Cardiopulmonary coupling spectrogram as an ambulatory clinical biomarker of sleep stability and quality in health, sleep apnea, and insomnia

Age related disparities in sleep apnea diagnosis using a wearable device: Implications of 4% vs. 3% hypopnea scoring criteria

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) diagnosis relies on the Apnea-Hypopnea Index (AHI), with discrepancies arising from the 3% and 4% desaturation criteria. This study investigates age-related variations in OSA severity classification, utilizing data from 1201 adult patients undergoing Home Sleep Apnea Testing (HSAT) with SleepImage Ring@.

METHODS

The study employs Bland-Altman analysis to compare AHI values obtained with the 3% and 4% desaturation criteria. Age-stratified analysis explores discrepancies across different age groups.

RESULTS

The analysis reveals a systematic bias favoring the 3% criterion, impacting the quantification of apnea events. Age-specific patterns demonstrate diminishing agreement between criteria with increasing age.

CONCLUSION

This comprehensive study underscores the importance of standardized criteria in OSA diagnosis. The findings emphasize age-specific considerations and ethical concerns, providing crucial insights for optimizing patient care and advancing sleep medicine practices.

Association of objective sleep duration with cognition and brain aging biomarkers in older adults

The neuropathological mechanisms underlying the association between sleep duration and mild cognitive impairment remain poorly understood. This population-based study included 2032 dementia-free people (age ≥ 60 years; 55.1% women) derived from participants in the Multimodal Interventions to Delay Dementia and Disability in Rural China; of these, data were available in 841 participants for Alzheimer's plasma biomarkers (e.g. amyloid-β, total tau and neurofilament light chain), 1044 for serum microvascular biomarkers (e.g. soluble adhesion molecules) and 834 for brain MRI biomarkers (e.g. whiter matter, grey matter, hippocampus, lacunes, enlarged perivascular spaces and white matter hyperintensity WMH). We used electrocardiogram-based cardiopulmonary coupling analysis to measure sleep duration, a neuropsychological test battery to assess cognitive function and the Petersen's criteria to define mild cognitive impairment. Data were analysed with multivariable logistic and general linear models. In the total sample (n = 2032), 510 participants were defined with mild cognitive impairment, including 438 with amnestic mild cognitive impairment and 72 with non-amnestic mild cognitive impairment. Long sleep duration (>8 versus 6-8 h) was significantly associated with increased likelihoods of mild cognitive impairment and non-amnestic mild cognitive impairment and lower scores in global cognition, verbal fluency, attention and executive function (Bonferroni-corrected P < 0.05). In the subsamples, long sleep duration was associated with higher plasma amyloid-β40 and total tau, a lower amyloid-β42/amyloid-β40 ratio and smaller grey matter volume (Bonferroni-corrected P < 0.05). Sleep duration was not significantly associated with serum-soluble adhesion molecules, white matter hyperintensity volume, global enlarged perivascular spaces and lacunes (P > 0.05). Alzheimer's and neurodegenerative pathologies may represent common pathways linking long sleep duration with mild cognitive impairment and low cognition in older adults.

Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea

GOAL AND AIMS

Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.

FOCUS TECHNOLOGY

Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30.

DESIGN

Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule.

CORE ANALYTICS

Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix.

CORE OUTCOMES

The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep.

CORE CONCLUSION

Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.

Effect of short-term mindfulness-based stress reduction on sleep quality in male patients with alcohol use disorder

Background

Sleep disturbance is one of the most prominent complaints of patients with alcohol use disorder (AUD), with more than 70% of patients with AUD reporting an inability to resolve sleep problems during abstinence. Mindfulness-based stress reduction (MBSR) has been shown to improve sleep quality and as an alternative therapy to hypnotics for sleep disorders.

Objective

The aim of the present study was to evaluate the effect of short-term MBSR on sleep quality in male patients with AUD after withdrawal.

