Cardiovascular and respiratory dynamics during normal and pathological sleep

Heart rate response to cortical arousals in patients with isolated obstructive sleep apnea and with comorbid insomnia (COMISA)

PURPOSE

Comorbid insomnia often occurs in patients with obstructive sleep apnea (OSA), referred to as COMISA. Cortical arousals manifest as a common feature in both OSA and insomnia, often accompanied by elevated heart rate (HR). Our objective was to evaluate the heart rate response to nocturnal cortical arousals in patients with COMISA and patients with OSA alone.

METHODS

We analyzed data from patients with COMISA and from patients with OSA matched for apnea-hypopnea index. Sleep staging and analysis of respiratory events and cortical arousals were performed using the Philips Somnolyzer automatic scoring system. Beat-by-beat HR was analyzed from the onset of the cortical arousal to 30 heartbeats afterwards. HR responses were divided into peak and recovery phases. Cortical arousals were separately evaluated according to subtype (related to respiratory events and spontaneous) and duration (3-6 s, 6-10 s, 10-15 s).

RESULTS

A total of 72 patients with COMISA and 72 patients with OSA were included in this study. There were no overall group differences in the number of cortical arousals with and without autonomic activation. No significant differences were found for spontaneous cortical arousals. The OSA group had more cortical arousals related to respiratory events (21.0 [14.8-30.0] vs 16.0 [9.0-27.0], p = 0.016). However, the COMISA group had longer cortical arousals (7.2 [6.4-7.8] vs 6.7 [6.2-7.7] s, p = 0.024) and the HR recovery phase was prolonged (52.5 [30.8-82.5] vs 40.0 [21.8-55.5] beats/min, p = 0.017). Both the peak and the recovery phase for longer cortical arousals with a duration of 10-15 s were significantly higher in patients with COMISA compared to patients with OSA (47.0 [27.0-97.5] vs 34.0 [21.0-71.0] beats/min, p = 0.032 and 87.0 [47.0-132.0] vs 71.0 [43.0-103.5] beats/min, p = 0.049, respectively).

CONCLUSIONS

The HR recovery phase after cortical arousals related to respiratory events is prolonged in patients with COMISA compared to patients with OSA alone. This response could be indicative of the insomnia component in COMISA.

A sleep stage estimation algorithm based on cardiorespiratory signals derived from a suprasternal pressure sensor

Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep-disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1-N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of -1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information-rich signal may enable new ways of clinical assessments, such as night-to-night variability in obstructive sleep apnea and other sleep disorders.

U-shaped association between sleep duration and the risk of respiratory diseases mortality: a large prospective cohort study from UK Biobank

STUDY OBJECTIVES

Our cohort study aimed to study the association between sleep duration and risk of mortality due to respiratory diseases.

METHODS

We included 498,200 participants from UK Biobank (2006-2021). We classified sleep duration as short sleep duration (< 7 hours), long sleep duration (> 9 hours for adults, > 8 hours for older adults), and midrange sleep duration (7-9 hours). We used the Cox proportional hazards model and restricted cubic spline analysis to explore the association between sleep duration and respiratory diseases mortality.

RESULTS

During a median follow-up of 12.49 years, 2,477 deaths due to respiratory diseases were recorded, of which 1,099 were deaths due to chronic lower respiratory diseases. Cox models with penalized splines showed U-shaped associations of sleep duration with mortality due to total respiratory diseases and chronic lower respiratory diseases. Compared with midrange sleep duration, short sleep duration was associated with 14% higher risk of total respiratory diseases mortality (hazard ratio = 1.14; 95% confidence interval: 1.04, 1.25), and long sleep duration was associated with 35% higher risk of total respiratory diseases mortality (hazard ratio = 1.35; 95% confidence interval: 1.19, 1.55), after adjustment of baseline characteristics, health status, and lifestyle habits. Similarly, the hazard ratios for chronic lower respiratory diseases mortality were 1.20 (95% confidence interval: 1.04, 1.38) and 1.44 (95% confidence interval: 1.19, 1.74), respectively.

CONCLUSIONS

There was a U-shaped association between sleep duration and respiratory diseases mortality. Appropriate sleep duration may improve the progress of respiratory diseases.

CITATION

Du M, Liu M, Liu J. U-shaped association between sleep duration and the risk of respiratory diseases mortality: a large prospective cohort study from UK Biobank. J Clin Sleep Med. 2023;19(11):1923-1932.

