Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality.

BACKGROUND: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. OBJECTIVES: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. METHODS: The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. RESULTS: During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. CONCLUSIONS: The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging.

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.

The Association Between the 5-Hydroxytryptamine Receptor 2A Gene Variants rs6311 and rs6313 and Obstructive Sleep Apnea in the Iranian Kurdish Population.

Introduction: Sleep is one of the most significant parts of everyone's life. Most people sleep for about one-third of their lives. Sleep disorders negatively impact the quality of life. Obstructive sleep apnea (OSA) is a severe sleep disorder that significantly impacts the patient's life and their family members. This study aimed to investigate the relationship between rs6313 and rs6311 polymorphisms in the serotonin receptor type 2A gene and OSA in the Kurdish population. Materials and Methods: The study's population comprises 100 OSA sufferers and 100 healthy people. Polysomnography diagnostic tests were done on both the patient and control groups. The polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) was used to investigate the relationship between OSA and LEPR gene polymorphisms. Results: Statistical analysis showed a significant relationship between genotype frequencies of patient and control groups of rs6311 with OSA in dominant [odds ratio (OR) = 5.203, p < 0.001) and codominant models (OR = 9.7, p < 0.001). Also, there was a significant relationship between genotype frequencies of patient and control groups of rs6313 with OSA in dominant (OR = 10.565, p < 0.001) and codominant models (OR = 5.938, p < 0.001). Conclusions: Findings from the study demonstrated that the two polymorphisms rs6311 and rs6313 could be effective at causing OSA; however, there was no correlation between the severity of the disease and either of the two polymorphisms.

Increasing obstructive sleep apnea risk is associated with albuminuria in Korean adults: cross-sectional analysis.

Several studies have shown an association between albuminuria and obstructive sleep apnea (OSA). However, studies on the relationship between the STOP-BANG questionnaire that can screen for OSA and microalbuminuria are still insufficient. Therefore, this study attempted to clarify the relationship between microalbuminuria and OSA risk using the STOP-BANG questionnaire in Korean adults. A total of 7478 participants (3289 men and 4189 women) aged over 40 were enrolled in the Korean National Health and Nutrition Examination Survey from 2019 to 2020. STOP-BANG questionnaire to screen OSA was obtained from subjects. The urinary albumin/creatinine ratio (ACR) and proteinuria were measured via a single dipstick to evaluate renal function. The high OSA risk group had a higher mean ACR value than the low OSA risk group (36.8 ± 172.2 vs 17.7 ± 82.5; P < 0.001). The proportion of subjects with values of 30 ≤ ACR < 300 mg/g (11.9% vs 6.1%; P < 0.001) and ACR > 300 mg/g (2.1% vs 0.7%; P < 0.001) was significantly higher in high OSA risk group. Multivariate logistic regression results confirmed that microalbuminuria (OR 1.279, 95% confidence interval (CI) 1.068-1.532, P = 0.008) was significantly correlated with high OSA risk. In addition, significant correlation with high OSA risk was also found in macroalbuminuria (OR 1.684, 95% CI 1.073-2.530, P = 0.022) and proteinuria (OR 1.355, 95% CI 1.030-1.783, P = 0.030). We confirmed a significant correlation between high OSA risk and albuminuria/proteinuria in Korean adults. Therefore, renal function evaluation is required in high OSA risk patients, and OSA diagnosis through PSG test and treatment is necessary.

Reliable automatic sleep stage classification based on hybrid intelligence.

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.

The predictive value of mandibular advancement maneuvers during drug-induced sleep endoscopy for treatment success of oral appliance treatment in obstructive sleep apnea: a prospective study.

