Electroencephalographic Changes during Respiratory Cycles Predict Sleepiness in Sleep Apnea

All Obstructive Sleep Apnea Events Are Not Created Equal: The Relationship between Event-related Hypoxemia and Physiologic Response

Rationale: Obstructive sleep apnea (OSA) severity is typically assessed by the apnea-hypopnea index (AHI), a frequency-based metric that allocates equal weight to all respiratory events. However, more severe events may have a greater physiologic impact. Objectives: The purpose of this study was to determine whether the degree of event-related hypoxemia would be associated with the postevent physiologic response. Methods: Patients with OSA (AHI, ⩾5/h) from the multicenter Canadian Sleep and Circadian Network cohort were studied. Using mixed-effect linear regression, we examined associations between event-related hypoxic burden (HBev) assessed by the area under the event-related oxygen saturation recording with heart rate changes (ΔHRev), vasoconstriction (vasoconstriction burden [VCBev] assessed with photoplethysmography), and electroencephalographic responses (power ratio before and after events). Results: Polysomnographic recordings from 658 patients (median [interquartile range] age, 55.00 [45.00, 64.00] yr; AHI, 27.15 [14.90, 64.05] events/h; 42% female) were included in the analyses. HBev was associated with an increase in all physiologic responses after controlling for age, sex, body mass index, sleep stage, total sleep time, and study centers; for example, 1 standard deviation increase in HBev was associated with 0.21 [95% confidence interval, 0.2, 0.22], 0.08 [0.08, 0.09], and 0.22 [0.21, 0.23] standard deviation increases in ΔHRev, VCBev, and β-power ratio, respectively. Conclusions: Increased event-related hypoxic burden was associated with greater responses across a broad range of physiologic signals. Future metrics that incorporate information about the variability of these physiologic responses may have promise in providing a more nuanced assessment of OSA severity.

Polysomnographic characteristics of excessive daytime sleepiness phenotypes in obstructive sleep apnea: results from the international sleep apnea global interdisciplinary consortium

STUDY OBJECTIVES

Excessive daytime sleepiness (EDS) is a major symptom of obstructive sleep apnea (OSA). Traditional polysomnographic (PSG) measures only partially explain EDS in OSA. This study analyzed traditional and novel PSG characteristics of two different measures of EDS among patients with OSA.

METHODS

Sleepiness was assessed using the Epworth Sleepiness Scale (>10 points defined as "risk of dozing") and a measure of general sleepiness (feeling sleepy ≥ 3 times/week defined as "feeling sleepy"). Four sleepiness phenotypes were identified: "non-sleepy," "risk of dozing only," "feeling sleepy only," and "both at risk of dozing and feeling sleepy."

RESULTS

Altogether, 2083 patients with OSA (69% male) with an apnea-hypopnea index (AHI) ≥ 5 events/hour were studied; 46% were "non-sleepy," 26% at "risk of dozing only," 7% were "feeling sleepy only," and 21% reported both. The two phenotypes at "risk of dozing" had higher AHI, more severe hypoxemia (as measured by oxygen desaturation index, minimum and average oxygen saturation [SpO2], time spent < 90% SpO2, and hypoxic impacts) and they spent less time awake, had shorter sleep latency, and higher heart rate response to arousals than "non-sleepy" and "feeling sleepy only" phenotypes. While statistically significant, effect sizes were small. Sleep stages, frequency of arousals, wake after sleep onset and limb movement did not differ between sleepiness phenotypes after adjusting for confounders.

CONCLUSIONS

In a large international group of patients with OSA, PSG characteristics were weakly associated with EDS. The physiological measures differed among individuals characterized as "risk of dozing" or "non-sleepy," while "feeling sleepy only" did not differ from "non-sleepy" individuals.

Neurobehavioral morbidity of pediatric mild sleep-disordered breathing and obstructive sleep apnea

STUDY OBJECTIVES

Obstructive sleep apnea is associated with neurobehavioral dysfunction, but the relationship between disease severity as measured by the apnea-hypopnea index and neurobehavioral morbidity is unclear. The objective of our study is to compare the neurobehavioral morbidity of mild sleep-disordered breathing versus obstructive sleep apnea.

METHODS

Children 3-12 years old recruited for mild sleep-disordered breathing (snoring with obstructive apnea-hypopnea index < 3) into the Pediatric Adenotonsillectomy Trial for Snoring were compared to children 5-9 years old recruited for obstructive sleep apnea (obstructive apnea-hypopnea 2-30) into the Childhood Adenotonsillectomy Trial. Baseline demographic, polysomnographic, and neurobehavioral outcomes were compared using univariable and multivariable analysis.

RESULTS

The sample included 453 participants with obstructive sleep apnea (median obstructive apnea-hypopnea index 5.7) and 459 participants with mild sleep-disordered breathing (median obstructive apnea-hypopnea index 0.5). By polysomnography, participants with obstructive sleep apnea had poorer sleep efficiency and more arousals. Children with mild sleep-disordered breathing had more abnormal executive function scores (adjusted odds ratio 1.96, 95% CI 1.30-2.94) compared to children with obstructive sleep apnea. There were also elevated Conners scores for inattention (adjusted odds ratio 3.16, CI 1.98-5.02) and hyperactivity (adjusted odds ratio 2.82, CI 1.83-4.34) in children recruited for mild sleep-disordered breathing.

CONCLUSIONS

Abnormal executive function, inattention, and hyperactivity were more common in symptomatic children recruited into a trial for mild sleep-disordered breathing compared to children recruited into a trial for obstructive sleep apnea. Young, snoring children with only minimally elevated apnea-hypopnea levels may still be at risk for deficits in executive function and attention.

TRIAL REGISTRATION

Pediatric Adenotonsillectomy for Snoring (PATS), NCT02562040; Childhood Adenotonsillectomy Trial (CHAT), NCT00560859.

The relationship between snoring sounds and EEG signals on polysomnography

PURPOSE

The aim of this study was to analyze the relationship of snoring sound signals obtained by polysomnography (PSG) in the sleep laboratory with cortical EEG (6 channel) signals to find answers to two important questions that have been covered to a limited extent in the literature: (1) Would the sounds generated by a snoring individual have an effect on the cerebral electrical waves occurring during sleep (specifically deep restorative sleep)? (2) Would the snoring sounds of an individual being examined by PSG have more of an effect on any one of the EEG electrodes?

METHODS

PSG recordings were obtained from volunteers with primary snoring and those with obstructive sleep apnea syndrome (OSAS) on six different EEG channels (F4-M1, C4-M1, and O2-M1, F3-M2, C3-M2, and O1-M2). The relationship of each of these recordings and snoring sound signals was analyzed by using a computer-based electrophysiological signal analysis method. A three-tier approach was used in this relationship: "Feature extraction, Feature selection, and Classification".