Methods

A total of 91 male patients with AUD after 2 weeks of routine withdrawal therapy were randomly divided into two groups using a coin toss: the treatment group (n = 50) and the control group (n = 41). The control group was received supportive therapy, and the intervention group added with MBSR for 2 weeks on the basis of supportive therapy. Objective sleep quality was measured at baseline and 2 weeks after treatment using the cardiopulmonary coupling (CPC). Indicators related to sleep quality include total sleep time, stable sleep time, unstable sleep time, rapid eye movement (REM) sleep time, wake-up time, stable sleep latency, sleep efficiency, and apnea index. These indicators were compared by an analysis of covariance (ANCOVA) between the two groups, controlling for individual differences in the respective measures at baseline.

Results

The results showed that there were no significant differences in the age [t (89) = -0.541, P = 0.590), BMI [t (89) = -0.925, P = 0.357], educational status [t (89) = 1.802, P = 0.076], years of drinking [t (89) = -0.472, P = 0.638), daily intake [t (89) = 0.892, P = 0.376], types of alcohol [χ2 (1) = 0.071, P = 0.789], scores of CIWA-AR [t (89) = 0.595, P = 0.554], scores of SDS [t (89) = -1.151, P = 0.253), or scores of SAS [t (89) = -1.209, P = 0.230] between the two groups. Moreover, compared with the control group, the total sleep time [F (1.88) = 4.788, P = 0.031) and stable sleep time [F (1.88) = 6.975, P = 0.010] were significantly increased in the treatment group. Furthermore, the average apnea index in the patients who received MBSR was significantly decreased than in the control group [F (1.88) = 5.284, P = 0.024].

Conclusion

These results suggest that short-term MBSR could improve sleep quality and may serve as an alternative treatment to hypnotics for sleep disturbance in patients with AUD after withdrawal.

Sleep Technology

Pediatric sleep providers frequently encounter issues related to sleep technology in clinical settings. In this review article, we discuss technical issues related to standard polysomnography, research on putative complementary novel metrics derived from polysomnographic signals as well as research on home sleep apnea testing in children and consumer sleep devices. Although developments across several of these domains are exciting, it remains a rapidly evolving area. When evaluating innovative devices and home sleep testing approaches, clinicians should be mindful of accurately interpreting diagnostic agreement statistics to apply these technologies appropriately.

Sleep medicine: Practice, challenges and new frontiers

Sleep medicine is an ambitious cross-disciplinary challenge, requiring the mutual integration between complementary specialists in order to build a solid framework. Although knowledge in the sleep field is growing impressively thanks to technical and brain imaging support and through detailed clinic-epidemiologic observations, several topics are still dominated by outdated paradigms. In this review we explore the main novelties and gaps in the field of sleep medicine, assess the commonest sleep disturbances, provide advices for routine clinical practice and offer alternative insights and perspectives on the future of sleep research.

Cardiopulmonary Coupling

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Cardiopulmonary coupling and serum cardiac biomarkers in obesity hypoventilation syndrome and obstructive sleep apnea with morbid obesity

STUDY OBJECTIVES

The main cause of death in patients with obesity hypoventilation syndrome (OHS) is cardiac rather than respiratory failure. Here, we investigated autonomic-respiratory coupling and serum cardiac biomarkers in patients with OHS and obstructive sleep apnea (OSA) with comparable body mass index (BMI) and apnea-hypopnea index (AHI).

METHODS

Cardiopulmonary coupling (CPC) and cyclic variation of heart rate (CVHR) analysis was performed on the electrocardiogram signal from the overnight polysomnogram. Cardiac serum biomarkers were obtained in patients with OHS and OSA with a BMI > 40kg/m². Samples were obtained at baseline and after 3 months of positive airway pressure (PAP) therapy in both groups.