Sympathetic and Vagal Nerve Activity in COPD: Pathophysiology, Presumed Determinants and Underappreciated Therapeutic Potential

This article explains the comprehensive state of the art assessment of sympathetic (SNA) and vagal nerve activity recordings in humans and highlights the precise mechanisms mediating increased SNA and its corresponding presumed clinical determinants and therapeutic potential in the context of chronic obstructive pulmonary disease (COPD). It is known that patients with COPD exhibit increased muscle sympathetic nerve activity (MSNA), as measured directly using intraneural microelectrodes—the gold standard for evaluation of sympathetic outflow. However, the underlying physiological mechanisms responsible for the sympathoexcitation in COPD and its clinical relevance are less well understood. This may be related to the absence of a systematic approach to measure the increase in sympathetic activity and the lack of a comprehensive approach to assess the underlying mechanisms by which MSNA increases. The nature of sympathoexcitation can be dissected by distinguishing the heart rate increasing properties (heart rate and blood pressure variability) from the vasoconstrictive drive to the peripheral vasculature (measurement of catecholamines and MSNA) (Graphical Abstract Figure 1). Invasive assessment of MSNA to the point of single unit recordings with analysis of single postganglionic sympathetic firing, and hence SNA drive to the peripheral vasculature, is the gold standard for quantification of SNA in humans but is only available in a few centres worldwide because it is costly, time consuming and requires a high level of training. A broad picture of the underlying pathophysiological determinants of the increase in sympathetic outflow in COPD can only be determined if a combination of these tools are used. Various factors potentially determine SNA in COPD (Graphical Abstract Figure 1): Obstructive sleep apnoea (OSA) is highly prevalent in COPD, and leads to repeated bouts of upper airway obstructions with hypoxemia, causing repetitive arousals. This probably produces ongoing sympathoexcitation in the awake state, likely in the “blue bloater” phenotype, resulting in persistent vasoconstriction. Other variables likely describe a subset of COPD patients with increase of sympathetic drive to the heart, clinically likely in the “pink puffer” phenotype. Pharmacological treatment options of increased SNA in COPD could comprise beta blocker therapy. However, as opposed to systolic heart failure a similar beneficial effect of beta blocker therapy in COPD patients has not been shown. The point is made that although MSNA is undoubtedly increased in COPD (probably independently from concomitant cardiovascular disease), studies designed to determine clinical improvements during specific treatment will only be successful if they include adequate patient selection and translational state of the art assessment of SNA. This would ideally include intraneural recordings of MSNA and—as a future perspective—vagal nerve activity all of which should ideally be assessed both in the upright and in the supine position to also determine baroreflex function.

Cardiorespiratory Interaction and Autonomic Sleep Quality Improve during Sleep in Beds Made from Pinus cembra (Stone Pine) Solid Wood

Cardiorespiratory interactions (CRIs) reflect the mutual tuning of two important organismic oscillators—the heartbeat and respiration. These interactions can be used as a powerful tool to characterize the self-organizational and recreational quality of sleep. In this randomized, blinded and cross-over design study, we investigated CRIs in 15 subjects over a total of 253 nights who slept in beds made from different materials. One type of bed, used as control, was made of melamine faced chipboard with a wood-like appearance, while the other type was made of solid wood from stone pine (Pinus cembra). We observed a significant increase of vagal activity (measured by respiratory sinus arrhythmia), a decrease in the heart rate (as an indicator of energy consumption during sleep) and an improvement in CRIs, especially during the first hours of sleep in the stone pine beds as compared to the chipboard beds. Subjective assessments of study participants’ well-being in the morning and sub-scalar assessments of their intrapsychic stability were significantly better after they slept in the stone pine bed than after they slept in the chipboard bed. Our observations suggest that CRIs are sensitive to detectable differences in indoor settings that are relevant to human health. Our results are in agreement with those of other studies that have reported that exposure to volatile phytochemical ingredients of stone pine (α-pinene, limonene, bornyl acetate) lead to an improvement in vagal activity and studies that show a reduction in stress parameters upon contact with solid wood surfaces.

