Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning

Advances in digital health allow us to take an active part in monitoring and improving our sleep quality. Both, objectively recorded and subjectively perceived sleep quality impacts our general health and well-being. This research shows how these two dimensions of sleep quality can be captured with smartwatches and digital symptom trackers. We contribute to the gap in the literature on how recorded values from wearables and user-generated content from mobile applications can elevate each other. Analysing the recorded and re- ported sleep quality in a longitudinal sleep study (n=45) shows differences in how partic- ipants perceive their sleep. We address this need for personalization, by creating clusters of participants with a similar perception of sleep using unsupervised machine learning. Analysing these clusters provides us with a more wholesome understanding of their sleep quality and raises awareness for the uniqueness of individuals in digital health

Nocturnal sweating--a common symptom of obstructive sleep apnoea: the Icelandic sleep apnoea cohort.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.To estimate the prevalence and characteristics of frequent nocturnal sweating in obstructive sleep apnoea (OSA) patients compared with the general population and evaluate the possible changes with positive airway pressure (PAP) treatment. Nocturnal sweating can be very bothersome to the patient and bed partner

The different clinical faces of obstructive sleep apnoea: a cluster analysis.

To access publisher's full text version of this article click on the hyperlink at the bottom of the pageAlthough 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

Towards a Digital Sleep Diary Standard

A sleep diary is an important tool to gather subjective sleep data, which provides key information for the diagnosis of a variety of sleep disorders. In 2012, an expert panel created a standardized sleep diary in pen-and-paper format. However, pen-and-paper has certain limitations, in particular, it is difficult to monitor participant compliance and memory bias. We improve upon these limitations with a digital design and identify benefits and drawbacks of the pen-and-paper format in comparison to a digital sleep diary in an empirical study based on an action design research project. The main contribution consists of five design guidelines: i) use the native environment, ii) utilize established input methods, iii) embed customization to minimize participant workload, iv) evaluate the application continuously using analytics, and v) integrate digital elements to increase compliance. Furthermore, we propose a mobile application design for a digital sleep diary that is in accordance with these guidelines

Multi-centre arousal scoring agreement in the Sleep Revolution

We investigated arousal scoring agreement within full-night polysomnography in a multi-centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second-by-second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04-0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second-by-second agreement was fair (Fleiss' kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07-0.68). Fleiss' kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes.Peer reviewe

Sleep medicine catalogue of knowledge and skills – Revision

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

Sleep medicine catalogue of knowledge and skills - Revision

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.Peer reviewe

Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesSTUDY OBJECTIVES: A recent study of patients with moderate-severe obstructive sleep apnea (OSA) in Iceland identified three clinical clusters based on symptoms and comorbidities. We sought to verify this finding in a new cohort in Iceland and examine the generalizability of OSA clusters in an international ethnically diverse cohort. METHODS: Using data on 972 patients with moderate-severe OSA (apnea-hypopnea index [AHI] ≥ 15 events per hour) recruited from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC), we performed a latent class analysis of 18 self-reported symptom variables, hypertension, cardiovascular disease, and diabetes. RESULTS: The original OSA clusters of disturbed sleep, minimally symptomatic, and excessively sleepy replicated among 215 SAGIC patients from Iceland. These clusters also generalized to 757 patients from five other countries. The three clusters had similar average AHI values in both Iceland and the international samples, suggesting clusters are not driven by OSA severity; differences in age, gender, and body mass index were also generally small. Within the international sample, the three original clusters were expanded to five optimal clusters: three were similar to those in Iceland (labeled disturbed sleep, minimal symptoms, and upper airway symptoms with sleepiness) and two were new, less symptomatic clusters (labeled upper airway symptoms dominant and sleepiness dominant). The five clusters showed differences in demographics and AHI, although all were middle-aged (44.6-54.5 years), obese (30.6-35.9 kg/m2), and had severe OSA (42.0-51.4 events per hour) on average. CONCLUSIONS: Results confirm and extend previously identified clinical clusters in OSA. These clusters provide an opportunity for a more personalized approach to the management of OSA.National Institutes of Health Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) National Center For Advancing Translational Science

Quantifying Airflow Limitation and Snoring During Sleep.

Traditional techniques to assess respiratory disturbances during sleep allow the accurate diagnosis of moderate and severe cases of obstructive sleep apnea but have serious limitations in mild obstructive sleep apnea and cases with signs of obstructive breathing during sleep without apneas and hypopneas. This article describes advantages and limitations of available techniques to measure obstructive breathing during sleep by measuring flow limitation, respiratory effort, and snoring. Standardization of these techniques is crucial for moving the field further and understanding the pathophysiologic role of obstructive breathing itself, and not solely focusing on the associated outcomes of arousals and oxygen desaturations.Nox Medica

Improving Machine Learning Technology in the Field of Sleep.

Icelandic Research FundThe authors discuss the challenges of machine- and deep learning-based automatic analysis of obstructive sleep apnea with respect to known issues with the signal interpretation, patient physiology, and the apnea-hypopnea index. Their goal is to provide guidance for sleep and machine learning professionals working in this area of sleep medicine. They suggest that machine learning approaches may well be better targeted at examining and attempting to improve the diagnostic criteria, in order to build a more nuanced understanding of the detailed circumstances surrounding OSA, rather than merely attempting to reproduce human scoring. Keywords: Deep learning; Polysomnography; Sleep apnea; Sleep staging