Respiratory cycle-related EEG changes: response to CPAP

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and brain imaging support and through detailed clinic-epidemiologic observations, several topics are still dominated by outdated paradigms.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.

Altered K-complex morphology during sustained inspiratory airflow limitation is associated with next-day lapses in vigilance in obstructive sleep apnea

STUDY OBJECTIVES

Determine if changes in K-complexes associated with sustained inspiratory airflow limitation (SIFL) during N2 sleep are associated with next-day vigilance and objective sleepiness.

METHODS

Data from thirty subjects with moderate-to-severe obstructive sleep apnea who completed three in-lab polysomnograms: diagnostic, on therapeutic continuous positive airway pressure (CPAP), and on suboptimal CPAP (4 cmH2O below optimal titrated CPAP level) were analyzed. Four 20-min psychomotor vigilance tests (PVT) were performed after each PSG, every 2 h. Changes in the proportion of spontaneous K-complexes and spectral characteristics surrounding K-complexes were evaluated for K-complexes associated with both delta (∆SWAK), alpha (∆αK) frequencies.

RESULTS

Suboptimal CPAP induced SIFL (14.7 (20.9) vs 2.9 (9.2); %total sleep time, p < 0.001) with a small increase in apnea-hypopnea index (AHI3A: 6.5 (7.7) vs 1.9 (2.3); p < 0.01) versus optimal CPAP. K-complex density (num./min of stage N2) was higher on suboptimal CPAP (0.97 ± 0.7 vs 0.65±0.5, #/min, mean ± SD, p < 0.01) above and beyond the effect of age, sex, AHI3A, and duration of SIFL. A decrease in ∆SWAK with suboptimal CPAP was associated with increased PVT lapses and explained 17% of additional variance in PVT lapses. Within-night during suboptimal CPAP K-complexes appeared to alternate between promoting sleep and as arousal surrogates. Electroencephalographic changes were not associated with objective sleepiness.

CONCLUSIONS

Sustained inspiratory airflow limitation is associated with altered K-complex morphology including the increased occurrence of K-complexes with bursts of alpha as arousal surrogates. These findings suggest that sustained inspiratory flow limitation may be associated with nonvisible sleep fragmentation and contribute to increased lapses in vigilance.

Sleep staging from electrocardiography and respiration with deep learning

STUDY OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Temporal Courses in EEG Theta and Alpha Activity in the Dynamic Health Qigong Techniques Wu Qin Xi and Liu Zi Jue

Health Qigong is a common technique of Traditional Chinese Medicine applied to strengthen mental and physical health. Several studies report increases in EEG theta and alpha activity after meditative Qigong techniques indicating a relaxed state of mind. To date, little is known on the effects of dynamic Health Qigong techniques that comprise bodily movements on brain activity. In the current study, we compared effects of two dynamic Health Qigong techniques on EEG brain activity. Subjects performed the techniques Wu Qin Xi (five animals play) and Liu Zi Jue (six healing sounds) in a within-subjects design. Eyes-open and eyes-closed resting EEG was recorded before and immediately after each 15-min practice block. Additionally, the Profile of Mood States (POMS) questionnaire was administered at pretest, and after each 15-min practice block. Results show a decrease in alpha activity after 15 min, followed by an increase after 30 min in the Health Qigong technique Liu Zi Jue. Theta activity was decreased after 15 min, followed by an increase after 30 min in the technique Wu Qin Xi. Results of the POMS indicated an increased vigor-activity level with decreased fatigue and tension-anxiety levels in both techniques after 30 min of practice. Our results demonstrate different temporal dynamics in EEG theta and alpha activity for the Health Qigong techniques Wu Qin Xi and Liu Zi Jue. We hypothesize that the found brain activation patterns result from different attentional focusing styles and breathing techniques performed during the investigated Health Qigong techniques.

To sleep, or not to sleep - that is the question, for polysomnography

Have we reached the point where respiratory polygraphy can replace polysomnography in the assessment of OSAS? http://ow.ly/UxCU30bNopq.

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.

EEG Brain Activity in Dynamic Health Qigong Training: Same Effects for Mental Practice and Physical Training?

In recent years, there has been significant uptake of meditation and related relaxation techniques, as a means of alleviating stress and fostering an attentive mind. Several electroencephalogram (EEG) studies have reported changes in spectral band frequencies during Qigong meditation indicating a relaxed state. Much less is reported on effects of brain activation patterns induced by Qigong techniques involving bodily movement. In this study, we tested whether (1) physical Qigong training alters EEG theta and alpha activation, and (2) mental practice induces the same effect as a physical Qigong training. Subjects performed the dynamic Health Qigong technique Wu Qin Xi (five animals) physically and by mental practice in a within-subjects design. Experimental conditions were randomized. Two 2-min (eyes-open, eyes-closed) EEG sequences under resting conditions were recorded before and immediately after each 15-min exercise. Analyses of variance were performed for spectral power density data. Increased alpha power was found in posterior regions in mental practice and physical training for eyes-open and eyes-closed conditions. Theta power was increased after mental practice in central areas in eyes-open conditions, decreased in fronto-central areas in eyes-closed conditions. Results suggest that mental, as well as physical Qigong training, increases alpha activity and therefore induces a relaxed state of mind. The observed differences in theta activity indicate different attentional processes in physical and mental Qigong training. No difference in theta activity was obtained in physical and mental Qigong training for eyes-open and eyes-closed resting state. In contrast, mental practice of Qigong entails a high degree of internalized attention that correlates with theta activity, and that is dependent on eyes-open and eyes-closed resting state.

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.

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.