RESULTS

Patients with OHS (n=15) and OSA (n=36) were recruited. No group differences in CPC, CVHR and serum biomarkers were observed at baseline and after 3 months of PAP therapy. An improvement in several CPC metrics, including the sleep apnea index, unstable sleep (low frequency coupling and elevated low frequency coupling narrow band [e-LFC_NB]) and CVHR were observed in both groups with PAP use. However, distinct differences in response characteristics were noted. e-LFC_NB coupling correlated with highly sensitive troponin (hs-troponin-T, p<0.05) in the combined cohort. Baseline hs-troponin-T inversely correlated with awake oxygen saturation in the OHS group (p<0.05).

CONCLUSIONS

PAP therapy can significantly improve CPC stability in obese patients with OSA or OHS, with key differences. e-LFC_NB may function as a surrogate biomarker for early subclinical cardiac disease. Low awake oxygen saturation could also increase this biomarker in OHS.

CLINICAL TRIAL REGISTRATION

Registry: Australian New Zealand Clinical Trials Registry; Name: Obesity Hypoventilation Syndrome and Neurocognitive Dysfunction; URL: https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367492; Identifier: ACTRN12615000122550.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

Characteristics of Children Likely to Have Spontaneous Resolution of Obstructive Sleep Apnea: Results from the Childhood Adenotonsillectomy Trial (CHAT)

OBJECTIVE

To evaluate if cardiopulmonary coupling (CPC) calculated sleep quality (SQI) may have a role in identifying children that may benefit from other intervention than early adenotonsillectomy (eAT) in management of obstructive sleep apnea (OSA).

METHODS

A secondary analysis of electrocardiogram-signals (ECG) and oxygen saturation-data (SpO₂) collected during polysomnography-studies in the prospective multicenter Childhood Adenotonsillectomy Trial (CHAT) to calculate CPC-SQI and apnea hypopnea index (AHI) was executed. In the CHAT, children 5-9 years with OSA without prolonged oxyhemoglobin desaturations were randomly assigned to adenotonsillectomy (eAT) or watchful waiting with supportive care (WWSC). The primary outcomes were to document change in attention and executive function evaluated with the Developmental Neuropsychological Assessment (NEPSY). In our analysis, children in the WWSC-group with spontaneous resolution of OSA (AHI_Obstructive < 1.0) and high-sleep quality (SQI ≥ 75) after 7-months were compared with children that showed residual OSA.

RESULTS

Of the 227 children randomized to WWSC, 203 children had available data at both baseline and 7-month follow-up. The group that showed resolution of OSA at month 7 (n = 43, 21%) were significantly more likely to have high baseline SQI 79.96 [CI_95% 75.05, 84.86] vs. 72.44 [CI_95% 69.50, 75.39], p = 0.005, mild OSA AHI_Obstructive 4.01 [CI_95% 2.34, 5.68] vs. 6.52 [CI_95% 5.47, 7.57], p= 0.005, higher NEPSY-attention-executive function score 106.22 [CI_95% 101.67, 110.77] vs. 101.14 [CI_95% 98.58, 103.72], p = 0.038 and better quality of life according to parents 83.74 [CI_95% 78.95, 88.54] vs. 77.51 [74.49, 80.53], p = 0.015. The groups did not differ when clinically evaluated by Mallampati score, Friedman palate position or sleep related questionnaires.

CONCLUSIONS

Children that showed resolution of OSA were more likely to have high-SQI and mild OSA, be healthy-weight and have better attention and executive function and quality of life at baseline. As this simple method to evaluate sleep quality and OSA is based on analyzing signals that are simple to collect, the method is practical for sleep-testing, over multiple nights and on multiple occasions. This method may assist physicians and parents to determine the most appropriate therapy for their child as some children may benefit from WWSC rather than interventions. If the parameters can be used to plan care a priori, this would provide a fundamental shift in how childhood OSA is diagnosed and managed.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Cardiopulmonary coupling-derived sleep quality is associated with improvements in blood pressure in patients with obstructive sleep apnea at high-cardiovascular risk

OBJECTIVE

Investigate if changes in objective sleep quality index (SQI) assessed through cardiopulmonary-coupling analysis impacts blood pressure (BP) in patients with obstructive sleep apnea at high-cardiovascular risk.

