Impaired sleep quality in fibromyalgia: Detection and quantification with ECG-based cardiopulmonary coupling spectrograms

Comparative study of the SleepImage ring device and polysomnography for diagnosing obstructive sleep apnea

Purpose We aim to evaluate the diagnostic performance of the SleepImage Ring device in identifying obstructive sleep apnea (OSA) across different severity in comparison to standard polysomnography (PSG). Methods Thirty-nine patients (mean age, 56.8 ± 15.0 years; 29 [74.3%] males) were measured with the SleepImage Ring and PSG study simultaneously in order to evaluate the diagnostic performance of the SleepImage device for diagnosing OSA. Variables such as sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive value, and accuracy were calculated with PSG-AHI thresholds of 5, 15, and 30 events/h. Receiver operating characteristic curves were also built according to the above PSG-AHI thresholds. In addition, we analyzed the correlation and agreement between the apnea-hypopnea index (AHI) obtained from the two measurement devices. Results There was a strong correlation (r = 0.89, P < 0.001 and high agreement in AHI between the SleepImage Ring and standard PSG. Also, the SleepImage Ring showed reliable diagnostic capability, with areas under the receiver operating characteristic curve of 1.00 (95% CI, 0.91, 1.00), 0.90 (95% CI, 0.77, 0.97), and 0.98 (95% CI, 0.88, 1.000) for corresponding PSG-AHI of 5, 15 and 30 events/h, respectively. Conclusion The SleepImage Ring could be a clinically reliable and cheaper alternative to the gold standard PSG when aiming to diagnose OSA in adults.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00304-9.

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

Introduction: Autonomic nervous system (ANS) plays an important role in the exchange of metabolic information between organs and regulation on peripheral metabolism with obvious circadian rhythm in a healthy state. Sleep, a vital brain phenomenon, significantly affects both ANS and metabolic function. Objectives: This study investigated the relationships among sleep, ANS and metabolic function in type 2 diabetes mellitus (T2DM), to support the evaluation of ANS function through heart rate variability (HRV) metrics, and the determination of the correlated underlying autonomic pathways, and help optimize the early prevention, post-diagnosis and management of T2DM and its complications. Materials and methods: A total of 64 volunteered inpatients with T2DM took part in this study. 24-h electrocardiogram (ECG), clinical indicators of metabolic function, sleep quality and sleep staging results of T2DM patients were monitored. Results: The associations between sleep quality, 24-h/awake/sleep/sleep staging HRV and clinical indicators of metabolic function were analyzed. Significant correlations were found between sleep quality and metabolic function (|r| = 0.386 ± 0.062, p < 0.05); HRV derived ANS function showed strengthened correlations with metabolic function during sleep period (|r| = 0.474 ± 0.100, p < 0.05); HRV metrics during sleep stages coupled more tightly with clinical indicators of metabolic function [in unstable sleep: |r| = 0.453 ± 0.095, p < 0.05; in stable sleep: |r| = 0.463 ± 0.100, p < 0.05; in rapid eye movement (REM) sleep: |r| = 0.453 ± 0.082, p < 0.05], and showed significant associations with glycemic control in non-linear analysis [fasting blood glucose within 24 h of admission (admission FBG), |r| = 0.420 ± 0.064, p < 0.05; glycated hemoglobin (HbA1c), |r| = 0.417 ± 0.016, p < 0.05]. Conclusions: HRV metrics during sleep period play more distinct role than during awake period in investigating ANS dysfunction and metabolism in T2DM patients, and sleep rhythm based HRV analysis should perform better in ANS and metabolic function assessment, especially for glycemic control in non-linear analysis among T2DM patients.

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 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).

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.

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.

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.

Assessment of sleep quality using cardiopulmonary coupling analysis in patients with Parkinson's disease

Objectives

To assess the sleep quality of patients with Parkinson's disease (PD) and evaluate the effect of cardiopulmonary coupling (CPC) analysis on sleep quality and its correlation with subjective complaints in patients with PD.

Methods

Our study included 42 patients with PD and 30 healthy controls. CPC analysis and the Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the sleep quality of subjects.