STUDY OBJECTIVES: To prospectively validate drug-induced sleep endoscopy with mandibular advancement maneuvers as a prediction tool for treatment success of oral appliance treatment (OAT). METHODS: Seventy-seven patients diagnosed with moderate obstructive sleep apnea were included and underwent drug-induced sleep endoscopy. The upper airway collapse was assessed using the VOTE classification. Additionally, three mandibular advancement maneuvers were performed to predict treatment success of OAT. If the maneuver was negative, the level and degree and configuration of the persistent collapse was described according to the VOTE classification. All patients were treated with OAT and completed a follow-up sleep study with OAT in situ without regard to their anticipated response to treatment. RESULTS: Sixty-four patients completed 6-month follow up. A positive jaw thrust maneuver proved to be significantly associated with favorable OAT response, whereas the chin lift maneuver and the vertical chin lift maneuver were not. Additionally, a persistent lateral oropharyngeal collapse when performing any mandibular advancement maneuver was significantly associated with unfavorable OAT response. CONCLUSIONS: The current findings suggest that a jaw thrust maneuver should be preferred over the chin lift maneuver for predicting OAT response. Patients with a positive jaw thrust maneuver should be counseled toward favorable OAT response, whereas those with persistent lateral oropharyngeal collapse should be advised about the likelihood of unfavorable OAT response. A negative jaw thrust maneuver did not prove to be a significant predictor for unfavorable response to OAT. Consequently, uncertainties arise regarding the justification of performing drug-induced sleep endoscopy solely for predicting the efficacy of OAT. However, the results of the current study could be influenced by heterogeneity in the assessment of respiratory parameters, variability in the performance of the mandibular advancement maneuvers, and the instability of bolus technique sedation. CLINICAL TRIAL REGISTRATION: Registry: Netherlands Trial Register; Name: Drug-induced Sleep Endoscopy: a prediction tool for success rate of oral appliance treatment; Identifier: NL8425; URL: https://www.onderzoekmetmensen.nl/en/trial/20741. CITATION: Veugen CCAFM, Kant E, Kelder JC, Schipper A, Stokroos RJ, Copper MP. The predictive value of mandibular advancement maneuvers during drug-induced sleep endoscopy for treatment success of oral appliance treatment in obstructive sleep apnea: a prospective study. J Clin Sleep Med. 2024;20(3): 353-361.

Objective evaluation of excessive daytime sleepiness.

Excessive daytime sleepiness (EDS) is multifactorial. It combines, among other things, an excessive propensity to fall asleep ("physiological sleepiness") and a continuous non-imperative sleepiness (or drowsiness/hypo-arousal) leading to difficulties remaining awake and maintaining sustained attention and vigilance over the long term ("manifest sleepiness"). There is no stand-alone biological measure of EDS. EDS measures can either capture the severity of physiological sleepiness, which corresponds to the propensity to fall asleep, or the severity of manifest sleepiness, which corresponds to behavioral consequences of sleepiness and reduced vigilance. Neuropsychological tests (The psychomotor vigilance task (PVT), Oxford Sleep Resistance Test (OSLeR), Sustained Attention to Response Task (SART)) explore manifest sleepiness through several sustained attention tests but the lack of normative values and standardized protocols make the results difficult to interpret and use in clinical practice. Neurophysiological tests explore the two main aspects of EDS, i.e. the propensity to fall asleep (Multiple sleep latency test, MSLT) and the capacity to remain awake (Maintenance of wakefulness test, MWT). The MSLT and the MWT are widely used in clinical practice. The MSLT is recognized as the "gold standard" test for measuring the severity of the propensity to fall asleep and it is a diagnostic criterion for narcolepsy. The MWT measures the ability to stay awake. The MWT is not a diagnostic test as it is recommended only to evaluate the evolution of EDS and efficacy of EDS treatment. Even if some efforts to standardize the protocols for administration of these tests have been ongoing, MSLT and MWT have numerous limitations: age effect, floor or ceiling effects, binding protocol, no normal or cutoff value (or determined in small samples), and no or low test-retest values in some pathologies. Moreover, the recommended electrophysiological set-up and the determination of sleep onset using the 30­sec epochs scoring rule show some limitations. New, more precise neurophysiological techniques should aim to detect very brief periods of physiological sleepiness and, in the future, the brain local phenomenon of sleepiness likely to underpin drowsiness, which could be called "physiological drowsiness".

Short-term stability and night-to-night variability of sleep parameters in nightmares comorbid with chronic insomnia Disorder across multiple nights of polysomnography.