RESULTS

Data were obtained from a total of 40 volunteers (32 men, mean age (± SD) 47.5 ± 3.2 years), 20 with primary snoring and 20 with OSAS. The discrete wavelet transform (DWT) feature extraction method was the most successful method, and by utilizing this method for analyzing EEG channels, snoring sound signals were found to affect the C3-M2 channel the most (Duncan test, p < 0.05). Delta wave frequency levels during snoring were decreased compared to both before snoring (p = 0.160) and after snoring (p = 0.04) periods (paired sample test).

DISCUSSION

When snoring sounds and EEG signals were analyzed for frequency, time, and wave conversion with feature extraction methods, the C3-M2 channel was to be found the most affected channel. The sleep physiologist who made the PSG analyses reported that, among the 6 EEG channels analyzed for periods where there was no apnea or hypopnea events but only snoring, C3-M2 was the channel showing changes in delta wave activity.

CONCLUSION

Our study showed that the monotonous and repetitive snoring sounds of the snorer do not wake the individual, but do affect deep restorative sleep (N3). PSG signal analysis revealed that the most significant changes were in the C3-M2 channel (N3 delta wave amplitude increase and frequency decrease during snoring). Thus, clinicians may be able to monitor the characteristic changes occuring in large cortical delta waves in snoring individuals with innovative single-channel EEG devices without microphones.

Relationship between the Spectral Power Density of Sleep Electroencephalography and Psychiatric Symptoms in Patients with Breathing-related Sleep Disorder

Objective

Patients with breathing-related sleep disorder (BRSD) often complain of psychiatric symptoms such as depression in addition to snoring, excessive sleepiness, and disturbed sleep. However, the relationship between psychiatric symptoms and severity of sleep apnea in BRSD is controversial. We conducted this study to investigate the relationship between psychiatric symptoms and sleep electroencephalography (EEG) findings in BRSD patients using spectral analysis.

Methods

All participants underwent polysomnography and evaluation using Symptom Checklist-90-Revised (SCL-90-R) scale. We analyzed the absolute spectral power density values of standard EEG frequency bands in the participants (n = 169) with BRSD during the non-rapid eye movement (NREM) sleep period. We performed correlation analysis between the domain scores of SCL-90-R scale and the absolute values of the EEG frequency bands.

Results

Significant positive correlation was observed between the absolute spectral power density values in the slow oscillation band and the degree of paranoid ideation (r = 0.226, p = 0.028) and depression (r = 0.216, p = 0.044) in SCL-90-R. The multiple linear regression model showed that higher paranoid ideation domain score (B = 0.007, p = 0.020), younger age (B = −0.011, p < 0.001), and female sex (B = 0.213, p = 0.004) were associated with higher slow oscillation power during NREM sleep.

Conclusion

The results of the present study suggested a relationship between sleep EEG and psychiatric symptoms in patients with BRSD. This relationship needs to be validated with further studies.

Sleep Spindle Characteristics in Obstructive Sleep Apnea Syndrome (OSAS)

Background: We compared the density and duration of sleep spindles topographically in stage 2 and 3 of non-rapid eye movement sleep (N2 and N3) among adults diagnosed with Obstructive Sleep Apnea Syndrome (OSAS) and healthy controls.

Materials and Methods: Thirty-one individuals with OSAS (mean age: 48.50 years) and 23 healthy controls took part in the study. All participants underwent a whole night polysomnography. Additionally, those with OSAS were divided into mild, moderate and severe cases of OSAS.

Results: For N2, sleep spindle density did not significantly differ between participants with and without OSAS, or among those with mild, moderate and severe OSAS. For N3, post-hoc analyses revealed significantly higher spindle densities in healthy controls and individuals with mild OSAS than in those with moderate or severe OSAS. Last, in N2 a higher AHI was associated with a shorter sleep spindle duration.

Conclusion: OSAS is associated with a significantly lower spindle density in N3 and a shorter spindle duration in N2. Our results also revealed that, in contrast to moderate and severe OSAS, the sleep spindle characteristics of individuals with mild OSAS were very similar to those of healthy controls.

Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology

Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.

Artificial intelligence in sleep medicine: background and implications for clinicians

None

Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.

Difference in spectral power density of sleep EEG between patients with simple snoring and those with obstructive sleep apnoea

Patients with simple snoring (SS) often complain of poor sleep quality despite a normal apnoea-hypopnoea index (AHI). We aimed to identify the difference in power spectral density of electroencephalography (EEG) between patients with SS and those with obstructive sleep apnoea (OSA). We compared the absolute power spectral density values of standard EEG frequency bands between the SS (n = 42) and OSA (n = 129) groups during the non-rapid eye movement (NREM) sleep period, after controlling for age and sex. We also analysed partial correlation between AHI and the absolute values of the EEG frequency bands. The absolute power spectral density values in the beta and delta bands were higher in the OSA group than in the SS group. AHI also positively correlated with beta power in the OSA group as well as in the combined group (OSA + SS). In conclusion, higher delta and beta power during NREM sleep were found in the OSA group than in the SS group, and beta power was correlated with AHI. These findings are microstructural characteristics of sleep-related breathing disorders.

Intra-individual stability of NREM sleep quantitative EEG measures in obstructive sleep apnea

Electroencephalography is collected routinely during clinical polysomnography, but is often utilised to simply determine sleep time to calculate apnea-hypopnea indices. Quantitative analysis of these data (quantitative electroencephalogram) may provide trait-like information to predict patient vulnerability to sleepiness. Measurements of trait-like characteristics need to have high test-retest reliability. We aimed to investigate the intra-individual stability of slow-wave (delta power) and spindle frequency (sigma power) activity during non-rapid eye movement sleep in patients with obstructive sleep apnea. We recorded sleep electroencephalograms during two overnight polysomnographic recordings in 61 patients with obstructive sleep apnea (median days between studies 47, inter-quartile range 53). Electroencephalograms recorded at C3-M2 derivation were quantitatively analysed using power spectral analysis following artefact removal. Relative delta (0.5-4.5 Hz) and sigma (12-15 Hz) power during non-rapid eye movement sleep were calculated. Intra-class correlation coefficients and Bland-Altman plots were used to assess agreement between nights. Intra-class correlation coefficients demonstrated good-to-excellent agreement in the delta and sigma frequencies between nights (intra-class correlation coefficients: 0.84, 0.89, respectively). Bland-Altman analysis of delta power showed a mean difference close to zero (-0.4, 95% limits of agreement -9.4, 8.7) and no heteroscedasticity with increasing power. Sigma power demonstrated heteroscedasticity, with reduced stability as sigma power increased. The mean difference of sigma power between nights was close to zero (0.1, 95% limits -1.6, 1.8). We have demonstrated the stability of slow-wave and spindle frequency electroencephalograms during non-rapid eye movement sleep within patients with obstructive sleep apnea. The electroencephalogram profile during non-rapid eye movement sleep may be a useful biomarker for predicting vulnerability to daytime impairment in obstructive sleep apnea and responsiveness to treatment.

