSA analysis was able to identify higher CRC strength at REST in athletes. Moreover, the response of CRC to STAND depended on the time scales of the analysis and was much more evident in athletes than in non-athletes, thus indicating a more reactive autonomic control in athletes.

CONCLUSION

Assessing CRC in athletes via JSA provides additional information compared to standard linear time and frequency domain tools likely due to the more relevant presence of nonlinearities in HP-RESP variability relationship.

Effects of exercise anticipation on cardiorespiratory coherence

In this study, we explored the role of feedforward mechanisms in triggering cardiorespiratory adjustments before the onset of exercise. To isolate the feedforward aspects, we examined the effect of exercise anticipation on cardiorespiratory coherence. Twenty-nine healthy males (age = 18.8 [0.96] years) were subjected to bicycle (BE) and handgrip exercise (H) at two different intensities, viz., low and high. Bicycle exercise was performed in a unilateral (left- and right-sided) or bilateral mode, whereas handgrip was performed only in a unilateral mode. Single-lead ECG and respiratory rhythm, measured in the 5 min of anticipation phase before the onset of exercise, were used for analysis. Coherence was computed between ECG-derived instantaneous heart rate and respiratory signal. Average coherence in the high-frequency band (0.15-0.4 Hz) was used to estimate respiratory sinus arrhythmia (RSA). We found that coherence decreased with the anticipation of exercise relative to baseline (baseline = 0.54 [0.16], BE = 0.41 [0.12], H = 0.39 [0.12], p < 0.001). The decrease was greater for high intensity exercise (low = 0.42 [0.11], high = 0.37 [0.1], p < 0.001). The fall of coherence with intensity was stronger for bicycle exercise (BE: low = 0.44 [0.12], high = 0.37 [0.12], H: low = 0.4 [0.12], high = 0.37 [0.12], p = 0.00433). The expectation of bilateral exercise resulted in lower coherence compared to unilateral exercise (right-sided = 0.45 [0.16], left-sided = 0.4 [0.16], bilateral = 0.36 [0.15], unilateral vs. bilateral: p < 0.001), and the left-sided exercise had lower coherence compared to that of the right (left-sided vs. right-sided: p = 0.00925). Handgrip exercise showed similar trend (right-sided = 0.4 [0.15], left-sided = 0.37 [0.14], p = 0.0056). In conclusion, feedforward RSA adjustments in anticipation of exercise covaried with subsequent exercise-related features like intensity, muscle mass (unilateral vs. bilateral), and the exercise side (left vs. right). The left versus the right difference in coherence indicates autonomic asymmetry. Feedforward changes in RSA are like those seen during actual exercise and might facilitate the rapid phase transition between rest and exercise.

Response of heart rate variability and cardiorespiratory phase synchronization to routine bronchodilator test in patients with asthma

Heart rate variability (HRV) and cardiorespiratory phase synchronization (CRPS) were employed to study the cardio- and respiratory interactions in patients with asthma receiving inhalation of beta2-agonist (Berotec 200 mcg) for routine bronchodilator test. Both time- and frequency-domain parameters were used to analyze the HRV. A weighted G-index was introduced to study the quality of the CRPS. The HRV parameters, in both the time and frequency domains, exhibited significant changes pointing to a sympathetic activation of the autonomic balance immediately after the inhalation. On the other hand, the CRPS index barely changed throughout the entire process. This indicates that inhalation of beta2-agonist does not alter the CRPS appreciably, and that the CRPS, in contrast to HRV, is relatively stable in response to the inhalation of beta2-agonist in patients with asthma.

Weak closed-loop vibrational stimulation improves the depth of slow-wave sleep and declarative memory consolidation

Sleep is a unique behavioral state that affects body functions and memory. Although previous studies suggested stimulation methods to enhance sleep, a new method is required that is practical for long-term and unconstrained use by people. In this study, we used a novel closed-loop vibration stimulation method that delivers a stimulus in interaction with the intrinsic heart rhythm and examined the effects of stimulation on sleep and memory. Twelve volunteers participated in the experiment and each underwent one adaptation night and two experimental conditions-a stimulation condition (STIM) and a no-stimulation condition (SHAM). The heart rate variability analysis showed a significant increase in the normalized high frequency and the normalized low frequency significantly decreased under the STIM during the slow-wave sleep (SWS) stage. Furthermore, the synchronization ratio between the heartbeat and the stimulus significantly increased under the STIM in the SWS stage. From the electroencephalogram (EEG) spectral analysis, EEG relative powers of slow-wave activity and theta frequency bands showed a significant increase during the STIM in the SWS stage. Additionally, memory retention significantly increased under the STIM compared to the SHAM. These findings suggest that the closed-loop stimulation improves the SWS-stage depth and memory retention, and further provides a new technique for sleep enhancement.