METHODS

Secondary analysis of ECG and pulse-oximetry-[oxygen saturation (SpO2)] data from the Heart Biomarker Evaluation in Apnea Treatment study, multicenter, controlled trial in patients with cardiovascular disease and moderate-severe obstructive sleep apnea, randomly assigned to intervention of healthy lifestyle and sleep hygiene education (HLSE; control group), continuous positive airway pressure (CPAP) or nocturnal supplemental oxygen (NSO). Participants with good-quality ECG-signal and SpO2-signal (n = 241) were included.

RESULTS

CPAP-therapy significantly improved BP, with net average improvement in mean arterial blood pressure during sleep (MAP) when compared with nocturnal supplemental oxygen-therapy or healthy lifestyle and sleep education-therapy, -3.92 (P = 0.012) and -3.83 (P = 0.016), respectively. When stratified on the basis of baseline-SQI, CPAP-therapy improves 24-h MAP -3.02 (P = 0.030) and MAP -5.00 (P = 0.001), in patients with compromised baseline-SQI (SQI < 55). Stratifying the cohort based on changes in SQI during the study period (SQI-SQI), controlling for sex, age over 60, apnea-hypopnea index, SpO2 less than 80%, baseline BP and cardiovascular disease, significant differences are observed comparing the groups that Improved-SQI (SQI < 55, SQI ≥ 55) and Declined-SQI (SQI ≥ 55, SQI < 55) in MAP -4.87 (P = 0.046) and mean diastolic blood pressure (MDP) -4.42 (P = 0.026) as well as MAP -6.36 (P = 0.015), mean systolic blood pressure wake (MSP) -7.80 (P = 0.048) and MDP -5.64 (P = 0.009), respectively. Improved SQI reflects the magnitude of positive effect on BP which is reached mostly through initiation of CPAP-therapy.

CONCLUSION

Cardiopulmonary coupling-derived sleep quality impacted 24-h MAP and MDP, as well as BP during wake, in patients participating in the Heart Biomarker Evaluation in Apnea Treatment-study.

Cognitive deficit is correlated with sleep stability in insomnia: A cardiopulmonary coupling study

OBJECTIVES

To assess the correlation of cognitive function with sleep stability and depressive-anxious symptoms in insomnia patients.

METHODS

Twenty-two insomnia patients with cognitive impairment (insomnia-CI), 21 insomnia patients with normal cognition (insomnia-CN), and 15 matched healthy control subjects (HCs) were enrolled and completed neuropsychological tests, the Hamilton Depression and Anxiety Scales (HAMD and HAMA), the Epworth Sleepiness Scale, the Pittsburgh Sleep Quality Index (PSQI),the Insomnia Severity Index (ISI), and the cardiopulmonary coupling (CPC) examination. Ratios of high-frequency coupling (HFC), low-frequency coupling (LFC), and very low-frequency coupling (VLFC) measured by CPC analysis represent stable sleep, unstable sleep, and wake/rapid eye movement (REM) sleep, respectively.

RESULTS

The HAMD, HAMA, PSQI, and ISI scores were higher in the insomnia-CN patients than in the HCs (all p < .01). However, no differences were found in the HFC, LFC, and VLFC ratio between the HCs and insomnia-CN groups. Compared with the insomnia-CN patients, insomnia-CI patients exhibited higher scores on the HAMD, HAMA (all p < .01), and PSQI (p < .05), performed worse on the Auditory Verbal Learning Test, Trial Making Test B, and Stroop Test B (all p < .01), had a lower HFC ratio, and had a higher LFC ratio in the CPC analysis (all p < .01). Furthermore, in the insomnia patients, poorer cognition was correlated with a decreased HFC ratio and an increased VLFC ratio (r = .356, p = .019; r = -.339, p =.026, respectively) and increased HAMD and HAMA scores (r = -.507, p < .001; r = -.561, p < .001, respectively); a higher VLFC ratio was correlated with an increased ISI score (r = .346, p = .023).

