SleepOMICS: How Big Data Can Revolutionize Sleep Science

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis

BACKGROUND

Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths.

OBJECTIVE

This study aims to examine ChatGPT's capability to debunk sleep-related disbeliefs.

METHODS

A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted.

RESULTS

ChatGPT labeled a significant portion (n=17, 85%) of the statements as "false" (n=9, 45%) or "generally false" (n=8, 40%), with varying accuracy across different domains. For instance, it correctly identified most myths about "sleep timing," "sleep duration," and "behaviors during sleep," while it had varying degrees of success with other categories such as "pre-sleep behaviors" and "brain function and sleep." ChatGPT's assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85% and a positive predictive value of 100%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT's appraisals and expert opinions could be found, suggesting that the AI's ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations.

CONCLUSIONS

ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors.

Evaluating the effectiveness of artificial intelligence-based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT-4

This study evaluates the performance of two major artificial intelligence-based tools (ChatGPT-4 and Google Bard) in debunking sleep-related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5-point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT-4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (-0.42) and kurtosis (-0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT-4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and -0.07, respectively, indicating a more normal distribution compared with ChatGPT-4. The inter-rater agreement between Google Bard and sleep experts had an intra-class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text-mining analysis revealed Google Bard's focus on practical advice, while ChatGPT-4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th-grade level material, versus ChatGPT-4's 12th-grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence-generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.

Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?

Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

Wireless electroencephalography (EEG) to monitor sleep among patients being withdrawn from opioids: Evidence of feasibility and utility

Sleep impairment is a common comorbid and debilitating symptom for persons with opioid use disorder (OUD). Research into underlying mechanisms and efficacious treatment interventions for OUD-related sleep problems requires both precise and physiologic measurements of sleep-related outcomes and impairment. This pilot examined the feasibility of a wireless sleep electroencephalography (EEG) monitor (Sleep Profiler™) to measure sleep outcomes and architecture among participants undergoing supervised opioid withdrawal. Sleep outcomes were compared to a self-reported sleep diary and opioid withdrawal ratings. Participants (n = 8, 100% male) wore the wireless EEG 85.6% of scheduled nights. Wireless EEG detected measures of sleep architecture including changes in total, NREM and REM sleep time during study phases, whereas the diary detected changes in wakefulness only. Direct comparisons of five overlapping outcomes revealed lower sleep efficiency and sleep onset latency and higher awakenings and time spent awake from the wireless EEG versus sleep diary. Associations were evident between wireless EEG and increased withdrawal severity, lower sleep efficiency, less time in REM and non-REM stages 1 and 2, and more hydroxyzine treatment; sleep diary was associated with total sleep time and withdrawal only. Data provide initial evidence that a wireless EEG is a feasible and useful tool for objective monitoring of sleep in persons experiencing acute opioid withdrawal. Data are limited by the small and exclusively male sample, but provide a foundation for using wireless EEG sleep monitors for objective evaluation of sleep-related impairment in persons with OUD in support of mechanistic and treatment intervention research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Planetary sleep medicine: Studying sleep at the individual, population, and planetary level

Circadian rhythms are a series of endogenous autonomous oscillators that are generated by the molecular circadian clock which coordinates and synchronizes internal time with the external environment in a 24-h daily cycle (that can also be shorter or longer than 24 h). Besides daily rhythms, there exist as well other biological rhythms that have different time scales, including seasonal and annual rhythms. Circadian and other biological rhythms deeply permeate human life, at any level, spanning from the molecular, subcellular, cellular, tissue, and organismal level to environmental exposures, and behavioral lifestyles. Humans are immersed in what has been called the "circadian landscape," with circadian rhythms being highly pervasive and ubiquitous, and affecting every ecosystem on the planet, from plants to insects, fishes, birds, mammals, and other animals. Anthropogenic behaviors have been producing a cascading and compounding series of effects, including detrimental impacts on human health. However, the effects of climate change on sleep have been relatively overlooked. In the present narrative review paper, we wanted to offer a way to re-read/re-think sleep medicine from a planetary health perspective. Climate change, through a complex series of either direct or indirect mechanisms, including (i) pollution- and poor air quality-induced oxygen saturation variability/hypoxia, (ii) changes in light conditions and increases in the nighttime, (iii) fluctuating temperatures, warmer values, and heat due to extreme weather, and (iv) psychological distress imposed by disasters (like floods, wildfires, droughts, hurricanes, and infectious outbreaks by emerging and reemerging pathogens) may contribute to inducing mismatches between internal time and external environment, and disrupting sleep, causing poor sleep quantity and quality and sleep disorders, such as insomnia, and sleep-related breathing issues, among others. Climate change will generate relevant costs and impact more vulnerable populations in underserved areas, thus widening already existing global geographic, age-, sex-, and gender-related inequalities.