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 central apneas on sympathovagal balance and hemodynamics at night: impact of underlying systolic heart failure

BACKGROUND

Increased sympathetic drive is the key determinant of systolic heart failure progression, being associated with worse functional status, arrhythmias, and increased mortality. Central sleep apnea is highly prevalent in systolic heart failure, and its effects on sympathovagal balance (SVB) and hemodynamics might depend on relative phase duration and background pathophysiology.

OBJECTIVE

This study compared the effects of central apneas in patients with and without systolic heart failure on SVB and hemodynamics during sleep.

METHODS

During polysomnography, measures of SVB (heart rate and diastolic blood pressure variability) were non-invasively recorded and analyzed along with baroreceptor reflex sensitivity and hemodynamic parameters (stroke volume index, cardiac index, total peripheral resistance index). Data analysis focused on stable non-rapid eye movement N2 sleep, comparing normal breathing with central sleep apnea in subjects with and without systolic heart failure.

RESULTS

Ten patients were enrolled per group. In heart failure patients, central apneas had neutral effects on SVB (all p > 0.05 for the high, low, and very low frequency components of heart rate and diastolic blood pressure variability). Patients without heart failure showed an increase in very low and low frequency components of diastolic blood pressure variability in response to central apneas (63 ± 18 vs. 39 ± 9%; p = 0.001, 43 ± 12 vs. 31 ± 15%; p = 0.002). In all patients, central apneas had neutral hemodynamic effects when analyzed over a period of 10 min, but had significant acute hemodynamic effects.

CONCLUSION

Effects of central apneas on SVB during sleep depend on underlying systolic heart failure, with neutral effects in heart failure and increased sympathetic drive in idiopathic central apneas.

Adaptive servo-ventilation therapy does not favourably alter sympatho-vagal balance in sleeping patients with systolic heart failure and central apnoeas: Preliminary data

BACKGROUND

In contrast to continuous positive airway pressure (CPAP), the use of adaptive servo-ventilation (ASV) for treatment of central sleep apnoea (CSA) was associated with increased mortality in patients with chronic systolic heart failure (CHF). In order to characterize the interplay between sleep-disordered breathing, CHF and sympathovagal balance (SVB) this study investigated the effect of nocturnal CPAP and ASV on SVB in CSA patients with or without CHF.

METHODS

Thirty-seven patients with ongoing positive airway pressure therapy (CPAP or ASV) for CSA (17 patients with systolic CHF - left ventricular ejection fraction <50% - and 20 patients with CSA but no CHF) underwent evaluation of SVB (spectral analysis of heart rate -HRV- and diastolic blood pressure variability) during full nocturnal polysomnography. The night was randomly split into equal parts including no treatment (NT), automatic CPAP and ASV. Data analysis was restricted to stable N2 sleep.

RESULTS

In patients with CSA and systolic CHF, neither automatic CPAP nor ASV showed favourable effects on parameters reflecting SVB during N2 sleep (all p > 0.05). In contrast, in subjects with CSA without CHF automatic CPAP, but not ASV, favourably altered SVB by decreasing the low frequency and increasing the high frequency component of HRV (both p = 0.03).

CONCLUSIONS

Effects of various modes of positive airway pressure therapy of CSA on SVB during sleep depend on the mode of pressure support and underlying cardiac function. Automatic CPAP but not ASV favourably influences SVB in subjects without CHF, whereas both interventions leave SVB unchanged in patients with CHF.

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.

Risk detection in patients with obstructive sleep apnea syndrome based on cardiovascular time series analysis

Obstructive sleep apnea represents the most common form of sleep-disordered breathing and has a high prevalence in patients with cardiovascular disease. Disturbed sleep is a potential risk factor for cardiovascular disorders such as arterial hypertension, cardiac ischemia, sudden cardiac death, and stroke. In this study we investigated polysomnographic records and analyzed the ECG, plethysmogram, respiration and SpO2 time series during wakefulness (WK), NREM, and REM sleep stages applying variability and coupling analyses methods. We enrolled 55 patients with obstructive sleep apnea syndrome (OSAS) and 29 healthy control subjects (CON: 45.9±14.9 years, 21 male) in this study. OSAS patients were subdivided into a low- and high-risk group (LR: 50.8±14.1 years, n=29, 21 male; HR: 57.2±13.4 years, n=26, 19 male) based on the Apnea-Hypopnea Index (AHI) (CON: 0-5 AHI, LR: 5-15 AHI, HR: >15 AHI). We could demonstrate the presence of an altered autonomic function in OSAS patients, differing from healthy controls. This altered autonomic function was mainly based on heart rate-, respiratory-, SpO2- and plethysmogram variability and their couplings. The discriminant analysis showed that the optimal set consisting of two autonomic indices revealed a high classification power (ACC=86.7%, AUC=90.3%, SENS=89.5% and SPEC=84.6%) when comparing low-risk and high-risk OSAS patients during WK. These results were slightly improved when analyzing REM sleep stages. Based on these results it seems to be possible to perform optimal risk stratification for OSAS patients based on autonomic indices. Based on our findings it is possible to differentiate between high-risk OSAS patient and low-risk OSAS patient at an early stage and in a promising manner allowing to set up therapy strategies for those patients in an early stage.