Sleep disturbances and their impact in pediatric cystic fibrosis

Cystic fibrosis is a chronic, life-shortening illness that affects multiple systems and results in frequent respiratory infections, chronic cough, fat malabsorption and malnutrition. Poor sleep is often reported by patients with cystic fibrosis. Although objective data to explain these complaints have been limited, they do show poor sleep efficiency and frequent arousals. Abnormalities in gas exchange are also observed during sleep in patients with cystic fibrosis. The potential impact of these abnormalities in sleep on health and quality of life remains largely unstudied. This review summarizes what is known about sleep in children with cystic fibrosis, and implications for clinical practice. This report also highlights new evidence on the impact of sleep problems on disease-specific outcomes such as lung function, and identifies areas that need further exploration.

Big data in sleep medicine: prospects and pitfalls in phenotyping

Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea-hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis

During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible-elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.

Quantitative electroencephalogram measures in adult obstructive sleep apnea - Potential biomarkers of neurobehavioural functioning

Obstructive sleep apnea (OSA) results in significantly impaired cognitive functioning and increased daytime sleepiness in some patients leading to increased risk of motor vehicle and workplace accidents and reduced productivity. Clinicians often face difficulty in identifying which patients are at risk of neurobehavioural dysfunction due to wide inter-individual variability, and disparity between symptoms and conventional metrics of disease severity such as the apnea hypopnea index. Quantitative electroencephalogram (EEG) measures are determinants of awake neurobehavioural function in healthy subjects. However, the potential value of quantitative EEG (qEEG) measurements as biomarkers of neurobehavioural function in patients with OSA has not been examined. This review summarises the existing literature examining qEEG in OSA patients including changes in brain activity during wake and sleep states, in relation to daytime sleepiness, cognitive impairment and OSA treatment. It will speculate on the mechanisms which may underlie changes in EEG activity and discuss the potential utility of qEEG as a clinically useful predictor of neurobehavioural function in OSA.

Spindle Oscillations in Sleep Disorders: A Systematic Review

Measurement of sleep microarchitecture and neural oscillations is an increasingly popular technique for quantifying EEG sleep activity. Many studies have examined sleep spindle oscillations in sleep-disordered adults; however reviews of this literature are scarce. As such, our overarching aim was to critically review experimental studies examining sleep spindle activity between adults with and without different sleep disorders. Articles were obtained using a systematic methodology with a priori criteria. Thirty-seven studies meeting final inclusion criteria were reviewed, with studies grouped across three categories: insomnia, hypersomnias, and sleep-related movement disorders (including parasomnias). Studies of patients with insomnia and sleep-disordered breathing were more abundant relative to other diagnoses. All studies were cross-sectional. Studies were largely inconsistent regarding spindle activity differences between clinical and nonclinical groups, with some reporting greater or less activity, while many others reported no group differences. Stark inconsistencies in sample characteristics (e.g., age range and diagnostic criteria) and methods of analysis (e.g., spindle bandwidth selection, visual detection versus digital filtering, absolute versus relative spectral power, and NREM2 versus NREM3) suggest a need for greater use of event-based detection methods and increased research standardization. Hypotheses regarding the clinical and empirical implications of these findings, and suggestions for potential future studies, are also discussed.

Chronic intermittent hypoxia is independently associated with reduced postoperative opioid consumption in bariatric patients suffering from sleep-disordered breathing

Background

Evidence suggests that recurrent nocturnal hypoxemia may affect pain response and/or the sensitivity to opioid analgesia. We tested the hypothesis that nocturnal hypoxemia, quantified by sleep time spent at an arterial saturation (SaO₂) < 90% and minimum nocturnal SaO₂ on polysomnography, are associated with decreased pain and reduced opioid consumption during the initial 72 postoperative hours in patients having laparoscopic bariatric surgery.

Methods

With Institutional Review Board approval, we examined the records of all patients who underwent laparoscopic bariatric surgery between 2004 and 2010 and had an available nocturnal polysomnography study. We assessed the relationships between the time-weighted average of pain score and total opioid consumption during the initial 72 postoperative hours, and: (a) the percentage of total sleep time spent at SaO₂ < 90%, (b) the minimum nocturnal SaO₂, and (c) the number of apnea/hypopnea episodes per hour of sleep. We used multivariable regression models to adjust for both clinical and sleep-related confounders.

Results

Two hundred eighteen patients were included in the analysis. Percentage of total sleep time spent at SaO₂ < 90% was inversely associated with total postoperative opioid consumption; a 5-%- absolute increase in the former would relatively decrease median opioid consumption by 16% (98.75% CI: 2% to 28%, P = 0.006). However, the percentage of total sleep time spent at SaO₂ < 90% was not associated with pain. The minimum nocturnal SaO₂ was associated neither with total postoperative opioid consumption nor with pain. In addition, neither pain nor total opioid consumption was significantly associated with the number of apnea/hypopnea episodes per hour of sleep.

Conclusions

Preoperative nocturnal intermittent hypoxia may enhance sensitivity to opioids.

An automated algorithm to identify and reject artefacts for quantitative EEG analysis during sleep in patients with sleep-disordered breathing

PURPOSE

Large quantities of neurophysiological electroencephalogram (EEG) data are routinely collected in the sleep laboratory. These are underutilised due to the burden of managing artefact contamination. The aim of this study was to develop a new tool for automated artefact rejection that facilitates subsequent quantitative analysis of sleep EEG data collected during routine overnight polysomnography (PSG) in subjects with and without sleep-disordered breathing (SDB).

METHODS

We evaluated the accuracy of an automated algorithm to detect sleep EEG artefacts against artefacts manually scored by three experienced technologists (reference standard) in 40 PSGs. Spectral power was computed using artefact-free EEG data derived from (1) the reference standard, (2) the algorithm and (3) raw EEG without any prior artefact rejection.

RESULTS

The algorithm showed a high level of accuracy of 94.3, 94.7 and 95.8% for detecting artefacts during the entire PSG, NREM sleep and REM sleep, respectively. There was good to moderate sensitivity and excellent specificity of the algorithm detection capabilities during sleep. The EEG spectral power for the reference standard and algorithm was significantly lower than that of the raw, unprocessed EEG signal.

CONCLUSIONS

These preliminary findings support an automated way to process EEG artefacts during sleep, providing the opportunity to investigate EEG-based markers of neurobehavioural impairment in sleep disorders in future studies.

Do respiratory cycle-related EEG changes or arousals from sleep predict neurobehavioral deficits and response to adenotonsillectomy in children?

