Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep

Sleep duration and social jetlag in healthy adolescents. Association with anxiety, depression, and chronotype: a pilot study

PURPOSE

Misalignment between sleep opportunity and chronotype preference during adolescence may affect sleep and mental health. The aim of this study was to objectively evaluate sleep duration and social jetlag (SJL) to observe if there is a relationship with anxiety, depression, or chronotype.

METHODS

Community based cohort study (n = 65) was conducted in Northern Europe. Participants recorded their sleep during the regular school schedule for 3 school nights and 2 free nights with FDA-cleared/EU Medical Device Directive (CE-02862) compliant home sleep test. They also completed validated questionnaires to assess (Morningness Eveningness Questionnaire; MEQ), sleepiness (Epworth Sleepiness Scale; ESS), and insomnia (Insomnia Severity Index; ISI), anxiety (General Anxiety Disorder-7; GAD-7), and depression symptoms (Beck's Depression Inventory-II; BDI-II). Data were collected during the last week of February and the first 2 weeks of March, 2023.

RESULTS

Average sleep duration on school nights was 7 h, 15 min, with 18% of participants on average sleeping ≥ 8 h. Average sleep onset was significantly later on free nights (1 hour, 47 minutes; p < 0.0001) causing a high prevalence of moderate-severe SJL, in 71% of participants. Evening chronotypes (prevalence 15.4%) fell asleep later on free nights, causing them to have significantly more SJL (2 hours, 58 minutes) than morning chronotypes (1 hour, 32 minutes) and intermediary chronotypes (1 hour, 36 minutes). Evening chronotypes also had more severe insomnia (ISI + 4.4, p = 0.006; + 4.0, p = 0.001) and greater depressive symptoms (BDI-II + 21.6, p < 0.0001; + 17.1, p < 0.0001). A significant negative correlation was observed between MEQ scores and ESS (r =  - 0.287; p = 0.001), ISI (r =  - 0.343, p < 0.0001), GAD-7 (r =  - 0.185, p < 0.0001), BDI-II (r =  - 0.501, p = 0.0001), and suicidal thoughts (r =  - 0.294, p = 0.017).

CONCLUSION

Adolescents are sleep-deprived and have a high prevalence of SJL that is positively correlated with depressive symptoms and evening chronotype. That evening chronotypes have shorter sleep duration, more severe SJL, and significantly more sleepiness and insomnia, anxiety, and depressive symptoms may indicate possible benefits in identifying this group with the aim to assist them in improving their sleep habits with potentially positive effects on mental health.

Correlation analysis of heart rate variations and glucose fluctuations during sleep

OBJECTIVE

The body's glucose concentration is influenced by carbohydrate intake, insulin-induced carbohydrate reduction, and hepatic glycogen breakdown induced by stress hormones. This study investigated the potential of employing glucose fluctuations as a measure of stress by examining the relationship between heart rate variability (HRV) data and glucose levels during sleep in healthy subjects.

METHODS

In this cross-sectional study, a chest-worn electrocardiogram (ECG) and continuous glucose monitoring device (CGM) were respectively used to monitor the heart rate intervals and glucose fluctuations of five subjects (two males, three females) during sleep. A time-series correlation analysis was performed on the HRV data extracted from heart rate intervals and the corresponding glucose fluctuation data.

RESULTS

The time-series analysis of ECG and CGM data collected from subjects during sleep (n = 25 nights) revealed a moderate negative correlation between glucose levels and HRV, with a cross-correlation coefficient of r = -0.453.

CONCLUSION

Similar to HRV, changes in stress levels can be detected by observing glucose fluctuations, particularly during sleep when the impact of food intake can be eliminated. Our findings highlight a significant correlation between glucose levels and HRV, indicating that glucose fluctuations can be used as an indicator of autonomic nervous system activity in an exploratory study.

Sleep quality, sleep apnea, and metabolic health in children treated with adenotonsillectomy

PURPOSE

The aim of this study was to determine if cardiopulmonary coupling (CPC) calculated sleep quality (SQI) may predict changes in metabolic health in children treated with early adenotonsillectomy (eAT) for obstructive sleep apnea (OSA).

METHODS

Secondary analysis of the Childhood Adenotonsillectomy Trial (CHAT) was performed including children 5.0-9.9 years with OSA assigned to eAT. The cohort was stratified based on SQI and AHI to evaluate (1) response to eAT in children with high sleep quality (SQI ≥ 75) and mild-OSA, AHI < 5.0 (group1) and children with moderate-OSA AHI ≥ 5.0 or SQI < 75 (group2) at baseline and (2) effect of eAT therapy on metabolic health, GroupRemission (AHI < 1.0, SQI ≥ 75) compared to GroupResidual.