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.

Effects of bilateral lung transplantation on cardiac autonomic modulation and cardiorespiratory coupling: a prospective study

Background

Although cardiac autonomic modulation has been studied in several respiratory diseases, the evidence is limited on lung transplantation, particularly on its acute and chronic effects. Thus, we aimed to evaluate cardiac autonomic modulation before and after bilateral lung transplantation (BLT) through a prospective study on patients enrolled while awaiting transplant.

Methods

Twenty-two patients on the waiting list for lung transplantation (11 women, age 33 [24–51] years) were enrolled in a prospective study at Ospedale Maggiore Policlinico Hospital in Milan, Italy. To evaluate cardiac autonomic modulation, ten minutes ECG and respiration were recorded at different time points before (T0) and 15 days (T1) and 6 months (T2) after bilateral lung transplantation. As to the analysis of cardiac autonomic modulation, heart rate variability (HRV) was assessed using spectral and symbolic analysis. Entropy-derived measures were used to evaluate complexity of cardiac autonomic modulation. Comparisons of autonomic indices at different time points were performed.

Results

BLT reduced HRV total power, HRV complexity and vagal modulation, while it increased sympathetic modulation in the acute phase (T1) compared to baseline (T0). The HRV alterations remained stable after 6 months (T2).

Conclusion

BLT reduced global variability and complexity of cardiac autonomic modulation in acute phases, and these alterations remain stable after 6 months from surgery. After BLT, a sympathetic predominance and a vagal withdrawal could be a characteristic autonomic pattern in this population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01752-6.

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.

Cardiorespiratory coupling is associated with exercise capacity in patients with chronic obstructive pulmonary disease

Background

The interaction between the pulmonary function and cardiovascular mechanics is a crucial issue, particularly when treating patients with chronic obstructive pulmonary disease (COPD). Synchrogram index is a new parameter that can quantify this interaction and has the potential to apply in COPD patients. Our objective in this study was to characterize cardiorespiratory interactions in terms of cardiorespiratory coupling (CRC) using the synchrogram index of the heart rate and respiratory flow signals in patients with chronic obstructive pulmonary disease.

Methods

This is a cross-sectional and preliminary data from a prospective study, which examines 55 COPD patients. K-means clustering analysis was applied to cluster COPD patients based on the synchrogram index. Linear regression and multivariable regression analysis were used to determine the correlation between the synchrogram index and the exercise capacity assessed by a six-minute walking test (6MWT).

Results

The 55 COPD patients were separated into a synchronized group (median 0.89 (0.64–0.97), n = 43) and a desynchronized group (median 0.23 (0.02–0.51), n = 12) based on K-means clustering analysis. Synchrogram index was correlated significantly with six minutes walking distance (r = 0.42, p = 0.001) and distance saturation product (r = 0.41, p = 0.001) assessed by 6MWT, and still was an independent variable by multivariable regression analysis.

Conclusion

This is the first result studying the heart–lung interaction in terms of cardiorespiratory coupling in COPD patients by the synchrogram index, and COPD patients are clustered into synchronized and desynchronized groups. Cardiorespiratory coupling is associated with exercise capacity in patients with COPD.

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co-morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information-rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co-morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

Human Heart Rhythms Synchronize While Co-sleeping

Human physiological systems have a major role in maintenance of internal stability. Previous studies have found that these systems are regulated by various types of interactions associated with physiological homeostasis. However, whether there is any interaction between these systems in different individuals is not well-understood. The aim of this research was to determine whether or not there is any interaction between the physiological systems of independent individuals in an environment where they are connected with one another. We investigated the heart rhythms of co-sleeping individuals and found evidence that in co-sleepers, not only do independent heart rhythms appear in the same relative phase for prolonged periods, but also that their occurrence has a bidirectional causal relationship. Under controlled experimental conditions, this finding may be attributed to weak cardiac vibration delivered from one individual to the other via a mechanical bed connection. Our experimental approach could help in understanding how sharing behaviors or social relationships between individuals are associated with interactions of physiological systems.