CONCLUSIONS

Cognitive deterioration in insomnia patients was associated with a decreased stable sleep ratio, an increased wake/REM sleep ratio and more severe symptoms of depression and anxiety. CPC analysis can reflect the severity of insomnia.

Can improvements in sleep quality positively affect serum adiponectin-levels in patients with obstructive sleep apnea?

BACKGROUND

Assess if changes in sleep quality (Sleep Quality Index, SQI) based on cardiopulmonary coupling-analysis (CPC) impacts serum adiponectin-levels in patients with cardiovascular disease (CVD).

METHODS

Secondary analysis of electrocardiogram (ECG) data from the Heart Biomarker Evaluation in Apnea Treatment study (HeartBEAT), a multicenter, controlled trial in patients with CVD and moderate-severe sleep apnea, randomly assigned to intervention of Continuous Positive Airway Pressure (CPAP), Nocturnal Supplemental Oxygen (NSO) or Healthy Lifestyle and Sleep Hygiene Education (HLSE; control group). Participants with good-quality ECG-signal (n = 241) were included.

RESULTS

Improving CPC-sleep quality was associated with net average improvements in serum adiponectin-levels 2.69 μg/ml (p = 0.005) irrespective of therapy initiated. After controlling for confounders, a unit increase in SQI was associated with increase in serum adiponectin-levels 0.071 μg/ml (p = 0.012) and decrease in insulin-levels 0.197 μIU/ml (p = 0.0018). Similarly, a percentage point increase in sleep apnea indicator (SAI) was associated with decrease in serum adiponectin-levels of 0.071 μg/ml (p = 0.017) and increase in insulin-levels of 0.218 μIU/ml (p = 0.020). A percentage point increase in CPC-sleep fragmentation (eLFC_BB) had a predicted increase in glucose-levels 0.371 mg/dl (p = 0.009) and insulin-levels 0.284 μIU/ml (p = 0.010). In patients receiving CPAP-therapy, a difference in serum adiponictin levels of 3.82 μg/ml (p = 0.025) is observed comparing patients in which SQI-improved to patients that SQI-declined during the study period. The difference is mostly due to a decrease in serum adiponectin levels in patients that decline in SQI (-3.20 μg/ml).

CONCLUSION

Improvements in sleep quality were associated with higher serum adiponectin-levels, and improved measures of glycemic metabolism which may have beneficial effects on metabolic syndrome and cardiovascular health.

CLINICAL TRIAL REGISTRATION NAME AND NUMBER

The Heart Biomarker Evaluation in Apnea Treatment (HeartBEAT) study is registered at https://clinicaltrials.gov/ct2/show/NCT01086800.

Sleep staging from electrocardiography and respiration with deep learning

STUDY OBJECTIVES

Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.

METHODS

Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals.

RESULTS

ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen's kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications.

CONCLUSIONS

Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.

Night-to-night variability of sleep electroencephalography-based brain age measurements

OBJECTIVE

Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI.

METHODS

86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured.

RESULTS

The within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively.

CONCLUSIONS

Averaging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI.

SIGNIFICANCE

With increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients' homes to identify patients who should undergo further investigation or monitoring.

Multicomponent Analysis of Sleep Using Electrocortical, Respiratory, Autonomic and Hemodynamic Signals Reveals Distinct Features of Stable and Unstable NREM and REM Sleep