Sleep – the yet underappreciated player in cardiovascular diseases: A clinical review from the German Cardiac Society Working Group on Sleep Disordered Breathing

Patients with a wide variety of cardiovascular diseases, including arterial and pulmonary hypertension, arrhythmia, coronary artery disease and heart failure, are more likely to report impaired sleep with reduced sleep duration and quality, and also, sometimes, sleep interruptions because of paroxysmal nocturnal dyspnoea or arrhythmias. Overall, objective short sleep and bad sleep quality (non-restorative sleep) and subjective long sleep duration are clearly associated with major cardiovascular diseases and fatal cardiovascular outcomes. Sleep apnoea, either obstructive or central in origin, represents the most prevalent, but only one, of many sleep-related disorders in cardiovascular patients. However, observations suggest a bidirectional relationship between sleep and cardiovascular diseases that may go beyond what can be explained based on concomitant sleep-related disorders as confounding factors. This makes sleep itself a modifiable treatment target. Therefore, this article reviews the available literature on the association of sleep with cardiovascular diseases, and discusses potential pathophysiological mechanisms. In addition, important limitations of the current assessment, quantification and interpretation of sleep in patients with cardiovascular disease, along with a discussion of suitable study designs to address future research questions and clinical implications are highlighted. There are only a few randomised controlled interventional outcome trials in this field, and some of the largest studies have failed to demonstrate improved survival with treatment (with worse outcomes in some cases). In contrast, some recent pilot studies have shown a benefit of treatment in selected patients with underlying cardiovascular diseases.

A review of automated sleep stage scoring based on physiological signals for the new millennia

BACKGROUND AND OBJECTIVE

Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.

METHODS

This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.

RESULTS

Our review shows that all of these signals contain information for sleep stage scoring.

CONCLUSIONS

The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

End-expiratory lung volume decreases during REM sleep despite continuous positive airway pressure

PURPOSE

Patients with obstructive sleep apnea (OSA) may experience apneas and hypopneas primarily during stage R (REM) sleep when end-expiratory lung volume (EELV) reaches its nadir. The purpose of this study was to determine if REM-related reductions in EELV persist in the presence of continuous positive airway pressure (CPAP) prescribed during non-stage REM (NREM) sleep.

METHODS

We prospectively recruited 17 subjects referred to the sleep laboratory for CPAP titration. CPAP was titrated per AASM protocol to control respiratory events. The change in EELV was measured using magnetometry.

RESULTS

Of the 17 subjects, 12 (71%) had moderate to severe OSA. Despite the application of CPAP, there was a significant reduction in EELV between NREM and REM sleep (- 105.9 ± 92.2 to - 325.0 ± 113.1 mL, respectively, p < 0.01). The change in EELV between non-stage R (NREM) and REM significantly correlated with overall apnea-hypopnea index (AHI) (r = 0.5, p = 0.04), the number of respiratory arousals during REM (r = 0.5, p = 0.04), and prescribed level of CPAP (r = 0.7, p < 0.01).

CONCLUSION

REM-related reductions in EELV are associated with worsening sleep disordered breathing and occur despite the presence of CPAP.

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.

