Although commonly observed in clinical practice, the heterogeneity of obstructive sleep apnoea (OSA) clinical presentation has not been formally characterised. This study was the first to apply ...cluster analysis to identify subtypes of patients with OSA who experience distinct combinations of symptoms and comorbidities. An analysis of baseline data from the Icelandic Sleep Apnoea Cohort (822 patients with newly diagnosed moderate-to-severe OSA) was performed. Three distinct clusters were identified. They were classified as the "disturbed sleep group" (cluster 1), "minimally symptomatic group" (cluster 2) and "excessive daytime sleepiness group" (cluster 3), consisting of 32.7%, 24.7% and 42.6% of the entire cohort, respectively. The probabilities of having comorbid hypertension and cardiovascular disease were highest in cluster 2 but lowest in cluster 3. The clusters did not differ significantly in terms of sex, body mass index or apnoea-hypopnoea index. Patients with OSA have different patterns of clinical presentation, which need to be communicated to both the lay public and the professional community with the goal of facilitating care-seeking and early identification of OSA. Identifying distinct clinical profiles of OSA creates a foundation for offering more personalised therapies in the future.
The ‘catalogue of knowledge and skills’ for sleep medicine presents the blueprint for a curriculum, a textbook, and an examination on sleep medicine. The first catalogue of knowledge and skills was ...presented by the European Sleep Research Society in 2014. It was developed following a formal Delphi procedure. A revised version was needed in order to incorporate changes that have occurred in the meantime in the International Classification of Sleep Disorders, updates in the manual for scoring sleep and associated events, and, most important, new knowledge in sleep physiology and pathophysiology. In addition, another major change can be observed in sleep medicine: a paradigm shift in sleep medicine has taken place. Sleep medicine is no longer a small interdisciplinary field in medicine. Sleep medicine has increased in terms of recognition and importance in medical care. Consequently, major medical fields (e.g. pneumology, cardiology, neurology, psychiatry, otorhinolaryngology, paediatrics) recognise that sleep disorders become a necessity for education and for diagnostic assessment in their discipline. This paradigm change is considered in the catalogue of knowledge and skills revision by the addition of new chapters.
Summary
There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep‐disordered breathing. The present report provides a background review of ...existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta‐analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of s followed by full‐text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea–hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch‐by‐epoch and event‐by‐event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta‐analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta‐analysis will be performed for continuous outcomes using the DerSimonian and Laird random‐effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.
Although much is known about human body vibration discomfort, there is little research data on the effects of vibration on vehicle occupant drowsiness. A laboratory experimental setup has been ...developed. Vibration was applied to the volunteers sitting on the vehicle seat mounted on the vibration platform. Seated volunteers were exposed to a Gaussian random vibration, with 1–15 Hz frequency bandwidth at 0.2 ms-2 r.m.s., for 20-minutes. Two drowsiness measurement methods were used, Psychomotor Vigilance Test (PVT) and Karolinska Sleepiness Scale (KSS). Significant changes in PVT (p<0.05) and KSS (p<0.05) were detected in all eighteen volunteers. Furthermore, a moderate correlation (r>0.4) was observed between objective measurement (PVT) and subjective measurement (KSS). The results suggest that exposure to vibration even for 20-minutes can cause significant drowsiness impairing psychomotor performance. This finding has important implications for road safety.
Summary
Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring ...rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter‐rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage‐specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea–hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non‐rapid eye movement stages.
Identifying mouth breathing during sleep in a reliable, non-invasive way is challenging and currently not included in sleep studies. However, it has a high clinical relevance in pediatrics, as it can ...negatively impact the physical and mental health of children. Since mouth breathing is an anomalous condition in the general population with only 2% prevalence in our data set, we are facing an anomaly detection problem. This type of human medical data is commonly approached with deep learning methods. However, applying multiple supervised and unsupervised machine learning methods to this anomaly detection problem showed that classic machine learning methods should also be taken into account. This paper compared deep learning and classic machine learning methods on respiratory data during sleep using a leave-one-out cross validation. This way we observed the uncertainty of the models and their performance across participants with varying signal quality and prevalence of mouth breathing. The main contribution is identifying the model with the highest clinical relevance to facilitate the diagnosis of chronic mouth breathing, which may allow more affected children to receive appropriate treatment.
Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, ...researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers.
A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow.
We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17
.
We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.
Summary
It is 50 years ago, in 1972, that the founding conference of the European Sleep Research Society (ESRS) was organised in Basel. Since then the Society has had 13 presidents and a multitude of ...board members and has organised, among other things, another 24 congresses. At this 50th anniversary, as the 26th ESRS congress is approaching, we have summarised the history of the ESRS. In this review, we provide a background to show why the foundation of a European society was a logical step, and show how, in the course of the past 50 years, the Society changed and grew. We give special attention to some developments that occurred over the years and discuss where the ESRS stands now, and how we foresee its future.
Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, ...sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.
Abstract Objective This study investigates the prevalence and the association between restless legs syndrome (RLS) and a large variety of health variables in two well-characterized random samples ...from the general population in Reykjavik, Iceland, and Uppsala, Sweden. Methods Using the national registries of inhabitants, a random sample from adults aged 40 and over living in Reykjavík, Iceland ( n = 939), and Uppsala, Sweden ( n = 998), were invited to participate in a study on the prevalence of COPD (response rate 81.1% and 62.2%). In addition, the participants were asked to answer the following questionnaires: International RLS Rating Scale, Short Form-12, the Epworth Sleepiness Scale, and questions about sleep, gastroeosophageal reflux, diabetes and hypertension, as well as pharmacological treatment. Interleukin-6 (IL-6), C-reactive protein (CRP) and ferritin were measured in serum. Results RLS was more commonly reported in Reykjavik (18.3%) than in Uppsala (11.5%). Icelandic women reported RLS almost twice as often as Swedish women (24.4 vs. 13.9% p = 0.001), but there was no difference in prevalence of RLS between Icelandic and Swedish men. RLS was strongly associated with sleep disturbances and excessive daytime sleepiness. Subjects with RLS were more likely to be ex- and current smokers than subjects without RLS ( p < 0.001). Respiratory symptoms and airway obstruction were more prevalent among those reporting RLS and they also estimated their physical quality of life lower than those without RLS ( p < 0.001). RLS was not associated with symptoms of the metabolic syndrome like hypertension, obesity, markers of systemic inflammation (IL-6 and CRP) or cardiovascular diseases. Ferritin levels were significantly lower in RLS participants ( p = 0.0002), but not ( p = 0.07) after adjustment for center, age, sex and smoking history. Conclusion Restless legs syndrome was twice as common among Icelandic women compared to Swedish women. No such difference was seen for men. RLS was strongly associated with smoking and respiratory symptoms, decreased lung function, sleep disturbances, excessive daytime sleepiness, and physical aspects of life quality. RLS was not associated with markers of the metabolic syndrome like hypertension, obesity, cardiovascular diseases or biomarkers of systemic inflammation.
PDF