RESULTS

At baseline group2 (n=124) had higher average heart rate during sleep (AHRSleep), 87 vs. 81 beats/minute (p < 0.001) compared to group1 (n=72). After surgery, group2 on average had less increase in BMI z-score 0.13 vs. 0.27, (p = 0.025), improved their SQI + 2.06 compared to decline - 3.75 in group1, (p = 0.015), decreased AHRSleep-- 2.90 vs. - 0.34 (p = 0.025) and AHI - 5.00 vs. - 0.36 (p = 0.002). GroupRemission was younger 6.59 vs. 7.41; p < 0.001; with lower BMI z-score 0.90 vs. 1.34; p = 0.021; AHRSleep 80.60 vs. 83.50; p = 0.032; fasting insulin (µIU/ml) 7.54 vs. 12.58; p = 0.017 and glucose (mmol/L) 4.45 vs. 4.60; p = 0.049, with better lipid metabolism though not statistically significantly, low-density-lipoprotein 90.26 mg/dL vs. 97.94; p = 0.081 and cholesterol 154.66 mg/dL vs. 164.36; p = 0.076.

CONCLUSION

The results may indicate that children with mild-OSA and high-SQI may be less likely to benefit from eAT than children with moderate-OSA. To improve metabolic health, successfully treating both AHI and SQI is likely needed. CPC-calculated SQI may have a role to identify children less likely to benefit from eAT and to evaluate success of therapy.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00560859.

International Consensus Statement on Obstructive Sleep Apnea

BACKGROUND

Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA).

METHODS

Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus.

RESULTS

The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated.

CONCLUSION

This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.

The First-Night Effect on the Instability of Stage N2: Evidence from the Activity of the Central and Autonomic Nervous Systems

A series of studies have suggested that stage N2 is vulnerable and strongly affected by the first-night effect (FNE). However, the neurophysiological mechanism underlying the vulnerability of stage N2 of the FNE has not been well examined. A total of 17 healthy adults (11 women and 6 men, mean age: 21.59 ± 2.12) underwent two nights of polysomnogram recordings in the sleep laboratory. We analyzed sleep structure and central and autonomic nervous system activity during stage N2 and applied the electroencephalographic (EEG) activation index (beta/delta power ratio) and heart rate variability to reflect changes in central and autonomic nervous system activity caused by the FNE. Correlation analyses were performed between EEG activation and heart rate variability. The results showed that EEG activation and high-frequency heart rate variability increased on the adaptation night (Night 1). Importantly, EEG activation was significantly associated with the percentage of stage N1, and the correlation between EEG activation and high-frequency heart rate variability decreased due to the FNE. These findings indicate that the FNE affects the instability of stage N2 by increasing central nervous system activity and uncoupling the activity between the central and autonomic nervous systems.

Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal-subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.

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.

Bidirectional associations between physical activity and sleep in older adults: a multilevel analysis using polysomnography

Although recent studies have examined the bidirectional associations between physical activity and sleep parameters, few have focused on older adults utilizing objective assessments, such as polysomnography. This micro-longitudinal observational study included 92 Japanese older adults (aged 65–86 years) who underwent objective evaluations of sleep quality using polysomnography and completed subjective sleep-related questionnaires. Activity levels were assessed using an accelerometer. Polysomnography, subjective sleep-related questionnaires, and accelerometer were administered for 7 consecutive days. Multilevel models (participant-, day-level) were used to examine the temporal associations of objective and subjective sleep parameters with sedentary behavior and physical activity. In the day-level analysis, higher levels of sedentary behavior during daytime were associated with longer rapid eye movement (REM) sleep, shorter REM latency, lower levels of non-REM sleep (stage N3), and reduced delta power during daytime. Higher levels of low-intensity physical activity during daytime were associated with lower levels of REM sleep, longer REM latency, and increased stage N3 sleep in the day-level analysis. Higher levels of moderate-to-vigorous physical activity were associated with increased REM latency. Longer subjective sleep time was associated with increased next-day moderate-to-vigorous physical activity. Thus, low-intensity physical activity may provide objective benefits related to deep sleep parameters in older adults.

Association between sleep quality, hypertensive disorders of pregnancy, and sleep-disordered breathing in pregnant women with and without obesity: An observational cross-sectional study

AIM

To elucidate whether pregnancy and obesity are associated with poor sleep quality, and to investigate if sleep quality is associated with hypertensive disorders of pregnancy in pregnant women with obesity.

METHODS

This observational cross-sectional study examined 15 obese pregnant women (body mass index ≥30 kg/m² ) (p-Ob group), 15 nonobese pregnant women (p-nOb group), and 30 nonobese nonpregnant women (n-Pr group), using home recording devices to monitor sleep-disordered breathing (SDB): respiratory disturbance index, oxygen saturation, and sleep stage and quality. These variables were compared among the groups. Moreover, obese women with and without hypertensive disorders of pregnancy were compared.

RESULTS

Significant differences in respiratory disturbance index (median: 10.3 [p-Ob], 7.1 [p-nOb], 3.5 [n-Pr]) and oxygen saturation (95.1%, 96.5%, and 96.6%) were observed among the groups. Seven participants in the p-Ob group experienced hypertensive disorders of pregnancy with SDB before or after sleep examination. Particularly, those who developed hypertensive disorders of pregnancy before sleep examination showed a lower delta power throughout the night than those without this condition.

CONCLUSION

Pregnant women had poor sleep quality; those with obesity had higher frequency of SDB with worsened respiratory conditions that might cause complications. Our findings suggest that the development of hypertensive disorders of pregnancy in some obese pregnant women might be associated with insufficient delta power. Focusing on delta power may reflect subtle changes in sleep quality that occur in pregnant women. Future longitudinal studies with larger sample sizes are required to confirm these findings and investigate causality.