The Future of Sleep Measurements: A Review and Perspective

This article provides an overview of the current use, limitations, and future directions of the variety of subjective and objective sleep assessments available. This article argues for various ways and sources of collecting, combining, and using data to enlighten clinical practice and the sleep research of the future. It highlights the prospects of digital management platforms to store and present the data, and the importance of codesign when developing such platforms and other new instruments. It also discusses the abundance of opportunities that data science and machine learning open for the analysis of data.

Sleep health, diseases, and pain syndromes: findings from an electronic health record biobank

STUDY OBJECTIVES

Implementation of electronic health record biobanks has facilitated linkage between clinical and questionnaire data and enabled assessments of relationships between sleep health and diseases in phenome-wide association studies (PheWAS). In the Mass General Brigham Biobank, a large health system-based study, we aimed to systematically catalog associations between time in bed, sleep timing, and weekly variability with clinical phenotypes derived from ICD-9/10 codes.

METHODS

Self-reported habitual bed and wake times were used to derive variables: short (<7 hours) and long (≥9 hours) time in bed, sleep midpoint, social jetlag, and sleep debt. Logistic regression and Cox proportional hazards models were used to test cross-sectional and prospective associations, respectively, adjusted for age, gender, race/ethnicity, and employment status and further adjusted for body mass index.

RESULTS

In cross-sectional analysis (n = 34,651), sleep variable associations were most notable for circulatory system, mental disorders, and endocrine/metabolic phenotypes. We observed the strongest associations for short time in bed with obesity, for long time in bed and sleep midpoint with major depressive disorder, for social jetlag with hypercholesterolemia, and for sleep debt with acne. In prospective analysis (n = 24,065), we observed short time in bed associations with higher incidence of acute pain and later sleep midpoint and higher sleep debt and social jetlag associations with higher incidence of major depressive disorder.

CONCLUSIONS

Our analysis reinforced that sleep health is a multidimensional construct, corroborated robust known findings from traditional cohort studies, and supported the application of PheWAS as a promising tool for advancing sleep research. Considering the exploratory nature of PheWAS, careful interrogation of novel findings is imperative.

Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review

Lack of adequate sleep is a major source of many harmful diseases related to heart, brain, psychological changes, high blood pressure, diabetes, weight gain, etc. 40 to 50% of the world's population is suffering from poor or inadequate sleep. Insomnia is a sleep disorder in which an individual complaint of difficulties in starting/continuing sleep at least four weeks regularly. It is estimated that 70% of heart diseases are generated during insomnia sleep disorder. The main objective of this study is to determine all work conducted on insomnia detection and to make a database. We used two procedures including network visualization techniques on two databases including PubMed and Web of Science to complete this study. We found 169 and 36 previous publications of insomnia detection in the PubMed and the Web of Science databases, respectively. We analyzed 10 datasets, 2 databases, 21 genes, and 23 publications with 30105 subjects of insomnia detection. This work has revealed the future way and gap so far directed on insomnia detection and has also tried to provide objectives for the future work to be proficient in a scientific and significant manner.

U-Sleep: resilient high-frequency sleep staging

Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers

Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work. Data availability: https://osf.io/z2xpd/?view_only=87fa24be486a473884adb9ffbe8db4ec.

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

Dynamics of sleep: Exploring critical transitions and early warning signals

BACKGROUND AND OBJECTIVES

In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages.

METHODS

EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Firstly, distinct EEG epochs were selected, both detected by a MLR classifier and through manual scoring. Secondly, change point analysis was used to identify abrupt changes in the EEG signal. Thirdly, these detected change points were evaluated on whether they were preceded by early warning signals.

RESULTS

Multiple change points were identified in the EEG signal, mostly in interplay with N2. The dynamics before these changes revealed, for a part of the change points, indicators of generic early warning signals, characteristic of complex systems (e.g., ecosystems, climate, epileptic seizures, global finance systems).

CONCLUSIONS

The sketched new framework for studying critical transitions in sleep EEG might benefit the understanding of individual and pathological differences in the dynamics of sleep stage transitions. Formalising sleep as a nonlinear dynamical system can be useful for definitions of sleep quality, i.e. stability and accessibility of an equilibrium state, and disrupted sleep, i.e. constant shifting between instable sleep states.

The COVID-19 pandemic: reflections for the field of sleep medicine

Ramar K. The COVID-19 pandemic: reflections for the field of sleep medicine. J Clin Sleep Med . 2020;16(7):993–996.

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

The future of sleep health: a data-driven revolution in sleep science and medicine

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

Associations of sleep problems with health-risk behaviors and psychological well-being among Canadian adults

OBJECTIVES

Examine the associations of sleep problems with health-risk behaviors and psychological well-being in a representative sample of Canadian adults.