STUDY OBJECTIVES

Pediatric obstructive sleep apnea (OSA) is associated with hyperactive behavior, cognitive deficits, psychiatric morbidity, and sleepiness, but objective polysomnographic measures of OSA presence or severity among children scheduled for adenotonsillectomy have not explained why. To assess whether sleep fragmentation might explain neurobehavioral outcomes, we prospectively assessed the predictive value of standard arousals and also respiratory cycle-related EEG changes (RCREC), thought to reflect inspiratory microarousals.

METHODS

Washtenaw County Adenotonsillectomy Cohort II participants included children (ages 3-12 years) scheduled for adenotonsillectomy, for any clinical indication. At enrollment and again 7.2 ± 0.9 (SD) months later, children had polysomnography, a multiple sleep latency test, parent-completed behavioral rating scales, cognitive testing, and psychiatric evaluation. The RCREC were computed as previously described for delta, theta, alpha, sigma, and beta EEG frequency bands.

RESULTS

Participants included 133 children, 109 with OSA (apnea-hypopnea index [AHI] ≥ 1.5, mean 8.3 ± 10.6) and 24 without OSA (AHI 0.9 ± 0.3). At baseline, the arousal index and RCREC showed no consistent, significant associations with neurobehavioral morbidities, among all subjects or the 109 with OSA. At follow-up, the arousal index, RCREC, and neurobehavioral measures all tended to improve, but neither baseline measure of sleep fragmentation effectively predicted outcomes (all p > 0.05, with only scattered exceptions, among all subjects or those with OSA).

CONCLUSION

Sleep fragmentation, as reflected by standard arousals or by RCREC, appears unlikely to explain neurobehavioral morbidity among children who undergo adenotonsillectomy.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, ID: NCT00233194.

Respiratory cycle-related electroencephalographic changes during sleep in healthy children and in children with sleep disordered breathing

STUDY OBJECTIVE

To investigate respiratory cycle-related electroencephalographic changes (RCREC) in healthy children and in children with sleep disordered breathing (SDB) during scored event-free (SEF) breathing periods of sleep.

DESIGN

Interventional case-control repeated measurements design.

SETTING

Paediatric sleep laboratory in a hospital setting.

PARTICIPANTS

Forty children with SDB and 40 healthy, age- and sex-matched children.

INTERVENTIONS

Adenotonsillectomy in children with SDB and no intervention in controls.

MEASUREMENTS AND RESULTS

Overnight polysomnography; electroencephalography (EEG) power variations within SEF respiratory cycles in the overall and frequency band-specific EEG within stage 2 nonrapid eye movement (NREM) sleep, slow wave sleep (SWS), and rapid eye movement (REM) sleep. Within both groups there was a decrease in EEG power during inspiration compared to expiration across all sleep stages. Compared to controls, RCREC in children with SDB in the overall EEG were significantly higher during REM and frequency band specific RCRECs were higher in the theta band of stage 2 and REM sleep, alpha band of SWS and REM sleep, and sigma band of REM sleep. This between-group difference was not significant postadenotonsillectomy.

CONCLUSION

The presence of nonrandom respiratory cycle-related electroencephalographic changes (RCREC) in both healthy children and in children with sleep disordered breathing (SDB) during NREM and REM sleep has been demonstrated. The RCREC values were higher in children with SDB, predominantly in REM sleep and this difference reduced after adenotonsillectomy.

CITATION

Immanuel SA, Pamula Y, Kohler M, Martin J, Kennedy D, Saint DA, Baumert M. Respiratory cycle-related electroencephalographic changes during sleep in healthy children and in children with sleep disordered breathing.

Real-time prediction of disordered breathing events in people with obstructive sleep apnea

PURPOSE

Conventional therapies for obstructive sleep apnea (OSA) are effective but suffer from poor patient adherence and may not fully alleviate major OSA-associated cardiovascular risk factors or improve certain aspects of quality of life. Predicting the onset of disordered breathing events in OSA patients may lead to improved strategies for treating OSA and inform our understanding of underlying disease mechanisms. In this work, we describe a deployable system capable of performing real-time predictions of sleep disordered breathing events in patients diagnosed with OSA, providing a novel approach for gaining insight into OSA pathophysiology, discovering population subgroups, and improving therapies.

METHODS

LArge Memory STorage and Retrieval artificial neural networks with 864 different configurations were applied to polysomnogram records from 64 patients. Wavelet transforms, measures of entropy, and other statistics were applied to six physiological signals to provide network inputs. Approximate statistical tests were used to determine the best performing network for each patient. The most important predictors of disordered breathing events in OSA patients were determined by analyzing internal network parameters.

RESULTS

The average optimized individual prediction sensitivity and specificity were 0.81 and 0.77, respectively. Predictions were better than random guessing for all OSA patients. Analysis of internal network parameters revealed a high degree of heterogeneity among disordered breathing event predictors and may reveal patient subgroups.

CONCLUSIONS

We report the first practical system to predict individual disordered breathing events in a heterogeneous group of patients diagnosed with OSA. The pattern of disordered breathing predictors suggests variable underlying pathophysiological mechanisms and highlights the need for an individualized approach to OSA diagnosis, therapy, and management.

Respiratory-cycle related analysis of the EEG-spectrum during sleep: a healthy population study

Recent research has shown the EEG's spectral changes that occur in synchrony with the respiratory-cycle. During wakefulness, and for healthy subjects it is reported that the EEG power in several frequency bands changes between the expiratory and inspiratory phases. For sleep-disordered breathing (SDB) patients, it is reported that the amplitude of changes in normalized EEG power (referred to as respiratory-cycle related EEG changes RCREC) within a respiratory-cycle decreases after a successful intervention to alleviate the SDB condition. In this paper, we focus on analyzing the changes in the sleep’s EEG spectrum related to the respiratory-cycle for a healthy population comprising 39 subjects. For 3 sleep stages (N2, N3, REM), 6 EEG channels, and 7 frequency bands, two types of EEG spectral analyzes were considered: 1) the ratio between the EEG power during expiration and that during inspiration, and 2) the RCREC. For the first type of analysis and at the population level, no statistically significant difference was found between the EEG power during expiration and that during inspiration. For the second type of analysis, the RCREC for all conditions is at a level that is statistically significantly larger than 0.1. The latter being the value at which the RCREC decreased after successful SDB intervention.

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.

Do snoring sounds arouse the snorer?

STUDY OBJECTIVES

Respiratory cycle-related electroencephalographic (EEG) changes (RCREC), especially in delta and sigma frequencies, are thought to reflect subtle, breath-to-breath inspiratory microarousals that are exacerbated in association with increased work of breathing in obstructive sleep apnea (OSA). We wondered whether snoring sounds could create these microarousals, and investigated whether earplugs, anticipated to alter snoring perception, might affect RCREC.