Variation of Electrocardiogram Features Across Sleep Stages in Healthy Controls and in Patients with Sleep Apnea Hypopnea Syndrome

Sleep apnea hypopnea syndrome (SAHS) is an independent risk factor for various cardiovascular diseases. Electrocardiogram (ECG) features such as the RR, PR, QT, QTc, Tpe intervals and the Tpe/QT, Tpe/QTc ratios are used to predict and study cardiovascular diseases. It is not clear whether regular patterns of PR and Tpe-related features across sleep stages exist in SAHSs or healthy controls nor whether sleep stages affect the short- and long-range influences of respiratory events on ECG indices. We enrolled 36 healthy controls and 35 patients with SAHS in our study and analyzed the abovementioned ECG features. In the healthy controls, a significant regularity existed in these indices across sleep stages, which were weakened or disturbed in the patient group, especially the Tpe-related features. The differences between the patients and healthy controls were generally consistent across all sleep stages: patients had smaller RR, PR, QT and Tpe/QTc values, but larger QTc, Tpe and Tpe/QT values. After filtering the short-range influence of respiratory events, the differences in most features remained highly significant, except the QT interval. In the patient group, respiratory events decreased RR and PR intervals in most sleep stages and increased the Tpe-related features' values in deep sleep stages. These results may aid in the study of the relationships among SAHS, sleep disorders and cardiovascular diseases.

Feature relevance in physiological networks for classification of obstructive sleep apnea

OBJECTIVE

Physiological networks (PN) model couplings between organs in a high-dimensional parameter space. Machine learning methods, in particular artifical neural networks (ANNs), are powerful on high-dimensional classification tasks. However, lack of interpretability of the resulting models has been a drawback in research. We assess relevant PN topology changes in obstructive sleep apnea (OSA) by novel ANN interpretation techniques.

APPROACH

ANNs are trained to classify OSA based on the PNs of 48 patients and 48 age and gender matched healthy controls. The PNs consisting of 2812 links are derived from overnight biosignal recordings. The interpretation technique DeepLift is applied to the resulting ANN models, enabling the determination of the relevant features for classification decisions on individual subjects. The mean relevance scores of the features are compared to other machine learning methods (decision tree and random forests) and statistical tests on group differences.

MAIN RESULTS

The ANN interpretation results show good agreement with the compared methods and 87% of the samples could be correctly classified. OSA patients show a significantly higher coupling (p [Formula: see text] 0.001) in light sleep (N2) between breathing rate and EEG [Formula: see text] power in all electrode locations and to chin and leg muscular tone. In deep sleep (N3), OSA leads to significantly lower coupling (p [Formula: see text] 0.01) in lateral connections of EEG [Formula: see text] and [Formula: see text] power in central and frontal positions. Misclassified OSA patients had all mild/moderate AHIs and did not show PN topology changes. Both nights of these patients have been consistently misclassified as healthy. This may indicate, that the impact of respiratory events differs in subjects, thus forming different phenotypes.

SIGNIFICANCE

The proposed PN analysis provides a powerful and robust method to quantify a broad range of physiological interactions. Interpretability of the ANN make them a promising tool to identify new diagnostic markers in data-driven approaches.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

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.

Cardioventilatory Control in Preterm-born Children and the Risk of Obstructive Sleep Apnea

RATIONALE

The contribution of ventilatory control to the pathogenesis of obstructive sleep apnea (OSA) in preterm-born children is unknown.

OBJECTIVES

To characterize phenotypes of ventilatory control that are associated with the presence of OSA in preterm-born children during early childhood.

METHODS

Preterm- and term-born children without comorbid conditions were enrolled. They were categorized into an OSA group and a non-OSA group on the basis of polysomnography.

MEASUREMENTS AND MAIN RESULTS

Loop gain, controller gain, and plant gain, reflecting ventilatory instability, chemoreceptor sensitivity, and blood gas response to a change in ventilation, respectively, were estimated from spontaneous sighs identified during polysomnography. Cardiorespiratory coupling, a measure of brainstem maturation, was estimated by measuring the interval between inspiration and the preceding electrocardiogram R-wave. Cluster analysis was performed to develop phenotypes based on controller gain, plant gain, cardiorespiratory coupling, and gestational age. The study included 92 children, 63 of whom were born preterm (41% OSA) and 29 of whom were born at term (48% OSA). Three phenotypes of ventilatory control were derived with risks for OSA being 8%, 47%, and 77% in clusters 1, 2, and 3, respectively. There was a stepwise decrease in controller gain and an increase in plant gain from clusters 1 to 3. Children in cluster 1 had significantly higher cardiorespiratory coupling and gestational age than clusters 2 and 3. No difference in loop gain was found between clusters.