A new concept of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep is proposed, that of multi-component integrative states that define stable and unstable sleep, respectively, NREM_S, NREM_US REM_S, and REM_US. Three complementary data sets are used: obstructive sleep apnea (20), healthy subjects (11), and high loop gain sleep apnea (50). We use polysomnography (PSG) with beat-to-beat blood pressure monitoring, and electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC) analysis to demonstrate a bimodal, rather than graded, characteristic of NREM sleep. Stable NREM (NREM_S) is characterized by high probability of occurrence of the <1 Hz slow oscillation, high delta power, stable breathing, blood pressure dipping, strong sinus arrhythmia and vagal dominance, and high frequency CPC. Conversely, unstable NREM (NREM_US) has the opposite features: a fragmented and discontinuous <1 Hz slow oscillation, non-dipping of blood pressure, unstable respiration, cyclic variation in heart rate, and low frequency CPC. The dimension of NREM stability raises the possibility of a comprehensive integrated multicomponent network model of NREM sleep which captures sleep onset (e.g., ventrolateral preoptic area-based sleep switch) processes, synaptic homeostatic delta power kinetics, and the interaction of global and local sleep processes as reflected in the spatiotemporal evolution of cortical “UP” and “DOWN” states, while incorporating the complex dynamics of autonomic-respiratory-hemodynamic systems during sleep. Bimodality of REM sleep is harder to discern in health. However, individuals with combined obstructive and central sleep apnea allows ready recognition of REM_S and REM_US (stable and unstable REM sleep, respectively), especially when there is a discordance of respiratory patterns in relation to conventional stage of sleep.

Impact of Catheter Ablation on Sleep Quality and Relationship Between Sleep Stability and Recurrence of Paroxysmal Atrial Fibrillation After Successful Ablation: 24-Hour Holter-Based Cardiopulmonary Coupling Analysis

Background

Sleep fragmentation and sleep apnea are common in patients with atrial fibrillation (AF). We investigated the impact of radio‐frequency catheter ablation (RFCA) on sleep quality in patients with paroxysmal AF and the effect of a change in sleep quality on recurrence of AF.

Methods and Results

Of 445 patients who underwent RFCA for paroxysmal AF between October 2007 and January 2017, we analyzed 225 patients who had a 24‐hour Holter test within 6 months before RFCA. Sleep quality was assessed by cardiopulmonary coupling analysis using 24‐hour Holter data. We compared cardiopulmonary coupling parameters (high‐frequency coupling, low‐frequency coupling, very‐low‐frequency coupling) before and after RFCA. Six months after RFCA, the high‐frequency coupling (marker of stable sleep) and very‐low‐frequency coupling (rapid eye movement/wake marker) was significantly increased (29.84%–36.15%; P<0.001; and 26.20%–28.76%; P=0.002, respectively) while low‐frequency coupling (unstable sleep marker) was decreased (41.25%–32.13%; P<0.001). We divided patients into 3 tertiles according to sleep quality before RFCA, and the risk of AF recurrence in each group was compared. The second tertile was used as a reference; patients with unstable sleep (Tertile 3) had a significantly lower risk of AF recurrence (hazard ratio [HR], 0.32; 95% CI, 0.12–0.83 for high‐frequency coupling; and HR, 0.22; 95% CI, 0.09–0.58 for low‐frequency coupling).

Conclusions

Sleep quality improved after RFCA in patients with paroxysmal AF. The recurrence rate was significantly lower in patients who had unstable sleep before RFCA. These results suggest that RFCA can influence sleep quality, and sleep quality assessment before RFCA may provide a risk marker for recurrence after RFCA in patients with paroxysmal AF.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis

PURPOSE

Despite the increasing number of research studies of cardiopulmonary coupling (CPC) analysis, an electrocardiogram-based technique, the use of CPC in underserved population remains underexplored. This study aimed to first evaluate the reliability of CPC analysis for the detection of obstructive sleep apnea (OSA) by comparing with polysomnography (PSG)-derived sleep outcomes.

METHODS

Two hundred five PSG data (149 males, age 46.8 ± 12.8 years) were used for the evaluation of CPC regarding the detection of OSA. Automated CPC analyses were based on ECG signals only. Respiratory event index (REI) derived from CPC and apnea-hypopnea index (AHI) derived from PSG were compared for agreement tests.

RESULTS

CPC-REI positively correlated with PSG-AHI (r = 0.851, p < 0.001). After adjusting for age and gender, CPC-REI and PSG-AHI were still significantly correlated (r = 0.840, p < 0.001). The overall results of sensitivity and specificity of CPC-REI were good.