DESIGN

Randomized controlled trial.

SETTING

An accredited, academic sleep laboratory.

PATIENTS

Adults (n = 400) referred for suspected OSA.

INTERVENTIONS

Subjects were randomly assigned to use earplugs or not during a night of diagnostic polysomnography.

RESULTS

Two hundred three of the participants were randomized to use earplugs. Earplug use was associated with lower RCREC in delta EEG frequencies (0.5-4.5 Hz), although not in other frequencies, after controlling for potential confounds (P = 0.048). This effect of earplug use was larger among men in comparison with women (interaction term P = 0.046), and possibly among nonobese subjects in comparison with obese subjects (P = 0.081). However, the effect of earplug use on delta RCREC did not differ significantly based on apnea severity or snoring prominence as rated by sleep technologists (P > 0.10 for each).

CONCLUSIONS

This randomized controlled trial is the first study to show that perception of snoring sounds, as modulated by earplugs, can influence the cortical EEG during sleep. However, the small magnitude of effect, lack of effect on RCREC in EEG frequencies other than delta, and absence of effect modulation by apnea severity or snoring prominence suggest that perception of snoring is not the main explanation for RCREC.

Esophageal pressures, polysomnography, and neurobehavioral outcomes of adenotonsillectomy in children

BACKGROUND

Esophageal pressure monitoring during polysomnography in children offers a gold-standard, “preferred” assessment for work of breathing, but is not commonly used in part because prospective data on incremental clinical utility are scarce. We compared a standard pediatric apnea/hypopnea index to quantitative esophageal pressures as predictors of apnea-related neurobehavioral morbidity and treatment response.

METHODS

Eighty-one children aged 7.8 ± 2.8 (SD) years, including 44 boys, had traditional laboratory-based pediatric polysomnography, esophageal pressure monitoring, multiple sleep latency tests, psychiatric evaluations, parental behavior rating scales, and cognitive testing, all just before clinically indicated adenotonsillectomy, and again 7.2 ± 0.8 months later. Esophageal pressures were used, along with nasal pressure monitoring and oronasal thermocouples, not only to identify respiratory events but also more quantitatively to determine the most negative esophageal pressure recorded and the percentage of sleep time spent with pressures lower than -10 cm H(2)O.

RESULTS

Both sleep-disordered breathing and neurobehavioral measures improved after surgery. At baseline, one or both quantitative esophageal pressure measures predicted a disruptive behavior disorder (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition-defined attention-deficit/hyperactivity disorder, conduct disorder, or oppositional defiant disorder) and more sleepiness and their future improvement after adenotonsillectomy (each P < .05). The pediatric apnea/hypopnea index did not predict these morbidities or treatment outcomes (each P > .10). The addition of respiratory effort-related arousals to the apnea/hypopnea index did not improve its predictive value. Neither the preoperative apnea/hypopnea index nor esophageal pressures predicted baseline hyperactive behavior, cognitive performance, or their improvement after surgery.

CONCLUSIONS

Quantitative esophageal pressure monitoring may add predictive value for some, if not all, neurobehavioral outcomes of sleep-disordered breathing.

Factors associated with excessive daytime sleepiness in patients with severe obstructive sleep apnea

PURPOSE

Although excessive daytime sleepiness (EDS) is one of the key symptoms of obstructive sleep apnea (OSA), associations between OSA and EDS have been inconsistent, even in patients with severe OSA. To that end, our goal was to investigate factors associated with EDS based on the Epworth Sleepiness Scale (ESS) score in a large clinical population with severe OSA (apnea-hypopnea index ≥30).

METHODS

This cross-sectional study included 1,126 consecutive adult patients referred for their first in-laboratory polysomnogram for suspicion of OSA. All patients completed a routine questionnaire including demographics, race, co-morbidities, sleep history, ESS, short-form quality of life questionnaire-12 (SF-12), the Center for Epidemiologic Studies Depression scale, and medications used. Severe OSA was diagnosed in 498 patients. After excluding patients taking narcotics, hypnotics, benzodiazepines, antidepressants, or those with diagnosis of depression, 355 patients remained in the final analytic cohort. Patients were divided into quartiles based on the ESS and comparisons were made between the lowest quartile (ESS ≤ 6; n = 105) and highest quartile (ESS ≥ 13; n = 97).

RESULTS

Compared to the ESS ≤ 6 group, patients in the ESS ≥ 13 group had a significantly higher 3 % oxygen desaturation index and a significantly lower oxygen saturation nadir during sleep (p < 0.05). Moreover, patients with severe OSA in the highest quartile of ESS had higher depressive symptomatology.

CONCLUSIONS

In patients with severe OSA, intermittent hypoxemia and depressive symptoms are important contributing factors to EDS.

Respiratory cycle-related EEG changes: response to CPAP

STUDY OBJECTIVES

Respiratory cycle-related EEG changes (RCREC) quantify statistically significant synchrony between respiratory cycles and EEG spectral power, vary to some extent with work of breathing, and may help to predict sleepiness in patients with obstructive sleep apnea. This study was designed to assess the acute response of RCREC to relief of upper airway obstruction by positive airway pressure (PAP).

DESIGN

Comparison of RCREC between baseline diagnostic polysomnograms and PAP titration studies.

SETTING

Accredited academic sleep disorders center.

PATIENTS

Fifty adults referred for suspected sleep disordered breathing.

INTERVENTIONS

For each recording, the RCREC in specific physiologic EEG frequency ranges were computed as previously described for the last 3 h of sleep not occupied by apneic events.

RESULTS

The sample included 27 women; mean age was 47 ± 11 (SD) years; and median respiratory disturbance index at baseline was 24 (inter-quartile range 15-43). Decrements in RCREC, from baseline to PAP titration, reached 43%, 24%, 14%, 22%, and 31% for delta (P = 0.0004), theta (P = 0.01), alpha (P = 0.10), sigma (P = 0.08), and beta (P = 0.01) EEG frequency ranges, respectively. Within each specific sleep stage, these reductions from baseline to PAP studies in synchrony between EEG power and respiratory cycles still reached significance (P < 0.05) for one or more EEG frequency ranges and for all frequency ranges during REM sleep.

CONCLUSIONS

RCREC tends to diminish acutely with alleviation of upper airway obstruction by PAP. These data in combination with previous observations support the hypothesis that RCREC reflect numerous, subtle, brief, but consequential inspiratory microarousals.