CONCLUSIONS

The risk for OSA could be stratified according to controller gain, plant gain, cardiorespiratory coupling, and gestational age. These findings could guide personalized care for children at risk for OSA.

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.

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.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

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.

Development of Analytical Approach for an Automated Analysis of Continuous Long-Term Single Lead ECG for Diagnosis of Paroxysmal Atrioventricular Block

Reliable detection of significant ECG features such as the P-wave, QRS-complex and T-wave are of major clinical importance. In this paper we introduce a new algorithm based on synchrosqueezing wavelet transform for detection of P-waves in long-term ECG recordings. Synchrosqueezing is a powerful time-frequency analysis tool that provides precise frequency representation of a multicomponent signal through mode decomposition. First, we analyzed four wavelet filters with different filter parameters, to identify the best specification for quantification of QRS and P-wave. Second, the algorithm was tested on ECG recording comprising of events with paroxysmal atrioventricular block and validated through visual scanning. Using morlet wavelet with a peak frequency of 5Hz and separation of 0.1Hz, our proposed algorithm was able to detect 95.5% of P-waves. From this study, it appears that synchrosqueezing wavelet transform may provide a powerful robust technique for automated ECG analysis.

Prelimbic prefrontal cortex mediates respiratory responses to mild and potent prolonged, but not brief, stressors

The prefrontal cortex is one of the key areas of the central mechanism of cardiovascular and respiratory control. Disinhibition of the prelimbic medial prefrontal cortex elicits tachypnoeic responses in anesthetized rats (Hassan et al., J. Physiol. 591: 6069-6088, 2013). The current study examines the effects of inhibition of the prelimbic prefrontal cortex during presentation of stressors of various lengths and intensities in conscious unrestrained rats. 8 Wistar rats were implanted with bilateral guide cannulas targeting the prelimbic prefrontal cortex and received microinjections of either saline of GABAA agonist muscimol prior to recording sessions. Inhibition of the prelimbic prefrontal cortex significantly attenuated respiratory responses to a novel environment stress, 30s light stimulus and restraint stress. It did not affect respiratory responses to 500 ms acoustic stimuli of varying intensities (40-90 dB). We conclude that the prelimbic prefrontal cortex contributes to generation of tachypnoeic responses to prolonged stressors, but does not contribute to respiratory arousal in response to brief stressors.

Heart rate variability and cardiorespiratory coupling in obstructive sleep apnea: elderly compared with young

INTRODUCTION

Aging is known to be a major contributing factor to the increased risk of obstructive sleep apnea (OSA). With aging, breathing undergoes significant changes during sleep, increasing the prevalence of apnea events, which affects heart rate variability (HRV) and cardiorespiratory coupling (CRC).

OBJECTIVES

To compare HRV and CRC during wakefulness and sleep between young and elderly patients with and without OSA; and to determine whether the presence of OSA in young and elderly patients has a different impact on HRV and CRC during sleep.

METHODS

One hundred subjects, 50 young (mean age, 27 ± 9; 20 normal and 30 OSA) and 50 elderly (mean age, 65 ± 7; 20 normal and 30 OSA), underwent polysomnography. Spectral, cross-spectrum, and HRV parameters were analyzed during wakefulness and sleep.

RESULTS

The spectral analysis indicated that age affected HRV, with higher values of low frequency (P < 0.05) in elderly subjects during wakefulness and an interaction between the presence of OSA and age. OSA influenced HRV during sleep with lower LF/HF ratios during stage 2 (S2) and rapid eye movement (REM) sleep (P <0.05), with an interaction between the presence of OSA and age in REM sleep. Elderly patients had significantly lower percent tachogram power coherent with respiration (%TPCR) during wakefulness (P < 0.05), and OSA led to lower %TPCR during S2.

CONCLUSIONS

Age and OSA have an unfavorable impact on HRV, with reduced autonomic modulation during wakefulness, S2, and REM sleep. Age affects CRC during wakefulness and the presence of OSA affects CRC during sleep.

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.