STUDY OBJECTIVES: The purpose of this study was to examine the degree of short-term stability of polysomnographic (PSG) measured sleep parameters and the overall differences between individuals with comorbid nightmares and insomnia compared to those with chronic insomnia disorder alone or good sleeping controls across four nights in the sleep lab. METHODS: A total of 142 good sleeping controls, 126 chronic insomnia alone, and 24 comorbid insomnia/nightmare participants underwent four consecutive nights of 8-hour PSG recordings. Outcomes included sleep continuity, architecture, and REM-related parameters across nights one through four. Intraclass correlation coefficients with mixed-effect variances and repeated-measure analysis of covariance were used, respectively, to determine short-term stability as well as between-participants and time-by-group interaction effects. RESULTS: Wake after sleep onset and stage 1 showed "poor stability" in the comorbid insomnia/nightmare group compared to "moderate stability" in the good sleeping controls and chronic insomnia alone group. Significant between-group effects (all ps < .05) showed that the comorbid insomnia/nightmare group took longer to fall asleep and had a greater first-night-effect in stage 1 compared to good sleeping controls and chronic insomnia alone group; in addition, the comorbid insomnia/nightmare and insomnia alone groups slept shorter, with fewer awakenings and REM periods, compared to the good sleeping controls. CONCLUSIONS: Nightmares are associated with abnormal sleep above and beyond REM disruption, as sleep continuity was the primary aspect in which poor stability and group differences emerged. The greater inability to fall asleep and instability of sleep fragmentation in those with comorbid insomnia/nightmares compared to chronic insomnia alone may be attributed to the impact of presleep anticipatory anxiety and nightmare-related distress itself. CLINICAL TRIAL INFORMATION: The data analyzed in this study does not come from any current or previous clinical trials. Therefore, there is no clinical trial information to report.

Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review.

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.

[Multi-modal physiological time-frequency feature extraction network for accurate sleep stage classification].

Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen's Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.

Creation of a shortened version of the Sleep Disorders Questionnaire (SDQ).

The 176-item Sleep Disorders Questionnaire (SDQ) was initially developed using canonical discriminant function analysis on 4 groups of sleep disorder patients, but it was never studied by factor analysis in its entirety. Several authors have criticized 2 of its subscales as being confounded with each other, and its narcolepsy scale as substantially over-diagnosing narcolepsy. This study describes its first exploratory factor analysis (EFA), the intent of which was to reassess item membership on the 4 existing subscales and to derive new scales to improve differential diagnosis between patient groups. It was also hoped that EFA could reduce the total number of questions, to increase speed of completion. The EFA was performed on the anonymized SDQ results from a retrospective review of the charts of 2131 persons from 7 sleep disorders clinics and research centers. Factors were assessed via scree plots and eigenvalues. The EFA identified four main factors: insomnia, daytime sleepiness, substance use, and sleep-disordered breathing. The insomnia factor had 3 subfactors: psychological symptoms of insomnia, subjective description of insomnia, and insomnia due to periodic limb movements. The sleepiness factor had two subfactors: daytime sleepiness and neurological symptoms of narcolepsy. The novel substance use factor was homogeneous, as was the sleep-disordered breathing factor. Importantly, the EFA reassigned items from the original SDQ's NAR, PSY, and PLM subscales to five of the new subscales. The Sleep Apnea (SA) subscale emerged mostly unchanged. The 7 resulting factors comprised only 66 items of the original 176-item SDQ. These results have allowed the creation of a new shorter questionnaire, to be called the SDQ-2. External validation of the SDQ-2 is currently underway. It will likely prove to be a superior differential diagnostic instrument for sleep disorders clinics, compared to the original SDQ.

SlumberNet: deep learning classification of sleep stages using residual neural networks.

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of overall performance (accuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~ 50 × faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet further demonstrates the power of deep learning in mouse sleep research.

Methodological approach to sleep state misperception in insomnia disorder: Comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording.

STUDY OBJECTIVE: To provide a comprehensive assessment of sleep state misperception in insomnia disorder (INS) and good sleepers (GS) by comparing recordings performed for one night in-lab (PSG and night review) and during several nights at-home (actigraphy and sleep diaries). METHODS: Fifty-seven INS and 29 GS wore an actigraphy device and filled a sleep diary for two weeks at-home. They subsequently completed a PSG recording and filled a night review in-lab. Sleep perception index (subjective/objective × 100) of sleep onset latency (SOL), sleep duration (TST) and wake duration (TST) were computed and compared between methods and groups. RESULTS: GS displayed a tendency to overestimate TST and WASO but correctly perceived SOL. The degree of misperception was similar across methods within the GS group. In contrast, INS underestimated their TST and overestimated their SOL both in-lab and at-home, yet the severity of misperception of SOL was larger at-home than in-lab. Finally, INS overestimated WASO only in-lab while correctly perceiving it at-home. While only the degree of TST misperception was stable across methods in INS, misperception of SOL and WASO were dependent on the method used. CONCLUSIONS: We found that GS and INS exhibit opposite patterns and severity of sleep misperception. While the degree of misperception in GS was similar across methods, only sleep duration misperception was reliably detected by both in-lab and at-home methods in INS. Our results highlight that, when assessing sleep misperception in insomnia disorder, the environment and method of data collection should be carefully considered.