[Diagnostic criteria and treatment for sleep-disordered breathing: RERA]

In polysomnography, RERA is defined as a respiratory parameter that indicates an arousal associated with a respiratory event and an increase in respiratory effort. Initially, RERA was described by means of esophageal manometry for the evaluation of respiratory effort. This greater respiratory effort occurs as a response to an increase in upper airway resistance, which is a factor present in the pathophysiology of sleep-disordered breathing, such as obstructive sleep apnea syndrome and upper airway resistance syndrome. Later, the use of a nasal pressure cannula was reported to be a reliable means of identifying airflow limitation and one that is more sensitive than is a thermistor. In addition, the nasal pressure cannula method has been used as a surrogate for esophageal manometry in the identification of periods in which upper airway resistance increases. Consequently, the American Academy of Sleep Medicine recommend the use of either method for the identification of RERA, which is defined by the flattening of the inspiratory curve, characteristic of airflow limitation. Although RERA has been identified and evaluated in current medical practice, it has yet to be standardized. Therefore, it is recommended that polysomnographic reports indicate which abnormal respiratory events were taken into consideration in the evaluation of the severity of sleep-disordered breathing.

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.

Engineering better sleep

Sleep medicine is a growing field with multidisciplinary origins in physiological monitoring techniques, on which it still largely depends. Collaborations between engineers and sleep specialists offer substantial opportunities to improve on current approaches to diagnosis and assessment of patients with sleep problems. Such collaborations could also prove key to improved fundamental understanding of the pathophysiology that underlies sleep disorders and their adverse impact on the brain, cardiovascular system, and optimal health.

Approaches to the assessment of arousals and sleep disturbance in children

Childhood arousals, awakenings, and sleep disturbances during the night are common problems for both patients and their families. Additionally, inadequate sleep may contribute to daytime sleepiness, behavioral problems, and other important consequences of pediatric sleep disorders. Arousals, awakenings, and sleep disturbances can be quantified by routine polysomnography, and arousal scoring is generally performed as part of the standard polysomnogram. Here, we review current approaches to quantification of arousals and sleep disturbances and examine outcomes that have been associated with these measures. Initial data suggest that computer-assisted identification of non-visible arousals, cyclic alternating patterns, or respiratory cycle-related EEG changes may complement what can be accomplished by human scorers. Focus on contiguous bouts of sleep or specific sleep stages may prove similarly useful. Incorporation of autonomic arousal measures-such as heart rate variability, pulse transit time, or peripheral arterial tone-into standard reports may additionally capture subtle sleep fragmentation.

Obstructive sleep apnea alters sleep stage transition dynamics

INTRODUCTION

Enhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep. One approach involves using stage transition analysis to characterize sleep continuity.

METHODS AND PRINCIPAL FINDINGS

We analyzed hypnograms from Sleep Heart Health Study (SHHS) participants using the following stage designations: wake after sleep onset (WASO), non-rapid eye movement (NREM) sleep, and REM sleep. We show that individual patient hypnograms contain insufficient number of bouts to adequately describe the transition kinetics, necessitating pooling of data. We compared a control group of individuals free of medications, obstructive sleep apnea (OSA), medical co-morbidities, or sleepiness (n = 374) with mild (n = 496) or severe OSA (n = 338). WASO, REM sleep, and NREM sleep bout durations exhibited multi-exponential temporal dynamics. The presence of OSA accelerated the "decay" rate of NREM and REM sleep bouts, resulting in instability manifesting as shorter bouts and increased number of stage transitions. For WASO bouts, previously attributed to a power law process, a multi-exponential decay described the data well. Simulations demonstrated that a multi-exponential process can mimic a power law distribution.

CONCLUSION AND SIGNIFICANCE

OSA alters sleep architecture dynamics by decreasing the temporal stability of NREM and REM sleep bouts. Multi-exponential fitting is superior to routine mono-exponential fitting, and may thus provide improved predictive metrics of sleep continuity. However, because a single night of sleep contains insufficient transitions to characterize these dynamics, extended monitoring of sleep, probably at home, would be necessary for individualized clinical application.

Sleep measures and morning plasma TNF-alpha levels in children with sleep-disordered breathing

BACKGROUND

Sleep disordered breathing in children is associated with severity-dependent increases in excessive daytime sleepiness (EDS). TNF-alpha is an inflammatory cytokine that has been implicated in EDS. Since, at any given level of apnea-hypopnea index, there is significant variability in EDS, we hypothesized that morning tumor necrosis factor (TNF)-alpha plasma levels may provide a biologic correlate of EDS.

METHODS

Children being evaluated for sleep disordered breathing underwent a blood draw after nocturnal polysomnography, and TNF-alpha plasma concentrations were assayed using ELISA. In a subset of 15 children with sleep disordered breathing and in 15 matched control subjects, whole blood cultures in the presence of lipopolysaccharide and Multiple Sleep Latency Test were conducted. Furthermore, 22 children with obstructive sleep apnea had TNF-alpha levels assayed and underwent nocturnal polysomnography and Multiple Sleep Latency Test before and after adenotonsillectomy.

RESULTS

In 298 children, morning TNF-alpha levels were globally increased in the presence of obstructive sleep apnea, particularly in more severe cases, and correlated with obstructive apnea-hypopnea index and sleep pressure score, a measure of respiratory-induced sleep fragmentation, but not with nadir Sa02. A stepwise logistic regression analysis revealed that sleep pressure score and body mass index accounted for 36.2% of the adjusted variance in TNF-alpha levels (P < 0.0001). Furthermore, multiple sleep latencies were correlated with whole blood culture-derived TNF-alpha levels (n = 15), and morning TNF-alpha levels decreased after adenotonsillectomy in 22 children.

CONCLUSIONS

TNF-alpha levels are increased in pediatric obstructive sleep apnea, are primarily driven by sleep fragmentation and body mass index, and are closely associated with the degree of sleepiness, as measured by Multiple Sleep Latency Test. Furthermore, surgical treatment of obstructive sleep apnea results in significant reductions in TNF-alpha levels with reciprocal prolongations in sleep latency.

Sleep stage dynamics differ between children with and without obstructive sleep apnea

STUDY OBJECTIVES

Analysis of sleep dynamics--distributions of contiguous sleep and sleep stage durations--reveal exponential distributions and potential clinical utility in adults. We sought to examine these polysomnographic variables for the first time in children, and in the context of childhood sleep disordered breathing (SDB).

DESIGN AND SETTING

Analysis of polysomnographic data available from the Washtenaw County Adenotonsillectomy Cohort.

PARTICIPANTS

Selected subjects were 48 children aged 5-12 years with SDB (pediatric apnea/hypopnea index > or = 1.5) who were scheduled for adenotonsillectomy and 20 control subjects of similar ages without SDB. Subjects were studied at enrollment and again one year later in almost all cases.

RESULTS

Durations of sleep and specific sleep stage bouts generally followed exponential distributions. At baseline, the number of sleep stage changes, proportion of total sleep time occupied by stage 1 sleep, proportion stage 2 sleep, mean stage 2 duration, and mean stage REM duration each distinguished subjects with and without SDB (P < 0.05), but only mean stage 2 duration did so independently after accounting for the other variables (P = 0.03). At one-year follow-up, changes in total sleep time, mean stage 2 duration, and mean stage REM duration distinguished SDB from control subjects, but again only changes in mean stage 2 duration did so independently (P = 0.01).