Fine temporal structure of cardiorespiratory synchronization

Cardiac and respiratory rhythms are known to exhibit a modest degree of phase synchronization, which is affected by age, diseases, and other factors. We study the fine temporal structure of this synchrony in healthy young, healthy elderly, and elderly subjects with coronary artery disease. We employ novel time-series analysis to explore how phases of oscillations go in and out of the phase-locked state at each cycle of oscillations. For the first time we show that cardiorespiratory system is engaged in weakly synchronized dynamics with a very specific temporal pattern of synchrony: the oscillations go out of synchrony frequently, but return to the synchronous state very quickly (usually within just 1 cycle of oscillations). Properties of synchrony depended on the age and disease status. Healthy subjects exhibited more synchrony at the higher (1:4) frequency-locking ratio between respiratory and cardiac rhythms, whereas subjects with coronary artery disease exhibited relatively more 1:2 synchrony. However, multiple short desynchronization episodes prevailed regardless of the age and disease status. The same average synchrony level could be alternatively achieved with few long desynchronizations, but this was not observed in the data. This implies functional importance of short desynchronization dynamics. These dynamics suggest that a synchronous state is easy to create if needed but is also easy to break. Short desynchronization dynamics may facilitate the mutual coordination of cardiac and respiratory rhythms by creating intermittent synchronous episodes. It may be an efficient background dynamics to promote adaptation of cardiorespiratory coordination to various external and internal factors.

Effect of resistive inspiratory and expiratory loading on cardio-respiratory interaction in healthy subjects

Resistive loading affects the breathing pattern and causes an increase in negative intrathoracic pressure. The aim of this paper was to study the influence inspiratory and expiratory loading on cardio-respiratory interaction. We recorded electrocardiogram (ECG) and respiratory inductance plethysmogram (RIP) in 11 healthy male subjects under normal and resistive loading conditions. The R-R time series were extracted from the ECG and respiratory phases were calculated from the ribcage and abdominal RIP using the Hilbert transform. Both the series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases, respectively. Subsequently, words of length `3 digits' were formed and the correspondence between words of the two series was determined to quantify cardio-respiratory interaction. Adding inspiratory and expiratory resistive loads resulted in an increase in inspiratory and expiatory time, respectively. Furthermore, we observed a significant increase in cardio-respiratory interaction during inspiratory resistive loading as compared to expiratory resistive loading (ribcage: 22.1±7.2 vs. 12.5±4.3 %, p<;0.0001; abdomen: 18.8±8.5 vs. 12.1±3.1 %, p<;0.05, respectively). Further studies may aid in better understanding the underlying physiological mechanisms and management of patients with breathing disorders.

Cardiorespiratory coupling during sleep in difficult-to-control asthmatic patients

Heart rate variability (HRV) and respiration recorded during sleep from 8 patients suffering from difficult-to-control asthma were studied to investigate autonomic nervous system control of cardiac and respiratory activities, and of cardio-respiratory coupling during different sleep stages.

Effect of spontaneous arousals on cardio-respiratory interaction in healthy children

The aim of this paper was to study the effect of spontaneous arousals during night-time sleep on the interactions between R-R intervals and respiratory phases in healthy children. We collected overnight polysomnography data of 40 healthy children and investigated cardio-respiratory interaction before and after spontaneous arousals during stage 2 sleep using joint symbolic dynamics. The R-R time series were extracted from electrocardiograms (ECG) and respiratory phases were obtained from abdominal sensors using the Hilbert transform. Both the series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases, respectively. Subsequently, words of length '2' were formed and the correspondence between words of the two series was determined to quantify cardio-respiratory interaction for pre- and post-spontaneous arousal episodes. We observed a brief but significant shortening in R-R and respiratory intervals after arousal. There was also a significant short-term increase in cardio-respiratory interaction during the first 30-second post-arousal episode as compared to 30-second pre-arousal episode (7.5±3.4 vs. 5.2±3.7%, p&#60;0.0001, respectively). In conclusion, spontaneous arousals in healthy children during night-time sleep are associated with a temporal but significant increase in cardio-respiratory interaction.