Effects of low-density lipoprotein cholesterol on sleep apnea: Insights from a rat model of cardiovascular autonomic dysregulation.

INTRODUCTION: The levels of low-density lipoprotein (LDL) cholesterol in plasma are important risk factors for coronary heart disease. Several reports suggest that elevated plasma cholesterol is associated with cardiac arrhythmias. In a subsequent study investigating LDL cholesterol levels and the frequency of LDL cholesterol measurements, a positive correlation was observed between the severity of sleep apnea and visit-to-visit LDL cholesterol variability. Our objective was to assess the effects of hypercholesterolemia on cardiac autonomic activity, disordered sleep patterns, and increased incidence of arrhythmias in freely moving rats. METHODS: Wireless transmission of polysomnographic recordings was performed in control and high cholesterol male rats during normal daytime sleep. Spectral analyses were conducted on the electroencephalogram and electromyogram (EMG) recordings to distinguish active waking, quiet sleep, and paradoxical sleep. Heart rate variability power spectrum analysis was used to measure cardiac autonomic activity. RESULTS: The high cholesterol group exhibited a higher low-frequency (LF)/high-frequency (HF) power ratio during all sleep stages compared to the control group. Additionally, the frequency of sleep interruptions was increased in the high cholesterol group compared to the control group. CONCLUSIONS: Our results show significant sleep fragmentation with sympathetic hyperactivity after exposure to high cholesterol. This indicates that high cholesterol may increase the risk of sleep apnea and poor sleep quality by disrupting autonomic homeostasis.

Changes in time structure of periodic leg movements during sleep in restless legs syndrome: Effects of sex and age.

BACKGROUND: The objective of this study was to check the hypothesis that in women with restless legs syndrome (RLS) different changes occur in periodic leg movements during sleep (PLMS) during the post-menopausal period (using >50 years as a proxy) than in men of the same age. METHODS: We recruited 36 untreated patients aged 18-50 years (19 men, median age 40 years, and 17 women, median age 37 years) while the remaining 67 were >50 years old (24 men, median age 66.6 years, and 43 women, median age 60.0 years). Leg movement activity during sleep was analyzed by means of an approach utilizing indexes especially suitable to assess leg movement periodicity. RESULTS: No significant difference was seen between men in the two age groups; conversely, in women, a clear and significant increase in Periodicity Index was observed in the older group, along with a decrease in isolated leg movements. In women, a clear age-related enhancement of PLMS was found in the intermovement interval graphs, especially in the 16-22 s range, which was more evident than that observed in men. The results remained unchanged also when they were replicated by selecting only subjects aged 18-45 years vs. those aged >55 years. CONCLUSIONS: Our findings indicate that assessing PLMS in women after menopause is clinically relevant because they are probably connected with the hormonal fluctuations of this period of life. Translationally, identifying and addressing PLMS in post-menopausal women is crucial for optimizing their sleep health and addressing potential health risks associated with sleep disturbances.

Validation of sleep-staging accuracy for an in-home sleep electroencephalography device compared with simultaneous polysomnography in patients with obstructive sleep apnea.

Efforts to simplify standard polysomnography (PSG) in laboratories, especially for obstructive sleep apnea (OSA), and assess its agreement with portable electroencephalogram (EEG) devices are limited. We aimed to evaluate the agreement between a portable EEG device and type I PSG in patients with OSA and examine the EEG-based arousal index's ability to estimate apnea severity. We enrolled 77 Japanese patients with OSA who underwent simultaneous type I PSG and portable EEG monitoring. Combining pulse rate, oxygen saturation (SpO2), and EEG improved sleep staging accuracy. Bland-Altman plots, paired t-tests, and receiver operating characteristics curves were used to assess agreement and screening accuracy. Significant small biases were observed for total sleep time, sleep latency, awakening after falling asleep, sleep efficiency, N1, N2, and N3 rates, arousal index, and apnea indexes. All variables showed > 95% agreement in the Bland-Altman analysis, with interclass correlation coefficients of 0.761-0.982, indicating high inter-instrument validity. The EEG-based arousal index demonstrated sufficient power for screening AHI ≥ 15 and ≥ 30 and yielded promising results in predicting apnea severity. Portable EEG device showed strong agreement with type I PSG in patients with OSA. These suggest that patients with OSA may assess their condition at home.

Instrumental assessment of sleep bruxism: A systematic review and meta-analysis.