Results

High-frequency coupling (HFC) and sleep efficiency were significantly lower in the PD than in the control group, whereas very low-frequency coupling (VLFC) and sleep latency were significantly higher in the PD than in the control group. PSQI scores were significantly higher in the PD than in the control group (all p < .05). The PSQI score showed a negative correlation with the HFC ratio in the PD group (r = -.478, p = .001). Factors related to the occurrence of PD with poor sleep quality were the unified Parkinson's disease rating scale (UPDRS) score and nocturia.

Conclusions

The sleep quality of patients with PD was generally decreased. CPC analysis can reflect the subjective sleep quality of patients with PD and serve as an effective sleep monitoring tool.

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

STUDY OBJECTIVES

Ambulatory tracking of sleep and sleep pathology is rapidly increasing with the introduction of wearable devices. The objective of this study was to evaluate a wearable device which used novel computational analysis of the electrocardiogram (ECG), collected over multiple nights, as a method to track the dynamics of sleep quality in health and disease.

METHODS

This study used the ECG as a primary signal, a wearable device, the M1, and an analysis of cardiopulmonary coupling to estimate sleep quality. The M1 measures trunk movements, the ECG, body position, and snoring vibrations. Data from three groups of patients were analyzed: healthy participants and people with sleep apnea and insomnia, obtained from multiple nights of recording. Analysis focused on summary measures and night-to-night variability, specifically the intraclass coefficient.

RESULTS

Data were collected from 10 healthy participants, 18 people with positive pressure-treated sleep apnea, and 20 people with insomnia, 128, 65, and 121 nights, respectively. In any participant, all nights were consecutive. High-frequency coupling (HFC), the signal biomarker of stable breathing and stable sleep, showed high intraclass coefficients (ICCs) in healthy participants and people with sleep apnea (0.83, 0.89), but only 0.66 in people with insomnia. The only statistically significant difference between weekday and weekend in healthy subjects was HFC duration: 242.8 ± 53.8 vs. 275.8 ± 57.1 minutes (89 vs. 39 total nights), F(1,126) = 9.86, p = .002.

CONCLUSIONS

The M1 and similar wearable devices provide new opportunities to measure sleep in dynamic ways not possible before. These measurements can yield new biological insights and aid clinical management.

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.

A Higher Proportion of Metabolic Syndrome in Chinese Subjects with Sleep-Disordered Breathing: A Case-Control Study Based on Electrocardiogram-Derived Sleep Analysis

Objective

The prevalence of metabolic syndrome (MS) has increased rapidly in Taiwan and worldwide. We aim to determine the association between sleep-disordered breathing (SDB) and MS in a Chinese general population.

Methods

This case-control study recruited subjects who have undergone a prospective electrocardiogram-based cardiopulmonary coupling (CPC) sleep spectrogram as part of the periodic health check-ups at the National Taiwan University Hospital. Comprehensive anthropometrics, blood biochemistry, prevalence of MS and its individual components were compared with Bonferroni correction between 40 subjects with SDB, defined as the CPC-derived apnea–hypopnea index (CPC-AHI) >5 event/hour and 80 age- and sex-matched controls, defined as CPC-AHI <1 event/hour. MS was diagnosed based on the Adult Treatment Panel III, with a modification of waist circumference for Asians.

Results

Subjects with SDB were more obese with larger waist circumferences (95.1±12.9 vs. 87.3±6.9, P < .001) and borderline higher BMI (27.0±4.9 vs. 24.3±2.5, P = .002). Waist circumference was independently associated with the presence of SDB after adjustment for BMI, systolic blood pressure and fasting blood glucose in multiple regression analyses. Subjects with SDB had a higher prevalence of central obesity (72.5% vs. 42.5%, P = .002), hyperglycemia (45.0% vs. 26.3%, P = .04), MS (45.0% vs. 22.5%, P = .01) and number of MS components (2.4 ± 1.6 vs. 1.7 ± 1.4, P = .01) than the control group. Waist circumference was significantly correlated with both CPC-AHI (r = .492, P = .0013) and PSG-AHI (r = .699, P < .0001) in the SDB group.