On the difference of cardiorespiratory synchronisation and coordination

Cardiorespiratory phase synchronisation (CRS) is a type of cardiorespiratory coupling that manifests through a prediliction for heart beats to occur at specific points relative to the phase of the respiratory cycle. It has been under investigation for nearly 20 years, and while it seems to be mostly occurring in relaxed states such as deep sleep and anesthesia, no clear clinical implications have been established. Cardiorespiratory coordination (CRC) is a recent development in this field where the relationship between the respiratory onset and heart beat is analysed in the time domain and the possible relationship of each heart beat is considered for both the previous and the next respiratory onset. This ostensibly closely related effect must not only show relevant information content but also do so independent of CRS in order to be relevant for future studies. In this paper, we investigate CRC and its relation to CRS mainly using graphical and statistical methods on two exemplary datasets: measurements from a pregnant woman participating in a preeclampsia study and those from a man suffering from sleep apnea. We show fundamental differences between the results of both approaches and are able to show a formerly unknown dependency between the heart activity and respiratory rate, potentially indicating heartbeat-initiated inspiration. Despite their differences, methods developed for the quantification of CRS can be adapted to CRC. Completing the comparison is an investigation into the relationship between CRC and respiratory sinus arrhythmia. Similar to previous results for CRS, the two effects are found to be orthogonal, meaning that they can be observed independently or in conjunction.

Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions

Within the framework of 'Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

Variable ventilation from bench to bedside

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals

Introduction

Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder.

Materials and Methods

Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI.

Results

A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA.

Discussion

We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome.

Conclusions

Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology.

Trial Registration

ClinicalTrials.gov NCT01161381

A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG

GOAL

This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG.

METHODS

It uses two novel features derived from the ECG, and two well-known features in heart rate variability analysis, namely the standard deviation and the serial correlation coefficients of the RR interval time series. The first novel feature uses the principal components of the QRS complexes, and it describes changes in their morphology caused by an increased sympathetic activity during apnea. The second novel feature extracts the information shared between respiration and heart rate using orthogonal subspace projections. Respiratory information is derived from the ECG by means of three state-of-the-art algorithms, which are implemented and compared here. All features are used as input to a least-squares support vector machines classifier, using an RBF kernel. In total, 80 ECG recordings were included in the study.

RESULTS

Accuracies of about 85% are achieved on a minute-by-minute basis, for two independent datasets including both hypopneas and apneas together. Separation between apnea and normal recordings is achieved with 100% accuracy. In addition to apnea classification, the proposed methodology determines the contamination level of each ECG minute.

CONCLUSION

The performances achieved are comparable with those reported in the literature for fully automated algorithms.

SIGNIFICANCE

These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea. Moreover, the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection.

Sleep apnea-hypopnea quantification by cardiovascular data analysis

Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration . We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration , as well as the average variance , are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least . Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure.

Prostaglandin D(2) is crucial for seizure suppression and postictal sleep

Epilepsy is a neurological disorder with the occurrence of seizures, which are often accompanied by sleep. Prostaglandin (PG) D2 is produced by hematopoietic or lipocalin-type PGD synthase (H- or L-PGDS) and involved in the regulation of physiological sleep. Here, we show that H-PGDS, L/H-PGDS or DP1 receptor (DP1R) KO mice exhibited more intense pentylenetetrazole (PTZ)-induced seizures in terms of latency of seizure onset, duration of generalized tonic-clonic seizures, and number of seizure spikes. Seizures significantly increased the PGD2 content of the brain in wild-type mice. This PTZ-induced increase in PGD2 was attenuated in the brains of L- or H-PGDS KO and abolished in L/H-PGDS KO mice. Postictal non-rapid eye movement sleep was observed in the wild-type and H-PGDS or DP2R KO, but not in the L-, L/H-PGDS or DP1R KO, mice. These findings demonstrate that PGD2 produced by H-PGDS and acting on DP1R is essential for seizure suppression and that the L-PGDS/PGD2/DP1R system regulates sleep that follows seizures.

Sleep-wake detection based on respiratory signal acquired through a pressure bed sensor

This study proposes an automatic method for the sleep-wake staging in normal and pathologic sleep based only on respiratory effort acquired from a Pressure Bed Sensor (PBS). Motion and respiratory movements were obtained through a PBS and sleep-wake staging was evaluated from those time series. 20 all night polysomnographies, with annotations, used as gold standard and the time series coming from the PBS were used to develop and to evaluate the automatic wake-sleep staging. The database was built up by: 10 healthy subjects and 10 patients with severe sleep apnea. The agreement of the statistical measures between the automatic classification and the human scoring were: 83.59 ± 6.79 of sensitivity, 83.60 ± 15.13 of specificity and 81.91 ± 6.36 of accuracy. These results suggest that some important indexes, such as sleep efficiency, could be computed through a contactless technique.