CONCLUSIONS

Durations of uninterrupted sleep and specific sleep stages appear to follow exponential distributions in children with or without SDB. Parameters that describe these distributions--particularly mean duration of stage 2 sleep periods--may provide useful additions to standard sleep stage analyses.

Obesity and excessive daytime sleepiness in prepubertal children with obstructive sleep apnea

INTRODUCTION

The epidemic of childhood obesity has prompted remarkable changes in the relative proportions of symptomatic overweight or obese children being referred for evaluation of habitual snoring. However, it remains unclear whether obesity modifies the relative frequency of daytime symptoms such as excessive daytime sleepiness.

METHODS

Fifty consecutive, nonobese, habitually snoring, otherwise-healthy children (age range: 6-9 years) and 50 age-, gender-, and ethnicity-matched obese children (BMI z score: >1.67) underwent an overnight polysomnographic evaluation, followed by a multiple sleep latency test the following day.

RESULTS

The mean obstructive apnea/hypopnea index values for the 2 groups were similar (nonobese: 12.0 +/- 1.7 episodes per hour of total sleep time; obese: 10.9 +/- 1.5 episodes per hour of total sleep time). However, the mean sleep latency for obese children was significantly shorter (12.9 +/- 0.9 minutes) than that for nonobese children (17.9 +/- 0.7 minutes). Furthermore, 21 obese children had mean sleep latencies of < or =12.0 minutes, compared with only 5 nonobese children. Although significant associations emerged between mean sleep latency, obstructive apnea/hypopnea index, proportion of total sleep time with oxygen saturation of <95%, and respiratory arousal index for the whole cohort, the slopes and intersects of the linear correlation of mean sleep latency with any of these polygraphic measures were consistently greater in the obese cohort.

CONCLUSIONS

The likelihood of excessive daytime sleepiness for obese children is greater than that for nonobese children at any given level of obstructive sleep apnea severity and is strikingly reminiscent of excessive daytime sleepiness patterns in adults with obstructive sleep apnea.

Respiratory cycle-related EEG changes during sleep reflect esophageal pressures

STUDY OBJECTIVES

Respiratory cycle-related EEG changes (RCREC) have been demonstrated during sleep by digital analysis and hypothesized to represent subtle inspiratory microarousals that may help to explain daytime sleepiness in patients with sleep-disordered breathing. We therefore examined for the first time associations between RCREC and esophageal pressure swings (deltaPes) that reflect work of breathing.

DESIGN

Retrospective analysis.

SETTING

Academic sleep laboratory.

PATIENTS

Forty adults referred for suspected sleep disordered breathing.

INTERVENTIONS

Polysomnography with esophageal pressure monitoring and automatic computation of deltaPes using a novel algorithm.

RESULTS

Computed deltaPes for nearly all respiratory cycles during sleep correlated well with visual scoring of selected respiratory cycle samples (Spearman rho = 0.86, P < 0.0001). The RCREC within the sigma EEG range (12.5-15.5 Hz) rather than that within other frequency ranges most often showed significant within-subject inverse correlations with deltaPes. In contrast, in between-subject comparisons, beta (15.5-30.5 Hz) and to a lesser extent theta (4.5-7.5 Hz) RCREC, rather than sigma RCREC, showed significant inverse associations with mean APes.

CONCLUSIONS

Variation within subjects of sigma RCREC with APes supports previous evidence that RCREC within this range may reflect microarousals exacerbated by increased work of breathing. Correlation of beta and theta, but not sigma RCREC with deltaPes in between-subject comparisons is more difficult to explain but suggests that ranges other than sigma also deserve further investigation for clinical utility.

Prospective randomized study of patients with insomnia and mild sleep disordered breathing

BACKGROUND

Patients with insomnia may present with mild and often unrecognized obstructive sleep apnea (OSA).

OBJECTIVE

To evaluate both subjective and objective outcomes of patients with complaints of insomnia and mild OSA who receive surgical treatment for OSA versus behavioral treatment with cognitive behavioral therapy for insomnia (CBT-I).

METHODS

Prospective study with crossover design of 30 patients with complaints of insomnia and mild OSA. Thirty subjects, matched for age and gender, were randomized with stratification to receive either CBT-I or surgical treatment of OSA as primary treatment. Patients were reassessed after completing the initial intervention and reassigned if agreeable to the alternative treatment option and assessed again on completion of both treatment arms. Outcome measures included clinical impression, Epworth Sleepiness Scale (ESS) score, Fatigue Severity Scale (FSS) score, and polysomnography (PSG) results.

RESULTS

Surgery resulted in greater improvements in total sleep time (TST), slow wave sleep and REM sleep duration, respiratory disturbance index, apnea-hypopnea index, minimum oxygen saturation, FSS, and ESS. CBT-I also improved TST and resulted in shorter sleep latency.

CONCLUSION

Surgical intervention for the management of patients with complaints of insomnia and mild OSA demonstrated greater improvement in both subjective and objective outcome measures. Initial treatment of underlying OSA in patients with insomnia was more successful in improving insomnia than CBT-I alone. However CBT-I as initial treatment improved TST compared to baseline; following surgical intervention, it had the additional benefit of further increasing TST and helped to control sleep onset difficulties that may be related to conditioning due to unrecognized symptoms of mild OSA.

Daytime sleepiness and polysomnography in obstructive sleep apnea patients

BACKGROUND

Excessive daytime sleepiness (EDS) is the major complaint in subjects with obstructive sleep apnea syndrome (OSAS). However, EDS is not universally present in all patients with OSAS. The mechanisms explaining why some patients with OSAS complain of EDS whereas others do not are unknown.

OBJECTIVE

To investigate polysomnographic determinants of excessive daytime sleepiness (EDS) in a large multicenter cohort of patients with obstructive sleep apnea (OSAS).

METHODS

All consecutive patients with an apnea-hypopnea index greater than 5h(-1) who were evaluated between 2003 and 2005. EDS was assessed using the Epworth Sleepiness Scale (ESS), and patients were considered to have EDS if the ESS was >10.

RESULTS

A total of 1649 patients with EDS ((mean [+/-SD] Epworth 15+/-3) and 1233 without EDS (Epworth 7+/-3) were studied. Patients with EDS were slightly younger than patients without EDS (51+/-12 vs 54+/-13 years, p<0.0001), had longer total sleep time (p<0.007), shorter sleep latency (p<0001), greater sleep efficiency (p<0.0001) and less NREM sleep in stages 1 and 2 (p<0.007) than those without EDS. Furthermore, patients with EDS had slightly higher AHI (p<0.005) and arousal index (p<0.001) and lower nadir oxygen saturation (p<0.01).