Conclusions

SDB was associated with a higher prevalence of MS and its individual components, notably central obesity, in a Chinese general population. Large-scale screening of high risk population with MS to identify subjects with SDB for appropriate management is warranted.

Parasympathetic reactivity in fibromyalgia and temporomandibular disorder: associations with sleep problems, symptom severity, and functional impairment

Despite evidence of autonomic disturbances in chronic multisymptom illnesses such as temporomandibular disorder (TMD) and fibromyalgia, additional work is needed to characterize the role of parasympathetic reactivity in these disorders. Given the high levels of comorbidity with psychiatric disorders characterized by stronger parasympathetic decline than controls in safe contexts (leading to higher arousal), it was hypothesized that individuals with TMD and fibromyalgia would respond similarly. In this preliminary investigation, 43 women with TMD (n = 17), TMD + fibromyalgia (n = 11), or neither (controls; n = 15) completed a baseline assessment of respiratory sinus arrhythmia (a measure of parasympathetic activity) followed by ongoing parasympathetic assessment during a questionnaire period. As predicted, patients showed greater parasympathetic decline during psychosocial assessment, suggesting an autonomic stance that supports defensive rather than engagement behaviors. Individual differences in parasympathetic reduction during the questionnaire period were related to a variety of physical and psychosocial variables. Although this study has a number of key limitations, including a convenience sampling approach and small group sizes, if replicated in larger samples, the findings would have important implications for the treatment of patients with these disorders.

PERSPECTIVE

Compared to controls, individuals with TMD or TMD and fibromyalgia demonstrated greater parasympathetic decline during psychosocial assessment, and individual differences in parasympathetic decline predicted negative patient outcomes. Such parasympathetic decline may demonstrate a tendency to readily perceive danger in safe environments.

Chronic Pain: Where the Body Meets the Brain

Chronic musculoskeletal pain is one of the most intractable clinical problems faced by clinicians and can be devastating for patients. Central pain amplification is perceived pain that cannot be fully explained on the basis of somatic or neuropathic processes and is due to physiologic alterations in pain transmission or descending pain modulatory pathways. In any individual, central pain amplification may complicate nociceptive or neuropathic pain. Furthermore, patients with somatic symptom disorders may have alterations in their psychological or behavioral responses to pain that contribute significantly to the clinical presentation. Genetic, physiologic, and psychological factors associated with central pain amplification are beginning to be understood. One important contributor to chronic pain is perceived stress and stress response systems. We and others have shown a complex relationship between the physiologic stress response and chronic pain symptoms. Unfortunately, treatments for chronic pain are woefully inadequate and often worsen clinical outcomes. Developing new treatment strategies for patients with chronic pain is of utmost urgency. This essay provides a framework for thinking about chronic pain and developing new treatment approaches.

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

OBJECTIVES

The physiologic relationship between slow-wave activity (SWA) (0-4 Hz) on the electroencephalogram (EEG) and high-frequency (0.1-0.4 Hz) cardiopulmonary coupling (CPC) derived from electrocardiogram (ECG) sleep spectrograms is not known. Because high-frequency CPC appears to be a biomarker of stable sleep, we tested the hypothesis that that slow-wave EEG power would show a relatively fixed-time relationship to periods of high-frequency CPC. Furthermore, we speculated that this correlation would be independent of conventional nonrapid eye movement (NREM) sleep stages.

METHODS

We analyzed selected datasets from an archived polysomnography (PSG) database, the Sleep Heart Health Study I (SHHS-I). We employed the cross-correlation technique to measure the degree of which 2 signals are correlated as a function of a time lag between them. Correlation analyses between high-frequency CPC and delta power (computed both as absolute and normalized values) from 3150 subjects with an apnea-hypopnea index (AHI) of ≤5 events per hour of sleep were performed.

RESULTS

The overall correlation (r) between delta power and high-frequency coupling (HFC) power was 0.40±0.18 (P=.001). Normalized delta power provided improved correlation relative to absolute delta power. Correlations were somewhat reduced in the second half relative to the first half of the night (r=0.45±0.20 vs r=0.34±0.23). Correlations were only affected by age in the eighth decade. There were no sex differences and only small racial or ethnic differences were noted.