Automated quantification of the synchrogram by recurrence plot analysis

Recently, the concept of phase synchronization of two weakly coupled oscillators has raised a great research interest and has been applied to characterize synchronization phenomenon in physiological data. Phase synchronization of cardiorespiratory coupling is often studied by a synchrogram analysis, a graphical tool investigating the relationship between instantaneous phases of two signals. Although several techniques have been proposed to automatically quantify the synchrogram, most of them require a preselection of a phase-locking ratio by trial and error. One technique does not require this information; however, it is based on the power spectrum of phase's distribution in the synchrogram, which is vulnerable to noise. This study aims to introduce a new technique to automatically quantify the synchrogram by studying its dynamic structure. Our technique exploits recurrence plot analysis, which is a well-established tool for characterizing recurring patterns and nonstationarities in experiments. We applied our technique to detect synchronization in simulated and measured infants' cardiorespiratory data. Our results suggest that the proposed technique is able to systematically detect synchronization in noisy and chaotic data without preselecting the phase-locking ratio. By embedding phase information of the synchrogram into phase space, the phase-locking ratio is automatically unveiled as the number of attractors.

Clustering of ventricular arrhythmic complexes in heart rhythm

We study the statistics of intervals τ between ventricular premature complexes (VPCs) in 24-h electrocardiogram records obtained from PhysioNet data source. We find that the long-term memory inherent in the heartbeat intervals leads to power laws in the probability density function P(τ) between VPCs for τ>6 s. As a consequence, the probability W(t,Δt) that at least one VPC will occur within the next time interval Δt, if the last VPC occurred t time units intervals ago, decays by a power law of t. Based on these results, we suggest a method to obtain a priori information about the occurrence of the next VPC, and how to predict it. We think that usage of this a priori information could be useful for the improvement of the algorithms in healthcare monitoring devices with alarm facilities.

Cardiovascular and respiratory dynamics in patients with sleep apnea

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. 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.

Symbolic dynamic analysis of relations between cardiac and breathing cycles in patients on weaning trials

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

Screening of patients with Obstructive Sleep Apnea Syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep

AIM

To classify patients with possible diagnosis of Obstructive Sleep Apnea Syndrome (OSAS) into groups according to the severity of the disease using a decision tree producing algorithm based on nonlinear analysis of 3 respiratory signals instead of the use of full polysomnography.

PATIENTS-METHODS

Eighty-six consecutive patients referred to the Sleep Unit of a Pulmonology Department underwent full polysomnography and their tests were manually scored. Three nonlinear indices (Largest Lyapunov Exponent-LLE, Detrended Fluctuation Analysis-DFA and Approximate Entropy-APEN) were extracted from two respiratory signals (nasal cannula flow-F and thoracic belt-T). The oxygen saturation signal (SpO(2)) was also selected. The above measurements provided data to the C4.5 algorithm using a data mining application.

RESULTS

Two decision trees were produced using linear and nonlinear data from 3 respiratory signals. The discrimination between normal subjects and sufferers from OSAS presented an accuracy of 84.9% and a recall of 90.3% using the variables age, sex, DFA from F and Time with SpO(2)<90% (T90). The classification of patients into severity groups had an accuracy of 74.2% and a recall of 81.1% using the variables APEN from F, DFA from F and T90.

CONCLUSION

It is possible to have reliable predictions of the severity of OSAS using linear and nonlinear indices from only two respiratory signals during sleep instead of performing full polysomnography. The proposed algorithm could be used for screening patients suspected to suffer from OSAS.

Respiratory and Mayer wave-related discharge patterns of raphé and pontine neurons change with vagotomy