CONCLUSIONS

Patients with OSAS and EDS are characterized by longer sleep duration and increased slow wave sleep compared to those without EDS. Although patients with EDS showed a mild worsening of respiratory disturbance and sleep fragmentation, these results suggest that sleep apnea and sleep disruption are not the primary determinants of EDS in all of these patients.

Childhood obstructive sleep-disordered breathing: a clinical update and discussion of technological innovations and challenges

Childhood sleep-disordered breathing (SDB) has been known to be associated with health and cognitive impacts for more than a century, and yet our understanding of this disorder is in its infancy. Neuropsychological consequences in children with snoring or subtle breathing disturbances not meeting the traditional definition of sleep apnea suggest that "benign, or primary snoring" may be clinically significant, and that the true prevalence of SDB might be underestimated. There is no standard definition of SDB in children. The polysomnographic technology used in many sleep laboratories may be inadequate to diagnose serious but subtle forms of clinically important airflow limitation. In the last several years, advances in digital technology as well as new observational studies of respiratory and arousal patterns in large populations of healthy children have led to alternative views of what constitutes sleep-related breathing and arousal abnormalities that may refine our diagnostic criteria. This article reviews our knowledge of childhood SDB, highlights recent advances in technology, and discusses diagnostic and treatment strategies that will advance the management of children with pediatric SDB.

Pediatric obstructive sleep apnea: complications, management, and long-term outcomes

Obstructive sleep apnea (OSA) in children has emerged not only as a relatively prevalent condition but also as a disease that imposes a large array of morbidities, some of which may have long-term implications, well into adulthood. The major consequences of pediatric OSA involve neurobehavioral, cardiovascular, and endocrine and metabolic systems. The underlying pathophysiological mechanisms of OSA-induced end-organ injury are now being unraveled, and clearly involve oxidative and inflammatory pathways. However, the roles of individual susceptibility (as dictated by single-nucleotide polymorphisms), and of environmental and lifestyle conditions (such as diet, physical, and intellectual activity), may account for a substantial component of the variance in phenotype. Moreover, the clinical prototypic pediatric patient of the early 1990s has been insidiously replaced by a different phenotypic presentation that strikingly resembles that of adults afflicted by the disease. As such, analogous to diabetes, the terms type I and type II pediatric OSA have been proposed. The different manifestations of these two entities and their clinical course and approaches to management are reviewed.

[Sleep disturbances in critically ill patients]

Sleep is an essential part of life with many important roles which include immunologic, cognitive and muscular functions. Of the working population 20% report sleep disturbances and in critically ill patients an incidence of more than 50% has been shown. However, sleep disturbances in the intensive care unit (ICU) population have not been investigated in detail. Sleep disturbances in ICU patients have a variety of reasons: e.g. patient-related pathologies like sepsis, acute or chronic pulmonary diseases, cardiac insufficiency, stroke or epilepsy, surgery, therapeutical interventions like mechanical ventilation, noise of monitors, pain or medication. Numerous scales and questionnaires are used to quantify sleep and the polysomnogramm is used to objectify sleep architecture. To improve sleep in ICU patients concepts are needed which include in addition to pharmacological treatment (pain reduction and sedation) synchronization of ICU activities with daylight, noise reduction and music for relaxation. In order to establish evidence-based guidelines, research activities about sleep and critical illness should be intensified. Questions to be answered are: 1) Which part of sleep disturbances in critically ill patients is directly related to the illness or trauma? 2) Is the grade of sleep disturbance correlated with the severity of the illness or trauma? 3) Which part is related to the medical treatment and can be modified or controlled? In order to define non-pharmacological and pharmacological concepts to improve sleep quality, studies need to be randomized and to include different ICU populations. The rate of nosocomial infections, cognitive function and respiratory muscle function should be considered in these studies as well. This will help to answer the question, whether it is useful to monitor sleep in ICU patients as a parameter to indicate therapeutical success and short-term quality of life. Follow-up needs to be long enough to detect adverse effects of withdrawal symptoms after termination of analgesia and sedation or delirium.

Obstructive sleep apnea - management update

Obstructive sleep apnea (OSA) is a highly significant condition based both on the high prevalence in community and significant consequences. Obstructive sleep apnea syndrome (OSAS), OSA together with hypersomnolence, is seen in 4% of middle-aged men and 2% of middle-aged women. OSA is associated with impaired quality of life and increased risks of motor vehicle accidents, cardiovascular disease (including hypertension and coronary artery disease), and metabolic syndrome. There is some evidence for the use of conservative interventions such as weight loss and position modification. CPAP remains the mainstay of treatment in this condition with high-level evidence supporting its efficacy. Continuous positive airway pressure (CPAP) is an intrusive therapy, with long-term adherence rates of less than 70%. Dental appliances have been shown to be effective therapy in some subjects but are limited by the inability to predict treatment responders. Alternative treatments are discussed but there is little role for upper airway surgery (except in a select few experienced institutions) or pharmacological treatment. The current levels of evidence for the different treatment regimens are reviewed.

Chronic fatigue, unrefreshing sleep and nocturnal polysomnography

BACKGROUND AND PURPOSE

To investigate the complaint of unrefreshing sleep with study of sleep electroencephalogram (EEG) in patients with chronic fatigue.

PATIENTS AND METHODS

Fourteen successively seen patients (mean age: 41.1 9.8) who complained of chronic fatigue but denied sleepiness and agreed to participate were compared to 14 controls (33.6+/-10.2 years) who were monitored during sleep recorded in parallel. After performing conventional sleep scoring we applied Fast Fourier Transformation (FFT) for the delta 1, delta 2, theta, alpha, sigma 1, sigma 2, beta EEG frequency bands. The presence of non-rapid eye movement (NREM) sleep instability was studied with calculation of cyclic alternating pattern (CAP) rate. Two-way analysis of variance (ANOVA) was performed to analyze FFT results and Mann-Whitney U-test to compare CAP rate in both groups of subjects.

RESULTS

Slow wave sleep (SWS) percentage and sleep efficiency were lower, but there was a significant increase in delta 1 (slow delta) relative power in the chronic fatigue group when compared to normals (P<0.01). All the other frequency bands were proportionally and significantly decreased compared to controls. CAP rate was also significantly greater in subjects with chronic fatigue than in normals (P=0.04). An increase in respiratory effort and nasal flow limitation were noted with chronic fatigue.

CONCLUSIONS

The complaints of chronic fatigue and unrefreshing sleep were associated with an abnormal CAP rate, with increase in slow delta power spectrum, affirming the presence of an abnormal sleep progression and NREM sleep instability. These specific patterns were related to subtle, undiagnosed sleep-disordered breathing.