CONCLUSIONS

These results support a tight temporal relationship between slow wave power, both within and outside conventional slow wave sleep periods, and high frequency cardiopulmonary coupling, an ECG-derived biomarker of "stable" sleep. These findings raise mechanistic questions regarding the cross-system integration of neural and cardiopulmonary control during sleep.

Technical advances in the characterization of the complexity of sleep and sleep disorders

The current clinical standard for quantifying sleep physiology is the laboratory polysomnogram, from which basic sleep-wake stages are determined. However, the complexity of sleep physiology has inspired alternative metrics that are providing additional insights into the rich dynamics of sleep. Electro-encephalography, magneto-encephalography, and functional magnetic resonance imaging represent advanced imaging modalities for understanding brain dynamics. These methods are complemented by autonomic measurements that provide additional important insights. We review here the spectrum of approaches that have been leveraged towards improved understanding of the complexity of sleep.

A Novel Home Sleep Monitoring Device and Brief Sleep Intervention for Bipolar Disorder: Feasibility, Tolerability, and Preliminary Effectiveness

Sleep disturbance is common in bipolar disorder and negatively impacts its course of illness. The purpose of this study is to assess the feasibility and tolerability of a novel EKG-based home sleep monitoring device (M1) as well as a brief (two session) psychosocial sleep intervention for individuals with bipolar disorder. The sleep intervention is individually tailored for patients with insomnia or hypersomnia and is designed to extend skills designed for non-psychiatric populations as well as include specific considerations for sleep disturbance in bipolar disorder. We found that both the M1 monitor and the sleep intervention were feasible and well tolerated. Participants' sleep duration improved after the brief sleep intervention, but the sleep was more unstable as measured by the M1. Self-reported sleepiness, sleep quality, and mood symptoms improved; however, only some measures reached statistical significance (i.e., duration of sleep, dysfunction due to sleepiness). These data suggest that the M1 device is a feasible means to obtain objective sleep quality and quantity data in individuals with bipolar disorder. A brief sleep intervention may be helpful in improving sleep in a bipolar population at risk for substantial sleep disturbance, but larger, longitudinal studies are warranted.

An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea

OBJECTIVE

The study compares polysomnography (PSG) and cardiopulmonary coupling (CPC) sleep quality variables in patients with (1) obstructive sleep apnea (OSA) and (2) successful and unsuccessful continuous positive airway pressure (CPAP) response.

PATIENTS/METHODS

PSGs from 50 subjects (32 F/18 M; mean age 48.4 ± 12.29 years; BMI 34.28 ± 9.33) were evaluated. OSA patients were grouped by no (n = 16), mild (n = 13), and moderate to severe (n = 20) OSA (apnea-hypopnea index (AHI) ≤ 5, >5-15, >15 events/h, respectively). Outcome sleep quality variables were sleep stages in non-rapid eye movement, rapid eye movement sleep, and high (HFC), low (LFC), very low-frequency coupling (VLFC), and elevated LFC broad band (e-LFCBB). An AHI ≤ 5 events/h and HFC ≥ 50 % indicated a successful CPAP response. CPC analysis extracts heart rate variability and QRS amplitude change that corresponds to respiration. CPC-generated spectrograms represent sleep dynamics from calculated coherence product and cross-power of both time series datasets.

RESULTS

T tests differentiated no and moderate to severe OSA groups by REM % (p = 0.003), HFC (p = 0.007), VLFC (p = 0.007), and LFC/HFC ratio (p = 0.038) variables. The successful CPAP therapy group (n = 16) had more HFC (p = 0.003), less LFC (p = 0.003), and e-LFCBB (p = 0.029) compared to the unsuccessful CPAP therapy group (n = 8). PSG sleep quality measures, except the higher arousal index (p = 0.038) in the unsuccessful CPAP group, did not differ between the successful and unsuccessful CPAP groups. HFC ≥ 50 % showed high sensitivity (77.8 %) and specificity (88.9 %) in identifying successful CPAP therapy.