Previous models have attributed changes in respiratory modulation of pontine neurons after vagotomy to a loss of pulmonary stretch receptor "gating" of an efference copy of inspiratory drive. Recently, our group confirmed that pontine neurons change firing patterns and become more respiratory modulated after vagotomy, although average peak and mean firing rates of the sample did not increase (Dick et al., J Physiol 586: 4265-4282, 2008). Because raphé neurons are also elements of the brain stem respiratory network, we tested the hypotheses that after vagotomy raphé neurons have increased respiratory modulation and that alterations in their firing patterns are similar to those seen for pontine neurons during withheld lung inflation. Raphé and pontine neurons were recorded simultaneously before and after vagotomy in decerebrated cats. Before vagotomy, 14% of 95 raphé neurons had increased activity during single respiratory cycles prolonged by withholding lung inflation; 13% exhibited decreased activity. After vagotomy, the average index of respiratory modulation (eta(2)) increased (0.05 +/- 0.10 to 0.12 +/- 0.18 SD; Student's paired t-test, P < 0.01). Time series and frequency domain analyses identified pontine and raphé neuron firing rate modulations with a 0.1-Hz rhythm coherent with blood pressure Mayer waves. These "Mayer wave-related oscillations" (MWROs) were coupled with central respiratory drive and became synchronized with the central respiratory rhythm after vagotomy (7 of 10 animals). Cross-correlation analysis identified functional connectivity in 52 of 360 pairs of neurons with MWROs. Collectively, the results suggest that a distributed network participates in the generation of MWROs and in the coordination of respiratory and vasomotor rhythms.

In the spotlight: biomedical signal processing

BSP is a well established discipline and constitutes the corner stone for the training of Biomedical Engineers both at undergraduate and at graduate levels. No basic curriculum on Biomedical Engineering could even exist without one or more BSP courses. All the advanced methods of signal processing (from traditional linear digital filtering up to non linear higher-order approaches) have been applied to BSP. The EEG signal, which is basically pseudo-stochastic, is a good example: the information contained in the original tracings, which are generally used for physiological or clinical interpretation, is hardly directly interpretable unless a more or less complicate pre-processing is done. This reasoning is obviously applicable, in different contexts, to other biomedical signals.

Stratification pattern of static and scale-invariant dynamic measures of heartbeat fluctuations across sleep stages in young and elderly

Cardiac dynamics exhibit complex variability characterized by scale-invariant and nonlinear temporal organization related to the mechanism of neuroautonomic control, which changes with physiologic states and pathologic conditions. Changes in sleep regulation during sleep stages are also related to fluctuations in autonomic nervous activity. However, the interaction between sleep regulation and cardiac autonomic control remains not well understood. Even less is known how this interaction changes with age, as aspects of both cardiac dynamics and sleep regulation differ in healthy elderly compared to young subjects. We hypothesize that because of the neuroautonomic responsiveness in young subjects, fractal and nonlinear features of cardiac dynamics exhibit a pronounced stratification pattern across sleep stages, while in elderly these features will remain unchanged due to age-related loss of cardiac variability and decline of neuroautonomic responsiveness. We analyze the variability and the temporal fractal organization of heartbeat fluctuations across sleep stages in both young and elderly. We find that independent linear and nonlinear measures of cardiac control consistently exhibit the same ordering in their values across sleep stages, forming a robust stratification pattern. Despite changes in sleep architecture and reduced heart rate variability in elderly subjects, this stratification surprisingly does not break down with advanced age. Moreover, the difference between sleep stages for some linear, fractal, and nonlinear measures exceeds the difference between young and elderly, suggesting that the effect of sleep regulation on cardiac dynamics is significantly stronger than the effect of healthy aging. Quantifying changes in this stratification pattern may provide insights into how alterations in sleep regulation contribute to increased cardiac risk.

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.

Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input

The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.

Introduction: Cardiovascular physics

The number of patients suffering from cardiovascular diseases increases unproportionally high with the increase of the human population and aging, leading to very high expenses in the public health system. Therefore, the challenge of cardiovascular physics is to develop high-sophisticated methods which are able to, on the one hand, supplement and replace expensive medical devices and, on the other hand, improve the medical diagnostics with decreasing the patient's risk. Cardiovascular physics-which interconnects medicine, physics, biology, engineering, and mathematics-is based on interdisciplinary collaboration of specialists from the above scientific fields and attempts to gain deeper insights into pathophysiology and treatment options. This paper summarizes advances in cardiovascular physics with emphasis on a workshop held in Bad Honnef, Germany, in May 2005. The meeting attracted an interdisciplinary audience and led to a number of papers covering the main research fields of cardiovascular physics, including data analysis, modeling, and medical application. The variety of problems addressed by this issue underlines the complexity of the cardiovascular system. It could be demonstrated in this Focus Issue, that data analyses and modeling methods from cardiovascular physics have the ability to lead to significant improvements in different medical fields. Consequently, this Focus Issue of Chaos is a status report that may invite all interested readers to join the community and find competent discussion and cooperation partners.