Autonomic Central Coupling during Daytime Sleep Differs between Older and Younger People

Decreased functioning in the elderly is mirrored by independent changes in central and autonomic nervous systems. Additionally, recent work suggests that the coupling of these systems may also serve an important role. In young adults, Autonomic and Central Events (ACEs), measured in the temporal coincidence of heart rate bursts (HRBs) and increased slow-wave-activity (SWA, 0.5-1Hz) and sigma activity (12-15Hz), followed by parasympathetic surge (RR_HF) during non-rapid eye movement (NREM) sleep, predicted cognitive improvements. However, ACEs have not been examined in the elderly. Thus, the current study compared ACEs during wake and daytime sleep in older and younger adults and examined associations with working memory improvement before and after a nap. Compared to youngers, older adults showed lower amplitude of ACEs during NREM sleep, but not during wake. Furthermore, while younger adults demonstrated a parasympathetic surge after HRBs, older adults showed an earlier rise and longer maintenance of the RRHF. Taken together, our results demonstrate that autonomic-central coupling declines with age. Pathological aging implicates independent roles for decreased autonomic and central nervous system functioning, the current findings suggest that the coupling of these systems may also deserve attention.

Associations of Reduced Sympathetic Neural Activity and Elevated Baroreflex Sensitivity With Non-Rapid Eye Movement Sleep: Evidence From Electroencephalogram- and Electrocardiogram-Based Sleep Staging

OBJECTIVE

Autonomic neural controls in sleep regulation have been previously demonstrated; however, whether these alternations can be observed by different sleep staging approaches remains unclear. Two established methods for sleep staging-the standardized visual scoring and the cardiopulmonary coupling (CPC) analysis based on electrocardiogram-were used to explore the cardiovascular profiles of sleep.

METHODS

Overnight polysomnography was recorded together with continuous beat-to-beat blood pressure. Cortical activity, heart rate variability, blood pressure variability, and baroreflex sensitivity during sleep stages from 24 nights of sleep were obtained from 15 normotensive participants and analyzed.

RESULTS

Non-rapid eye movement sleep (NREM) from visual scoring and restful sleep (RS) of CPC both showed the highest delta power of electroencephalogram (EEG) and lowest beta activity of EEG in comparison with other sleep stages (p < .001); likewise, the lowest total power of heart rate variability and suppressed vascular-sympathetic activity, reflected by low-frequency power of blood pressure variability, as well as a trend in elevated baroreflex sensitivity, were observed in the NREM or RS. This suppressed vascular-sympathetic activity during stable sleep further exhibited a significant correlation with increased slow-wave activity (NREM: r = -0.292 ± 0.34, p = .002; RS: r = -0.209 ± 0.30, p = .010).

CONCLUSIONS

Autonomic nervous system is evidently associated with stable sleep, as indicated by the similar findings obtained from sleep stages categorized by standardized visual scoring or CPC analysis. Such association between cardiovascular neural activity and sleep EEGs can be observed regardless of the sleep staging approach followed.

Slow wave synchronization and sleep state transitions

Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.

ECG and Heart Rate Variability in Sleep-Related Breathing Disorders

Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.

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.

Revisiting level II sleep studies in the era of COVID-19: a theoretical economic decision model in patients with suspected obstructive sleep apnea

Background

The recent pandemic has made it more challenging to assess patients with suspected obstructive sleep apnea (OSA) with in laboratory polysomnography (PSG) due to concerns of patient and staff safety. The purpose of this study was to assess how Level II sleep studies (LII, full PSG in the home) might be utilized in diagnostic algorithms of suspected OSA using a theoretical decision model.

Methods

We examined four diagnostic algorithms for suspected OSA: an initial PSG approach, an initial LII approach, an initial Level III approach (LIII, limited channel home sleep study) followed by PSG if needed, and an initial LIII approach followed by LII if needed. Costs per patient assessed was calculated as a function of pretest OSA probability and a variety of other variables (e.g. costs of tests, failure rate of LIII/LII, sensitivity/specificity of LIII). The situation in British Columbia was used as a case study.

Results

The variation in cost per test was calculated for each algorithm as a function of the above variables. For British Columbia, initial LII was the least costly across a broad range of pretest OSA probabilities (< 0.80) while initial LIII followed by LII as needed was least costly at very high pretest probability (> 0.8). In patients with a pretest OSA probability of 0.5, costs per patient for initial PSG, initial LII, initial LIII followed by PSG, and initial LIII followed by LII were: $588, $417, $607, and $481 respectively.

Conclusions

Using a theoretical decision model, we developed a preliminary cost framework to assess the potential role of LII studies in OSA assessment. Across a broad range of patient pretest probabilities, initial LII studies may provide substantial cost advantages. LII studies might be especially useful during pandemics as they combine the extensive physiologic information characteristic of PSG with the ability to avoid in-laboratory stays. More empiric studies need to be done to test these different algorithms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41606-021-00063-5.