Cardiorespiratory variability following repeat acute hypoxia in the conscious SHR versus two normotensive rat strains

A link between exaggerated chemoreceptor sensitivity and hypertension has been documented in the spontaneously hypertensive rat (SHR) but has also been questioned when comparisons with normotensive strains other than the Wistar Kyoto (WKY) rat are made. To further evaluate the link between hypertension and chemoreflex sensitivity, changes in cardiorespiratory variability in response to three successive bouts of 5 min of hypoxia (21%→10%) were evaluated in conscious male SHR, and WKY and Sprague Dawley (SD) rats (n=7-8/group). In response to the first bout of hypoxia, the change in respiratory frequency (RF) was greatest in the SHR, but the increase in mean arterial pressure (MAP) was similar in both SHRs and WKY rats and all strains demonstrated a similar rise in heart rate (HR). All strains showed some level of response accommodation during subsequent bouts of hypoxia. Spectral analysis of HR variability identified a significant difference in high frequency (HF) power between strains during hypoxia, including an increase in HF power in the WKY rats, a decrease in the SHRs and little overall change in the SD rats. Alternatively, all strains demonstrated a rise in systolic arterial pressure (SAP) variability in the low frequency (LF) range in response to hypoxia but the increase was greatest in the SHR. Since SAP LF power is linked to vasosympathetic tone, these results support the hypothesis that essential hypertension is linked to exaggerated sympathetic responses to chemoreceptor stimulation but confirm that estimation of augmented reflex function cannot be determined by quantifying simple changes in MAP or HR.

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.

Quantification of cardio-respiratory interactions in healthy children during night-time sleep using joint symbolic dynamics

The aim of this paper was to study interactions between R-R intervals and respiratory phases in healthy children during night-time sleep using a novel technique based on joint symbolic dynamics. We investigated overnight polysomnography data of 40 healthy children. The R-R time series were extracted from electrocardiograms (ECG) and respiratory phases were obtained from abdominal sensors using the Hilbert transform. Both the series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases, respectively. Subsequently, words of length '2' were formed and the correspondence between words of the two series for each sleep stage was determined to quantify cardio-respiratory interaction. We found a significantly higher percentage of similarity in the joint symbolic dynamics of R-R intervals and respiratory phases during slow-wave (SW) sleep compared to any other sleep stage. There was, however, no significant effect of age, gender or BMI on cardio-respiratory interaction. In conclusion, joint symbolic dynamics provides a novel efficient technique for the analysis of cardio-respiratory interaction.

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.

High-frequency and low-frequency chest compression: effects on lung water secretion, mucus transport, heart rate, and blood pressure using a trapezoidal source pressure waveform

High-frequency chest compression (HFCC), using an appropriate source (pump) waveform for frequencies at or above 3 Hz, can enhance pulmonary clearance for patients with cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). Using a trapezoidal HFCC source pressure waveform, secretion of water from epithelial tissue and transport of mucus through lung airways can be enhanced for patients with CF and COPD. At frequencies below 3 Hz, low-frequency chest compression (LFCC) appears to have a significant impact on the cardiovascular system. For a trapezoidal source pressure waveform at frequencies close to 1 Hz, LFCC produces amplitude or intensity variations in various components of the electrocardiogram time-domain waveform, produces changes at very low frequencies associated with the electrocardiogram frequency spectra (indicating enhanced parasympathetic nervous system activity), and promotes a form of heart rate synchronization. It appears that LFCC can also provide additional cardiovascular benefits by reducing peak and average systolic and diastolic blood pressure for patients with hypertension.

Impact of the vagal feedback on cardiorespiratory coupling in anesthetized rats

Cardiorespiratory coupling can be significantly influenced by both pontine and vagal modulation of medullary motor and premotor areas. We investigated influences of the pontine intertrigeminal region (ITR) and peripheral vagal pathways on the coupling between systolic blood pressure (SBP) and respiration in 9 anesthetized rats. Glutamate injection into the ITR perturbed both respiration and SBP and decreased SBP-respiratory coherence (0.95±0.01 vs 0.89±0.02; (p=0.01). Intravenous infusion of serotonin (5-HT) produced apnea and hypertension and also decreased SBP-respiratory coherence (0.95±0.01 vs 0.72±0.06; p=0.04). Bilateral vagotomy eliminated the cardiorespiratory coherence perturbations induced by central (glutamate injection into the ITR: 0.89±0.03 vs 0.86±0.03; p=0.63) and peripheral (5-HT infusion: 0.89±0.03 vs 0.88±0.02; p=0.98) pharmacologic manipulations. Glutamate stimulation of the ITR postvagotomy increased the relative spectral power density of SBP in the respiratory frequency range (0.25±0.08 vs 0.55±0.06; p=0.01). The data suggest that SBP-respiratory coupling is largely mediated within the central nervous system, with vagal systems acting in a way that disrupts coherence during transient cardiorespiratory disturbances. Although decreased cardiorespiratory coherence may increase cardiac work during perturbations, this may be physiologically advantageous in restoring homeostatic equilibrium of respiration and blood pressure.

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.