DESIGN

Cross-sectional.

SETTING

The 2011-2012 Canadian Community Health Survey (CCHS, conducted by Statistics Canada).

PARTICIPANTS

Of all individuals taking part in the 2011-2012 CCHS, 42,600 participants aged ≥18 years from five provinces/territories (Nova Scotia, Quebec, Manitoba, Alberta, and Yukon) who participated in the sleep survey module were selected for this study.

MEASUREMENTS

Health conditions were self-reported. Sleep problems referred to extreme sleep durations (either <5 or ≥10 hours) and insomnia symptom. Health-risk behaviors included physical inactivity, daily smoking, highly sedentary behavior, and insufficient fruit and vegetable consumption. Worse psychological well-being included having worse self-rated general health, worse self-rated mental health, and worse sense of belonging, and being dissatisfied with life.

RESULTS

The participants represented 10,614,600 Canadian adults aged ≥18 years from the five abovementioned provinces/territories. A significantly higher prevalence of all health-risk behaviors and worse psychological well-being was found among participants with extreme sleep durations (than those with 7 to <8 hours) and insomnia symptom (than those without insomnia symptom). After multivariate adjustment, extreme sleep durations and insomnia symptom were still independently associated with increased odds of all health-risk behaviors and worse psychological well-being.

CONCLUSIONS

Both extreme sleep durations and insomnia symptom were independently associated with health-risk behaviors and worse psychological well-being among Canadian adults.

Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea

IMPORTANCE

Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches.

OBJECTIVE

To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis.

DESIGN, SETTING, AND PARTICIPANTS

Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution (Sleep Laboratory, Centre Hospitalier Universitaire Université Catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium). Patients with suspected OSA were enrolled from July 5, 2017, to October 31, 2018.

MAIN OUTCOMES AND MEASURES

Obstructive sleep apnea diagnosis required either evoking signs or symptoms or related medical or psychiatric comorbidities coupled with a PSG-derived respiratory disturbance index (PSG-RDI) of at least 5 events/h. A PSG-RDI of at least 15 events/h satisfied the diagnosis criteria even in the absence of associated symptoms or comorbidities. Patients who did not meet these criteria were classified as not having OSA. Agreement analysis and diagnostic performance were assessed by Bland-Altman plot comparing PSG-RDI and the Sunrise system RDI (Sr-RDI) with diagnosis threshold optimization via receiver operating characteristic curves, allowing for evaluation of the device sensitivity and specificity in detecting OSA at 5 events/h and 15 events/h.

RESULTS

Among 376 consecutive adults with suspected OSA, the mean (SD) age was 49.7 (13.2) years, the mean (SD) body mass index was 31.0 (7.1), and 207 (55.1%) were men. Reliable agreement was found between PSG-RDI and Sr-RDI in patients without OSA (n = 46; mean difference, 1.31; 95% CI, -1.05 to 3.66 events/h) and in patients with OSA with a PSG-RDI of at least 5 events/h with symptoms (n = 107; mean difference, -0.69; 95% CI, -3.77 to 2.38 events/h). An Sr-RDI underestimation of -11.74 (95% CI, -20.83 to -2.67) events/h in patients with OSA with a PSG-RDI of at least 15 events/h was detected and corrected by optimization of the Sunrise system diagnostic threshold. The Sr-RDI showed diagnostic capability, with areas under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.96) and 0.93 (95% CI, 0.90-0.93) for corresponding PSG-RDIs of 5 events/h and 15 events/h, respectively. At the 2 optimal cutoffs of 7.63 events/h and 12.65 events/h, Sr-RDI had accuracy of 0.92 (95% CI, 0.90-0.94) and 0.88 (95% CI, 0.86-0.90) as well as posttest probabilities of 0.99 (95% CI, 0.99-0.99) and 0.89 (95% CI, 0.88-0.91) at PSG-RDIs of at least 5 events/h and at least 15 events/h, respectively, corresponding to positive likelihood ratios of 14.86 (95% CI, 9.86-30.12) and 5.63 (95% CI, 4.92-7.27), respectively.

CONCLUSIONS AND RELEVANCE

Automatic analysis of MM patterns provided reliable performance in RDI calculation. The use of this index in OSA diagnosis appears to be promising.

Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring

INTRODUCTION

Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods.

METHODS AND ANALYSIS

We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm.

ETHICS AND DISSEMINATION

The study was reviewed by the medical ethical committee of the Maxima Medical Center (Eindhoven, the Netherlands, File no: N16.074). All subjects provide informed consent before participation.The SOMNIA database is built to facilitate future research in sleep medicine. Data from the completed SOMNIA database will be made available for collaboration with researchers outside the institute.

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.