This systematic review and meta-analysis (MA) aimed to evaluate the diagnostic validity of portable electromyography (EMG) diagnostic devices compared to the reference standard method polysomnography (PSG) in assessing sleep bruxism. This systematic review was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and was registered with PROSPERO prior to the accomplishment of the main search. Ten clinical studies on humans, assessing the diagnostic accuracy of portable instrumental approaches with respect to PSG, were included in the review. Methodological shortcomings were identified by QUADAS-2 quality assessment. The certainty of the evidence analysis was established by different levels of evidence according to the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework. A meta-analysis of diagnostic test accuracy was performed with multiple thresholds per study applying a two-stage random effects model, using the thresholds offered by the studies and based on the number of EMG bruxism events per hour presented by the participants. Five studies were included. The MA indicated that portable EMG diagnostic devices showed a very good diagnostic capacity, although a high variability is evident in the studies with some outliers. Very low quality of evidence due to high risk of bias and high heterogeneity among included studies suggests that portable devices have shown high sensitivity and specificity when diagnosing sleep bruxism (SB) compared to polysomnography. The tests performed in the MA found an estimated optimal cut-off point of 7 events/hour of SB with acceptably high sensitivity and specificity for the EMG portable devices.

Sleepy Kids: are the current diagnostic criteria for multiple sleep latency tests enough?

Excessive daytime sleepiness (EDS) is common in childhood and is currently quantified using adult criteria on a multiple sleep latency test (MSLT). This study aimed to describe paediatric MSLT results, particularly focussing on a previously proposed alternative mean sleep latency (MSL) threshold for children of 12 min, and assess the impact of a 5th nap. We performed a retrospective analysis of MSLTs at a single paediatric centre from 2004 to 2021. Narcolepsy was defined as a mean sleep latency (MSL) ≤8min with ≥2 sleep onset REM (SOREM) periods. Idiopathic Hypersomnia (IH) was defined as a MSL ≤8min with <2 SOREMs. An ambiguous MSLT result was defined as a MSL 8-12min and/or ≥2 SOREM periods. Of 214 MSLTs [50 % female, median age 14.0y (range 3.3-20.1y)], narcolepsy was diagnosed in 48 (22 %), IH in 22 (10 %) and the result was ambiguous in 44 (21 %). Those with ambiguous MSLT results were older (15.6 vs 13.4y, p = 0.006) with a higher proportion of females (61 % vs 35 %, p = 0.01) in comparison to the narcolepsy group. A 5th nap was performed in 60 (28 %) of MSLTs and only changed the outcome in one case. In conclusion, MSLT results are borderline in 21 % of paediatric cases, suggesting that current adult diagnostic criteria may miss narcolepsy and IH in children. A 5th nap usually makes no difference or increases the MSL, suggesting that a four nap MSLT protocol could be used apart from rare cases where the result is borderline after the 4th nap.

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review.

Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.

Association of Periodic Limb Movements and Obstructive Sleep Apnea With Risk of Cardiovascular Disease and Mortality.

BACKGROUND: Obstructive sleep apnea is a well-established risk factor for cardiovascular disease (CVD). Recent studies have also linked periodic limb movements during sleep to CVD. We aimed to determine whether periodic limb movements during sleep and obstructive sleep apnea are independent or synergistic factors for CVD events or death. METHODS AND RESULTS: We examined data from 1049 US veterans with an apnea-hypopnea index (AHI) <30 events/hour. The primary outcome was incident CVD or death. Cox proportional hazards regression assessed the relationships between the AHI, periodic limb movement index (PLMI), and the AHI×PLMI interaction with the primary outcome. We then examined whether AHI and PLMI were associated with primary outcome after adjustment for age, sex, race and ethnicity, obesity, baseline risk of mortality, and Charlson Comorbidity Index. During a median follow-up of 5.1 years, 237 of 1049 participants developed incident CVD or died. Unadjusted analyses showed an increased risk of the primary outcome with every 10-event/hour increase in PLMI (hazard ratio [HR], 1.08 [95% CI, 1.05-1.13]) and AHI (HR, 1.17 [95% CI, 1.01- 1.37]). Assessment associations of AHI and PLMI and their interaction with the primary outcome revealed no significant interaction between PLMI and AHI. In fully adjusted analyses, PLMI, but not AHI, was associated with an increased risk of primary outcome: HR of 1.05 (95% CI, 1.00-1.09) per every 10 events/hour. Results were similar after adjusting with Framingham risk score. CONCLUSIONS: Our study revealed periodic limb movements during sleep as a risk factor for incident CVD or death among those who had AHI <30 events/hour, without synergistic association between periodic limb movements during sleep and obstructive sleep apnea.