CONCLUSION

Compared with the gold standard PSG, CPC approach yielded acceptable results among OSA patients. ECG recording can be used for the screening or diagnosis of OSA in the general population.

Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine

Current strategies for the management of OSA reflect a one-size-fits-all approach. Diagnosis and severity of OSA are based on the apnea-hypopnea index and treatment initiated with CPAP, followed by trials of alternatives (eg, oral appliances) if CPAP "fails." This approach does not consider the heterogeneity of individuals with OSA, reflected by varying risk factors, pathophysiological causes, clinical manifestations, and consequences. Recently, studies using analytic approaches such as cluster analysis have taken advantage of this heterogeneity to identify OSA phenotypes, or subtypes of patients with unique characteristics, that may enable more personalized approaches to prognostication and treatment. Examples include symptom-based subtypes such as "excessively sleepy" and "disturbed sleep" with differing impact of CPAP on symptoms and health-related quality of life. Polysomnographic subtypes, distinguished by respiratory event association with hypoxemia, arousals, or both, exhibit varying risks of cardiovascular disease and response to therapy. This review summarizes the findings from recent cluster analysis studies in sleep apnea and synthesizes common themes to describe the potential role (and limitations) of phenotypic subtypes in precision medicine for OSA. It also highlights future directions, including linking of phenotypes to clinically relevant outcomes, rigorous and transparent assessment of phenotype reproducibility, and need for tools that categorize patients into subtypes, to prospectively validate phenotype-based prognostication and treatment approaches. Finally, we highlight the critical need to include women and more racially/ethnically diverse populations in this area of research if we are to leverage the heterogeneity of OSA to improve patient lives.

Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices

Background

Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China.

Objective

We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices.

Methods

We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc.

Results

The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively).

Conclusions

Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL.

Insomnia symptoms in primary care: A prospective study focusing on prevalence of undiagnosed co-morbid sleep disordered breathing

OBJECTIVE

To determine prevalence of comorbid undiagnosed sleep disordered breathing (SDB) in chronic insomnia patients, using two complementary methods, one standard and one novel.

METHODS

Using prospective design, adult patients diagnosed with chronic insomnia, treated with prescription pharmacological agents for >3 months without prior objective sleep evaluation or diagnosis of SDB were invited to participate. All patients recorded their sleep for two consecutive nights using level 3 home-sleep-apnea-test (HSAT) device to derive Respiratory Event Index (REI) for OSA diagnosis. The electrocardiogram-signal (ECG) recorded by the same device was analyzed using FDA cleared medical software, Cardiopulmonary Coupling (CPC) to quantify sleep time and identify sleep-quality and pathology.

RESULTS

Of 110 chronic insomnia patients who volunteered between May 2017 and June 2018, 88% were women. Prevalence of moderate-severe SDB (REI > 15) was 25% based on REI-scoring. Surrogate markers of moderate-severe SDB detected by the novel method identified prevalence of 33%, with negative predictive value 96%, reclassifying 10 individuals that HSAT diagnosed with mild SDB with more advanced disease state. Agreement between the methods is 88%.

CONCLUSION

High prevalence and overlap in symptoms between insomnia and SDB warrants objective testing when evaluating sleep complaints before therapy is initiated. Diagnostic caution is even more importantly warranted for female patients presenting insomnia sleep complaints, as SDB may not be initially considered as a biological symptom driver. CPC-analysis can complement standard HSAT or serve as a standalone option to evaluate sleep complaints in individuals presenting insomnia symptoms before therapy is initiated.

CLINICAL TRIAL REGISTRY NAME AND NUMBER

Pilot study: Co-occurrence of Insomnia and Sleep Disordered Breathing (SDB) symptoms: Prospective study focusing on chronic insomnia patients treated with pharmacological agents. Approved by the Bioethics Committee on March 7^th, 2017. VSNb: 17- 047- S1/ ST - GRA - 17029 - PDX - SH http://vsn.is/is/content/17-047.