CONCLUSIONS

PSG and CPC measures differentiated no from moderate to severe OSA groups and HFC ≥ 50 % discriminated successful from unsuccessful CPAP therapy. The HFC ≥ 50 % cutoff showed clinical value in identifying sleep quality disturbance among CPAP users.

Sleep abnormalities in chronic fatigue syndrome/myalgic encephalomyelitis: a review

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a chronic, disabling illness that affects approximately 0.2% of the population. Non-restorative sleep despite sufficient or extended total sleep time is one of the major clinical diagnostic criteria; however, the underlying cause of this symptom is unknown. This review aims to provide a comprehensive overview of the literature examining sleep in CFS/ME and the issues surrounding the current research findings. Polysomnographic and other objective measures of sleep have observed few differences in sleep parameters between CFS/ME patients and healthy controls, although some discrepancies do exist. This lack of significant objective differences contrasts with the common subjective complaints of disturbed and unrefreshed sleep by CFS/ME patients. The emergence of new, more sensitive techniques that examine the microstructure of sleep are showing promise for detecting differences in sleep between patients and healthy individuals. There is preliminary evidence that alterations in sleep stage transitions and sleep instability, and other physiological mechanisms, such as heart rate variability and altered cortisol profiles, may be evident. Future research investigating the etiology of non-restorative sleep in CFS/ME may also help us to undercover the causes of non-restorative sleep and fatigue in other medical conditions.

Recent developments in home sleep-monitoring devices

Improving our understanding of sleep physiology and pathophysiology is an important goal for both medical and general wellness reasons. Although the gold standard for assessing sleep remains the laboratory polysomnogram, there is an increasing interest in portable monitoring devices that provide the opportunity for assessing sleep in real-world environments such as the home. Portable devices allow repeated measurements, evaluation of temporal patterns, and self-experimentation. We review recent developments in devices designed to monitor sleep-wake activity, as well as monitors designed for other purposes that could in principle be applied in the field of sleep (such as cardiac or respiratory sensing). As the body of supporting validation data grows, these devices hold promise for a variety of health and wellness goals. From a clinical and research standpoint, the capacity to obtain longitudinal sleep-wake data may improve disease phenotyping, individualized treatment decisions, and individualized health optimization. From a wellness standpoint, commercially available devices may allow individuals to track their own sleep with the goal of finding patterns and correlations with modifiable behaviors such as exercise, diet, and sleep aids.

Effect of pregabalin on sleep in patients with fibromyalgia and sleep maintenance disturbance: a randomized, placebo-controlled, 2-way crossover polysomnography study

OBJECTIVE

To assess the effect of pregabalin on polysomnographic (PSG) measures of sleep and patient-rated sleep, tiredness, and pain in fibromyalgia patients.

METHODS

We performed a randomized, double-blind, placebo-controlled, 2-period crossover PSG study. Patients ages ≥18 years with fibromyalgia satisfied subjective and objective sleep disturbance criteria prior to randomization. Eligible patients were randomized (1:1) to pregabalin (300-450 mg/day) or placebo for crossover period 1, and vice versa for period 2. Each crossover period comprised a dose-adjustment and dose-maintenance phase, with a 2-week taper/washout between periods. In-laboratory PSGs were recorded during 2 consecutive nights at screening and at the end of each crossover period. The primary end point was the difference in sleep maintenance defined by PSG-recorded wake after sleep onset (WASO; minutes) between 4 weeks of treatment with pregabalin and with placebo. Other PSG measures; patient-rated sleep, tiredness, and pain; and tolerability were assessed.

RESULTS

Of 119 patients randomized (103 women [86.6%], mean age 48.4 years), 102 (85.7%) completed both periods. Patients treated with pregabalin showed a reduction in PSG-determined WASO versus treatment with placebo (week 4 difference: -19.2 minutes [95% confidence interval (95% CI) -26.7, -11.6]; P < 0.0001). Pain score improved (decreased) with pregabalin versus placebo treatment at all 4 weeks (week 4 difference: -0.52 [95% CI -0.90, -0.14]; P = 0.0084). Modest (ρ = <0.3) but significant correlations were found between PSG sleep assessments and ratings of pain and sleep quality. Frequently reported all-causality adverse events (pregabalin versus placebo) were: dizziness (30.4% versus 9.9%), somnolence (20.5% versus 4.5%), and headache (8.9% versus 8.1%).