Digital sleep measures and white matter health in the Framingham Heart Study

AIM

Impaired sleep quality and sleep oxygenation are common sleep pathologies. This study assessed the impact of these abnormalities on white matter (WM) integrity in an epidemiological cohort.

METHODS

The target population was the Framingham Heart Study Generation-2/Omni-1 Cohorts. Magnetic resonance imaging (diffusion tensor imaging) was used to assess WM integrity. Wearable digital devices were used to assess sleep quality: the (M1-SleepImage™ system) and the Nonin WristOx for nocturnal oxygenation. The M1 device collects trunk actigraphy and the electrocardiogram (ECG); sleep stability indices were computed using cardiopulmonary coupling using the ECG. Two nights of recording were averaged.

RESULTS

Stable sleep was positively associated with WM health. Actigraphic periods of wake during the sleep period were associated with increased mean diffusivity. One marker of sleep fragmentation which covaries with respiratory chemoreflex activation was associated with reduced fractional anisotropy and increased mean diffusivity. Both oxygen desaturation index and oxygen saturation time under 90% were associated with pathological directions of diffusion tensor imaging signals. Gender differences were noted across most variables, with female sex showing the larger and significant impact.

CONCLUSIONS

Sleep quality assessed by a novel digital analysis and sleep hypoxia was associated with WM injury, especially in women.

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.

Discrepancies in the Time Course of Sleep Stage Dynamics, Electroencephalographic Activity and Heart Rate Variability Over Sleep Cycles in the Adaptation Night in Healthy Young Adults

OBJECTIVE

The aim of the present study was to characterize the cyclic sleep processes of sleep-stage dynamics, cortical activity, and heart rate variability during sleep in the adaptation night in healthy young adults.

METHODS

Seventy-four healthy adults participated in polysomnographic recordings on two consecutive nights. Conventional sleep variables were assessed according to standard criteria. Sleep-stage continuity and dynamics were evaluated by sleep runs and transitions, respectively. These variables were compared between the two nights. Electroencephalographic and cardiac activities were subjected to frequency domain analyses. Cycle-by-cycle analysis was performed for the above variables in 34 subjects with four sleep cycles and compared between the two nights.

RESULTS

Conventional sleep variables reflected lower sleep quality in the adaptation night than in the experimental night. Bouts of stage N1 and stage N2 were shorter, and bouts of stage Wake were longer in the adaptation night than in the experimental night, but there was no difference in stage N3 or stage REM. The normalized transition probability from stage N2 to stage N1 was higher and that from stage N2 to N3 was lower in the adaptation night, whereas that from stage N3 to other stages did not differ between the nights. Cycle-by-cycle analysis revealed that sleep-stage distribution and cortical beta EEG power differed between the two nights in the first sleep cycle. However, the HF amplitude of the heart rate variability was lower over the four sleep cycles in the adaptation night than in the experimental night.

CONCLUSION

The results suggest the distinct vulnerability of the autonomic adaptation processes within the central nervous system in young healthy subjects while sleeping in a sleep laboratory for the first time.

Cerebral cortical autonomic connectivity in low-risk term newborns

PURPOSE

The mature central autonomic network includes connectivity between autonomic nervous system brainstem centers and the cerebral cortex. The study objective was to evaluate the regional connectivity between the cerebral cortex and brainstem autonomic centers in term newborns by measuring coherence between high-density electroencephalography and heart rate variability as measured by electrocardiography.

METHODS

Low-risk term newborns with birth gestational age of 39-40 weeks were prospectively enrolled and studied using time-synced electroencephalography and electrocardiography for up to 60 min before discharge from the birth hospital. The ccortical autonomicc nervous system association was analyzed using coherence between electroencephalography-delta power and heart rate variability. Heart rate variability measured the parasympathetic tone (root mean square of successive differences of heart rate) and sympathetic tone (standard deviation of heart rate).

RESULTS

One hundred and twenty-nine low-risk term infants were included. High coherence delta-root mean square of successive differences was found in central, bitemporal, and occipital brain regions, with less robust coherence delta-standard deviation in the central region and bitemporal areas.

CONCLUSIONS

Our findings describe a topography of ccortical autonomicc connectivity present at term in low-risk newborns, which was more robust to parasympathetic than sympathetic brainstem centers and was independent of newborn state.

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.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

SIGNIFICANCE

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

Autonomic/central coupling benefits working memory in healthy young adults

Working memory (WM) is an executive function that can improve with training. However, the precise mechanism for this improvement is not known. Studies have shown greater WM gains after a period of sleep than a similar period of wake, and correlations between WM improvement and slow wave activity (SWA; 0.5-1 Hz) during slow wave sleep (SWS). A different body of literature has suggested an important role for autonomic activity during wake for WM. A recent study from our group reported that the temporal coupling of Autonomic/CentralEvents (ACEs) during sleep was associated with memory consolidation. We found that heart rate bursts (HR bursts) during non-rapid eye movement (NREM) sleep are accompanied by increases in SWA and sigma (12-15 Hz) power, as well as increases in the high-frequency (HF) component of the RR interval, reflecting vagal rebound. In addition, ACEs predict long-term, episodic memory improvement. Building on these previous results, we examined whether ACEs also contribute to gains in WM. We tested 104 young adults in an operation span task (OSPAN) in the morning and evening, with either a nap (n = 53; with electroencephalography (EEG) and electrocardiography (ECG)) or wake (n = 51) between testing sessions. We identified HR bursts in the ECG and replicated the increases in SWA and sigma prior to peak of the HR burst, as well as vagal rebound after the peak. Furthermore, we showed sleep-dependent WM improvement, which was predicted by ACE activity. Using regression analyses, we discovered that significantly more variance in WM improvement could be explained with ACE variables than with overall sleep activity not time-locked with ECG. These results provide the first evidence that coordinated autonomic and central events play a significant role in sleep-related WM improvement and implicate the potential of autonomic interventions during sleep for cognitive enhancement.

High-frequency cardiopulmonary coupling during sleep correlates with memory in depressed patients: A pilot study

BACKGROUND

It has been demonstrated that a reduction in the amount of high-frequency cardiopulmonary coupling (CPC) is indicative of unstable sleep in unmedicated patients with major depressive disorder (MDD). Considering the close relationship between sleep quality and memory consolidation, this study sought to investigate the potential of high-frequency CPC as a novel biomarker for objective evaluation of memory impairment in MDD.

METHODS

A total of 64 depressed patients and 35 healthy controls were included in this cross-sectional study. High-frequency coupling (HFC) was assessed by electrocardiogram-based CPC analysis using a portable sleep-respiration monitor during sleep for one night. The next day, subjects completed the cognition assessment with the Assessment of Neuropsychological Status (RBANS). The 17-Item Hamilton Depression Rating Scale (HAMD₁₇) and the Hamilton Rating Scales for Anxiety (HAMA) were used to evaluate the severity of depression and anxiety in each patient, respectively.

RESULTS

There was no significant difference in the proportion of HFC between depressed patients and healthy controls. In patients with low HFC proportion (<35%), severe anxiety could significantly decrease HFC proportion. The HFC proportion positively correlated with immediate and delayed memory in depressed patients. Further analysis showed that patients with low HFC proportion may have worse delayed memory.

LIMITATIONS

The lack of prior exposure to the monitoring equipment and procedure could have generated artefacts that would have disappeared after habituation.

CONCLUSIONS

These results support a positive correlation between the HFC proportion and memory in depressed patients. Further research is required to explore the clinical implications of these findings.

Variation in Sleep Stability with Differences in Severity of Sleep-Disordered Breathing in Children

OBJECTIVES

The aim of this study was to analyze the association between obstructive sleep apnea (OSA) severity and various cardiopulmonary coupling (CPC) parameters in children with OSA.

STUDY DESIGN

Retrospective cross-sectional study.

METHODS

A cross-sectional study was conducted among 117 children (aged 7.96 ± 3.54 years, 86 male) who underwent both full-night polysomnography (PSG) and CPC for suspicion of sleep-disordered breathing (SDB). We analyzed the association between various CPC and PSG findings.

RESULTS

The apnea-hypopnea index (AHI) was negatively correlated with high frequency coupling (HFC, r = -0.374, P < .001) and very low frequency coupling (VLFC, r = -0.192, P = .038) and positively correlated with low frequency coupling (LFC, r = 0.503, P < .001), elevated low frequency coupling (e-LFC, r = 0.475, P < .001), and narrow and broad band e-LFC (e-LFC_NB and e-LFC_BB ; r = 0.221, P = .016 and r = 0.468, P < .001, respectively). The arousal index was negatively correlated with HFC (r = - 0.466, P < .001) and positively correlated with LFC, e-LFC, e-LFC_NB , and e-LFC_BB (r = 0.543, r = 0.460, r = 0.239, and r = 0.445, respectively; all P < .001). In addition, we also found a significant difference in various CPC values according to OSA severity.

CONCLUSION

CPC parameters accurately reflect sleep fragmentation and OSA severity in children. Thus, we can verify objective sleep quality using CPC analysis, which is a simple method of analyzing sleep stability in children with SDB.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:435-439, 2021.

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.

Significance of sleep stability using cardiopulmonary coupling in sleep disordered breathing

OBJECTIVES

The aim of this study was to assess the sleep quality and sleep stability according to the severity of obstructive sleep apnea (OSA) by using questionnaires and cardiopulmonary coupling (CPC) analysis.

METHODS

Two hundred and twenty-one adults were evaluated using subjective sleep questionnaires, CPC parameters, and respiratory parameters measured during full-night polysomnography. We measured the differences in the CPC parameters of each OSA group and the correlation between the apnea-hypopnea index (AHI) and CPC parameters.

RESULTS

AHI and CPC parameters were not associated with sleep questionnaires. AHI is negatively correlated with high frequency coupling (HFC, r = -0.725, P < 0.001) and very low frequency coupling (VLFC, r = -0.475, P < 0.001), but it is positively correlated with low frequency coupling (LFC, r = 0.786, P < 0.001) and narrow- and broadband-elevated low frequency coupling (e-LFC_NB and e-LFC_BB ; r = 0.522, P < 0.001 and r = 0.668, P < 0.001, respectively). We also found similar results regarding the correlation between the arousal index and CPC parameters. In addition, there were significant differences in HFC, LFC, VLFC, e-LFC_NB , and e-LFC_BB (all P < 0.001) between the severe OSA group and all other groups.

CONCLUSION

We found that CPC parameters significantly correlated with AHI. In addition, sleep stability-related parameters differ significantly based on OSA severity, and apneic parameters in the severe OSA group are significantly different from those in the other groups.

LEVEL OF EVIDENCE

4 Laryngoscope, 130: 2069-2075, 2020.

Failure to de-arouse during sleep-onset transitions in the heart rates of individuals with sleep-onset insomnia

OBJECTIVE

Increased cardiovascular risk associated with sleep-onset insomnia has been reported, but the patterns of heart rate (HR) transitions during sleep onset in individuals with sleep-onset insomnia remain uncertain. This study explored the HR dynamics during objective and subjective sleep onset transitions among sleep-onset insomnia.

METHODS

Seventeen good sleepers and 17 individuals with sleep-onset insomnia had their night-time HR measured. HR and heart rate variability (HRV) were analyzed within 8-min periods of pre- and post-transition of stage N1, stage N2 and subjective sleep onset.

RESULTS

A significantly higher low-frequency percentage of HRV was observed in pre-N1 period among insomnia group, compared with good sleepers. Decline in HR begins in 160 s prior to N1 onset among good sleepers, whereas the HRs of insomnia group were reduced only after N1 onset in comparison to their HRs at the time of N1 onset. The good sleepers and insomnia group both had their HRs dropped to a level comparable to their HRs at respective stage N2 onset at 220 s and 80 s prior to N2 onset. No differences in HR was found during subjective sleep onset transition in both groups.

CONCLUSION

During the wake-to-sleep transition, a low and stable HR was observed before cortical alternations in good sleepers; however, a consistently high HR until N1 onset was shown among sleep-onset insomnia. This finding suggests a state-dependent and failure to de-arouse from the high arousal level of wakefulness into light sleep is associated with sleep initiation difficulty.

Further Development of P4 Approach to Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a heterogeneous disorder. Cluster analysis has identified different physiologic subtypes with respect to symptoms. A difference exists in cardiovascular risk from OSA between the 7 subtypes identified. There are 3 basic subtypes replicated in multiple studies: (a) a group where insomnia is the main symptom; (b) an asymptomatic group; (c) a group with marked excessive sleepiness. The symptomatic benefit from treatment with nasal CPAP varies between these 3 subtypes. Data from the Sleep Heart Health Study reveal that the increased risk of cardiovascular disease from OSA occurs only in the excessively sleepy group.

Age and autonomic control, but not cerebral oxygenation, are significant determinants of EEG spectral power in children

Study Objectives

Sleep disordered breathing (SDB) in children has significant effects on daytime functioning and cardiovascular control; attributed to sleep fragmentation and repetitive hypoxia. Associations between electroencephalograph (EEG) spectral power, autonomic cardiovascular control and cerebral oxygenation have been identified in adults with SDB. To date, there have been no studies in children. We aimed to assess associations between EEG spectral power and heart rate variability as a measure of autonomic control, with cerebral oxygenation in children with SDB.

Methods

One hundred sixteen children (3–12 years) with SDB and 42 controls underwent overnight polysomnography including measurement of cerebral oxygenation. Power spectral analysis of the EEG derived from C4-M1 and F4-M1, quantified delta, theta, alpha, and beta waveforms during sleep. Multiple regression tested whether age, SDB severity, heart rate (HR), HR variability (HRV), and cerebral oxygenation were determinants of EEG spectral power.

Results

There were no differences in EEG spectral power derived from either central or frontal regions for any frequency between children with different severities of SDB so these were combined. Age, HR, and HRV low frequency power were significant determinants of EEG spectral power depending on brain region and sleep stage.

Conclusion

The significant findings of this study were that age and autonomic control, rather than cerebral oxygenation and SDB severity, were predictive of EEG spectral power in children. Further research is needed to elucidate how the physiology that underlies the relationship between autonomic control and EEG impacts on the cardiovascular sequelae in children with SDB.

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.

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

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

Objective sleep quality and metabolic risk in healthy weight children results from the randomized Childhood Adenotonsillectomy Trial (CHAT)

BACKGROUND

We hypothesized that cardiopulmonary coupling (CPC) sleep quality reflects cardiovascular and cardiometabolic health, in healthy weight children.

METHODS

Retrospective signal analysis of existing ECG data utilizing CPC, FDA cleared, software as medical device (SaMD). ECG signals were extracted from baseline polysomnography studies in the prospective Childhood Adenotonsillectomy Trial database, multicenter, single-blind, randomized controlled trial of 5.0-9.9-year-old children identified with obstructive sleep apnea syndrome without severe hypoxemia. Healthy weight was defined as age- and gender-specific BMI in the 5th-85th percentile range and overweight above the 85th percentile. The cohort was stratified based on CPC sleep quality Index (SQI) defined as high sleep quality (SQI  ≥ 80) or low sleep quality (SQI < 60). Cardiovascular, cardiometabolic, quality of life, and cognition were compared between the sleep quality groups.

RESULTS

Healthy weight children with low sleep quality had more fragmented sleep with significantly higher arousal index (10.0 ± 4.3 vs.7.2 ± 3.1; p = 0.00) and eLFC_BB (12.4 ± 4.9 vs. 0.9 ± 1.0; p < 0.001) CPC indicator of sleep fragmentation, higher average heart rate during sleep (84.5 ± 10.6 vs. 79.4 ± 7.1; p = 0.03) and worse insulin/glucose ratio (1.7 ± 1.6 vs. 1.1 ± 1.1; p = 0.03) and fasting insulin levels (7.9 ± 7.2 vs.5.3 ± 5.5; p = 0.05) when compared to healthy weight children with high sleep quality. SQI significantly correlates with average heart rate during sleep, insulin and triglyceride levels; for a unit increase in SQI, there is 0.154 unit decrease in average heart rate during sleep, 0.109 unit in insulin levels and 0.332 unit in triglyceride levels, respectively.

CONCLUSION

CPC sleep quality offers insights into pediatric sleep and how it affects cardiovascular and cardiometabolic health. ECG is simple signal to collect, which makes this method practical for testing sleep, over multiple nights, and on multiple occasions providing information on sleep dynamics not possible before.

TRIAL REGISTRATION

NCT00560859.

The Sleep of the Ring: Comparison of the ŌURA Sleep Tracker Against Polysomnography

Objective/Background: To evaluate the performance of a multisensor sleep-tracker (ŌURA ring) against polysomnography (PSG) in measuring sleep and sleep stages. Participants: Forty-one healthy adolescents and young adults (13 females; Age: 17.2 ± 2.4 years). Methods: Sleep data were recorded using the ŌURA ring and standard PSG on a single laboratory overnight. Metrics were compared using Bland-Altman plots and epoch-by-epoch (EBE) analysis. Results: Summary variables for sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO) were not different between ŌURA ring and PSG. PSG-ŌURA discrepancies for WASO were greater in participants with more PSG-defined WASO (p < .001). Compared with PSG, ŌURA ring underestimated PSG N3 (~20 min) and overestimated PSG REM (~17 min; p < .05). PSG-ŌURA differences for TST and WASO lay within the ≤ 30 min a-priori-set clinically satisfactory ranges for 87.8% and 85.4% of the sample, respectively. From EBE analysis, ŌURA ring had a 96% sensitivity to detect sleep, and agreement of 65%, 51%, and 61%, in detecting "light sleep" (N1), "deep sleep" (N2 + N3), and REM sleep, respectively. Specificity in detecting wake was 48%. Similarly to PSG-N3 (p < .001), "deep sleep" detected with the ŌURA ring was negatively correlated with advancing age (p = .001). ŌURA ring correctly categorized 90.9%, 81.3%, and 92.9% into PSG-defined TST ranges of < 6 hr, 6-7 hr, > 7 hr, respectively. Conclusions: Multisensor sleep trackers, such as the ŌURA ring have the potential for detecting outcomes beyond binary sleep-wake using sources of information in addition to motion. While these first results could be viewed as promising, future development and validation are needed.

Association Between Phase Coupling of Respiratory Sinus Arrhythmia and Slow Wave Brain Activity During Sleep

Phase coupling of respiratory sinus arrhythmia (RSA) has been proposed to be an alternative measure for evaluating autonomic nervous system (ANS) activity. The aim of this study was to analyze how phase coupling of RSA is altered during sleep, in order to explore whether this measure is a predictor of slow wave sleep (SWS). Overnight electroencephalograms (EEG), electrocardiograms (ECG), and breathing using inductance plethysmography were recorded from 30 healthy volunteers (six females, age range 21–64, 31.6 ± 14.7 years). Slow wave activity was evaluated by the envelope of the amplitude of the EEG δ-wave (0.5–4 Hz). The RSA was extracted from the change in the R-R interval (RRI) by band-pass filter, where pass band frequencies were determined from the profile of the power spectral density for respiration. The analytic signals of RSA and respiration were obtained by Hilbert transform, after which the amplitude of RSA (A_RSA) and the degree of phase coupling (λ) were quantified. Additionally, the normalized high-frequency component (HF_n) of the frequency-domain heart rate variability (HRV) was calculated. Using auto- and cross-correlation analyses, we found that overnight profiles of λ and δ-wave were correlated, with significant cross-correlation coefficients (0.461 ± 0.107). The δ-wave and HF_n were also correlated (0.426 ± 0.115). These correlations were higher than that for the relationship between δ-wave and A_RSA (0.212 ± 0.161). The variation of λ precedes the onset of the δ-wave by ~3 min, suggesting a vagal enhancement prior to the onset of SWS. Auto correlation analysis revealed that the periodicity of λ was quite similar to that of the δ-wave (88.3 ± 15.7 min vs. 88.6 ± 16.3 min, λ-cycle = 0.938 × δ-cycle + 5.77 min, r = 0.902). These results suggest that phase coupling analysis of RSA appears to be a marker for predicting SWS intervals, thereby complementing other noninvasive tools and diagnostic efforts.

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

BACKGROUND

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

OBJECTIVE

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

SIGNIFICANCE

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

Dynamic coupling between the central and autonomic nervous systems during sleep: A review

Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep structure, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted.

Sleep Instabilities Assessed by Cardiopulmonary Coupling Analysis Increase During Childhood and Adolescence

The electrocardiogram-based cardiopulmonary coupling (CPC) technique may be used to track sleep instabilities. With progressing age, maturational changes during childhood and adolescence affect sleep. The objective was to assess developmental changes in sleep instabilities in a natural setting. ECGs during nighttime sleep on regular school days were recorded from 363 subjects aged 4 to 22 years (204 females). The estimated total sleep time (ETST) decreased from 598 to 445 min during childhood and adolescence. Stable sleep linearly decreased with progressing age (high frequency coupling (HFC): 70–48% ETST). Unstable sleep [low frequency coupling (LFC): 9–19% ETST], sleep fragmentation or disordered breathing (elevated LFC: 4–12% ETST), and wake/REM states [very low frequency coupling (VLFC): 20–32% ETST] linearly increased with age. Hence, with progressing age the sleep of children and adolescents shortens, becomes more unstable and is more often affected by fragmentation or sleep disordered breathing, especially in the age group >13 years. It remains to be clarified whether some of the changes are caused by a social jetlag, i.e., the misalignment of body clock and social time especially in adolescents.

Heart rate phenotypes and clinical correlates in a large cohort of adults without sleep apnea

BACKGROUND

Normal sleep is associated with typical physiological changes in both the central and autonomic nervous systems. In particular, nocturnal blood pressure dipping has emerged as a strong marker of normal sleep physiology, whereas the absence of dipping or reverse dipping has been associated with cardiovascular risk. However, nocturnal blood pressure is not measured commonly in clinical practice. Heart rate (HR) dipping in sleep may be a similar important marker and is measured routinely in at-home and in-laboratory sleep testing.

METHODS

We performed a retrospective cross-sectional analysis of diagnostic polysomnography in a clinically heterogeneous cohort of n=1047 adults without sleep apnea.

RESULTS

We found that almost half of the cohort showed an increased HR in stable nonrapid eye movement sleep (NREM) compared to wake, while only 13.5% showed a reduced NREM HR of at least 10% relative to wake. The strongest correlates of HR dipping were younger age and male sex, whereas the periodic limb movement index (PLMI), sleep quality, and Epworth Sleepiness Scale (ESS) scores were not correlated with HR dipping. PLMI was however significantly correlated with metrics of impaired HR variability (HRV): increased low-frequency power and reduced high-frequency power. HRV metrics were unrelated to sleep quality or the ESS value. Following the work of Vgontzas et al, we also analyzed the sub-cohort with insomnia symptoms and short objective sleep duration. Interestingly, the sleep-wake stage-specific HR values depended upon insomnia symptoms more than sleep duration.

CONCLUSION

While our work demonstrates heterogeneity in cardiac metrics (HR and HRV), the population analysis suggests that pathological signatures of HR (nondipping and elevation) are common even in this cohort selected for the absence of sleep apnea. Future prospective work in clinical populations will further inform risk stratification and set the stage for testing interventions.

A novel adhesive biosensor system for detecting respiration, cardiac, and limb movement signals during sleep: validation with polysomnography

BACKGROUND

Although in-lab polysomnography (PSG) remains the gold standard for objective sleep monitoring, the use of at-home sensor systems has gained popularity in recent years. Two categories of monitoring, autonomic and limb movement physiology, are increasingly recognized as critical for sleep disorder phenotyping, yet at-home options remain limited outside of research protocols. The purpose of this study was to validate the BiostampRC^® sensor system for respiration, electrocardiography (ECG), and leg electromyography (EMG) against gold standard PSG recordings.

METHODS

We report analysis of cardiac and respiratory data from 15 patients and anterior tibialis (AT) data from 19 patients undergoing clinical PSG for any indication who simultaneously wore BiostampRC^® sensors on the chest and the bilateral AT muscles. BiostampRC^® is a flexible, adhesive, wireless sensor capable of capturing accelerometry, ECG, and EMG. We compared BiostampRC^® data and feature extractions with those obtained from PSG.

RESULTS

The heart rate extracted from BiostampRC^® ECG showed strong agreement with the PSG (cohort root mean square error of 5 beats per minute). We found the thoracic BiostampRC^® respiratory waveform, derived from its accelerometer, accurately calculated the respiratory rate (mean average error of 0.26 and root mean square error of 1.84 breaths per minute). The AT EMG signal supported periodic limb movement detection, with area under the curve of the receiver operating characteristic curve equaling 0.88. Upon completion, 88% of subjects indicated willingness to wear BiostampRC^® at home on an exit survey.

CONCLUSION

The results demonstrate that BiostampRC^® is a tolerable and accurate method for capturing respiration, ECG, and AT EMG time series signals during overnight sleep when compared with simultaneous PSG recordings. The signal quality sufficiently supports analytics of clinical relevance. Larger longitudinal in-home studies are required to support the role of BiostampRC^® in clinical management of sleep disorders involving the autonomic nervous system and limb movements.

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

OBJECTIVE

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

APPROACH

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

MAIN RESULTS

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

SIGNIFICANCE

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