CONCLUSION

Patients with fibromyalgia treated with pregabalin had statistically significant and meaningful improvements in sleep, as assessed by PSG. Patients with fibromyalgia also reported decreased daily pain. Pregabalin was well tolerated.

Measuring sleep quality after adenotonsillectomy in pediatric sleep apnea

OBJECTIVES/HYPOTHESIS

The aim of this study was to demonstrate postoperative changes in sleep quality in children with obstructive sleep apnea (OSA), using both conventional sleep staging and electrocardiogram-based cardiopulmonary coupling (CPC) analysis. The hypothesis is that being electroencephalography (EEG)-independent, CPC may detect changes in sleep quality that traditional sleep architecture analysis cannot.

STUDY DESIGN

Retrospective outcome research.

METHODS

We included 37 children (aged 6.89 ± 2.76 years, 28 male) with OSA who underwent adenotonsillectomy, and analyzed standard polysomnography and CPC parameters from a full-night study before and after adenotonsillectomy. High-frequency coupling (HFC) and low-frequency coupling (LFC) were used as indices of stable and unstable sleep, respectively.

RESULTS

Adenotonsillectomy led to a significant change in CPC parameters (HFC, 50.3 ± 16.1% to 56.1 ± 14.7%, P = .03; LFC, 35.1 ± 14.5% to 27.3 ± 13.0%, P = .003), which was paralleled by improvements in the apnea-hypopnea (12.7 ± 13.7 to 1.0 ± 0.8, P < .001) and arousal index (20.8 ± 11.5 to 9.9 ± 3.9, P < .001). Polysomnographic sleep stage parameters other than the arousal index did not reflect postoperative resolution of OSA.

CONCLUSIONS

In pediatric OSA, postoperative improvement of sleep quality is more readily discernible by CPC analysis than EEG-based sleep staging. The CPC analysis may have potential advantages in the assessment of sleep quality in pediatric populations.

Electrocardiogram-based sleep spectrogram measures of sleep stability and glucose disposal in sleep disordered breathing

STUDY OBJECTIVES

Sleep disordered breathing (SDB) is independently associated with insulin resistance, glucose intolerance, and type 2 diabetes mellitus. Experimental sleep fragmentation has been shown to impair insulin sensitivity. Conventional electroencephalogram (EEG)-based sleep-quality measures have been inconsistently associated with indices of glucose metabolism. This analysis explored associations between glucose metabolism and an EEG-independent measure of sleep quality, the sleep spectrogram, which maps coupled oscillations of heart-rate variability and electrocardiogram (ECG)-derived respiration. The method allows improved characterization of the quality of stage 2 non-rapid eye movement (NREM) sleep.

DESIGN

Cross-sectional study.

SETTING

N/A.

PARTICIPANTS

Nondiabetic subjects with and without SDB (n = 118) underwent the frequently sampled intravenous glucose tolerance test (FSIVGTT) and a full-montage polysomnogram. The sleep spectrogram was generated from ECG collected during polysomnography.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Standard polysomnographic stages (stages 1, 2, 3+4, and rapid eye movement [REM]) were not associated with the disposition index (D(I)) derived from the FSIVGTT. In contrast, spectrographic high-frequency coupling (a marker of stable or "effective" sleep) duration was associated with increased, and very-low-frequency coupling (a marker of wake/REM/transitions) associated with reduced D(I). This relationship was noted after adjusting for age, sex, body mass index, slow wave sleep, total sleep time, stage 1, the arousal index, and the apnea-hypopnea index.

CONCLUSIONS

ECG-derived sleep-spectrogram measures of sleep quality are associated with alterations in glucose-insulin homeostasis. This alternate mode of estimating sleep quality could improve our understanding of sleep and sleep-breathing effects on glucose metabolism.

Probabilistic sleep architecture models in patients with and without sleep apnea

Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep-wake stage distributions exhibit multi-exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease. Sleep stage data were obtained from two subsets of the Sleep Heart Health Study database: control subjects with no medications, no OSA, no medical co-morbidities and no sleepiness (n = 374); and subjects with severe OSA (n = 338). Sleep architecture was simplified into three stages: wake after sleep onset (WASO); non-rapid eye movement (NREM) sleep; and rapid eye movement (REM) sleep. The connectivity and transition rates among eight 'generator' states of a first-order continuous-time Markov model were inferred from the observed ('phenotypic') distributions: three exponentials each of NREM sleep and WASO; and two exponentials of REM sleep. Ultradian REM cycling was accomplished by imposing time-variation to REM state entry rates. Fragmentation in subjects with severe OSA involved faster transition probabilities as well as additional state transition paths within the model. The Markov models exhibit two important features of human sleep architecture: multi-exponential stage dynamics (accounting for observed bout distributions); and probabilistic transitions (an inherent source of variability). In addition, the model quantifies the fragmentation associated with severe OSA. Markov sleep models may prove important for quantifying sleep disruption to provide objective metrics to correlate with endpoints ranging from sleepiness to cardiovascular morbidity.

Classification algorithms for predicting sleepiness and sleep apnea severity

Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study. The Epworth Sleepiness Scale and the apnea-hypopnea index were the targets of three classifiers: k-nearest neighbor, naive Bayes and support vector machine algorithms. Classification was based on up to 26 features including demographics, polysomnogram, and electrocardiogram (spectrogram). Naive Bayes was best for predicting abnormal Epworth class (0-10 versus 11-24), although prediction was weak: polysomnogram features had 16.7% sensitivity and 88.8% specificity; spectrogram features had 5.3% sensitivity and 96.5% specificity. The support vector machine performed similarly to naive Bayes for predicting sleep apnea class (0-5 versus >5): 59.0% sensitivity and 74.5% specificity using clinical features and 43.4% sensitivity and 83.5% specificity using spectrographic features compared with the naive Bayes classifier, which had 57.5% sensitivity and 73.7% specificity (clinical), and 39.0% sensitivity and 82.7% specificity (spectrogram). Mutual information analysis confirmed the minimal dependency of the Epworth score on any feature, while the apnea-hypopnea index showed modest dependency on body mass index, arousal index, oxygenation and spectrogram features. Apnea classification was modestly accurate, using either clinical or spectrogram features, and showed lower sensitivity and higher specificity than common sleep apnea screening tools. Thus, clinical prediction of sleep apnea may be feasible with easily obtained demographic and electrocardiographic analysis, but the utility of the Epworth is questioned by its minimal relation to clinical, electrocardiographic, or polysomnographic features.

Heart rate variability, sleep and sleep disorders

Heart rate (HR) is modulated by the combined effects of the sympathetic and parasympathetic nervous systems. Therefore, measurement of changes in HR over time (heart rate variability or HRV) provides information about autonomic functioning. HRV has been used to identify high risk people, understand the autonomic components of different disorders and to evaluate the effect of different interventions, etc. Since the signal required to measure HRV is already being collected on the electrocardiogram (ECG) channel of the polysomnogram (PSG), collecting data for research on HRV and sleep is straightforward, but applications have been limited. As reviewed here, HRV has been applied to understand autonomic changes during different sleep stages. It has also been applied to understand the effect of sleep-disordered breathing, periodic limb movements and insomnia both during sleep and during the daytime. HRV has been successfully used to screen people for possible referral to a Sleep Lab. It has also been used to monitor the effects of continuous positive airway pressure (CPAP). A novel HRV measure, cardiopulmonary coupling (CPC) has been proposed for sleep quality. Evidence also suggests that HRV collected during a PSG can be used in risk stratification models, at least for older adults. Caveats for accurate interpretation of HRV are also presented.

Cyclic alternating pattern (CAP): the marker of sleep instability

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders.