Sleep medicine, sleep research, and sleep education: a whole life devoted to sleep.

This article describes my participation in sleep medicine, sleep research, and sleep education, mainly in Europe, between the years 1970 and 2000.

An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight.

Introduction: 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. Methods: 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. Results: 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 κ. Discussion: 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.

Sleep-Deep-Learner is taught sleep-wake scoring by the end-user to complete each record in their style.

Sleep-wake scoring is a time-consuming, tedious but essential component of clinical and preclinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new datasets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Learner creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential (LFP) records via transfer learning of GoogLeNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. Sleep-Deep-Learner then automates scoring of the remainder of the EEG/LFP record. A novel REM sleep scoring correction procedure further enhanced accuracy. Sleep-Deep-Learner reliably scores EEG and LFP data and retains sleep-wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer's disease and in a genetic knock-down study, when compared to manual scoring. Sleep-Deep-Learner reduced manual scoring time to 1/12. Since Sleep-Deep-Learner uses transfer learning on each independent recording, it is not biased by previously scored existing datasets. Thus, we find Sleep-Deep-Learner performs well when used on signals altered by a drug, disease model, or genetic modification.

Review and perspective on sleep-disordered breathing research and translation to clinics.

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.

Individualising the social? The history of insomnia around 1900.

Using the example of the fin-de-siècle German Reich, this article outlines how insomnia emerged as a "disease of civilisation" in an industrialising society, defined by time-specific notions, reflecting and strengthening the social norms of the time. Furthermore, it analyses the process of individualisation and flexibilisation that transferred the social struggles and economic demands of modernity onto the subject's body or soul. The history of insomnia around 1900 thus reveals a pattern of thought that shaped the understanding of the insomniac throughout the 20th century.

[Research Status of and Recommendations for Prevention and Control of Sleep Disorders in China].

The quality of sleep, a key physiological factor that regulates information, memory, decision making, and other vital brain functions, can affect important physiological functions of the human body. According to disease classification systems, sleep disorders can be categorized into more than 90 types, including sleep apnea, insomnia, and hypersomnia. It may cause a variety of adverse consequences, such as depression, anxiety and other emotional disorders, as well as physical diseases such as hypertension, diabetes and stroke. In addition, the relevant cardiovascular and cerebrovascular diseases and cognitive impairment not only harm physical health, but also are associated with workplace accidents and safety problems, constituting public safety hazards. Sleep disorders have become a major social and scientific problem that impacts on the national economy and the livelihood of the people. Research on sleep disorders should be given more attention by researchers and policy makers. Herein, we mainly discussed the latest findings and difficulties concerning research on the prevention and intervention of sleep disorders and proposed strategies and suggestions accordingly.

Continuing Nursing Research During a Pandemic.

The coronavirus disease-2019 pandemic disrupted traditional research practices with the cessation of face-to-face contact with study participants. Researchers needed to respond with alternative methods to continue nurse-led clinical research. A rapid pivot to remote processes for recruitment, enrollment, data collection, and participant incentives can enable research to continue despite restrictions on in-person activities. Technology offers innovative methods in meeting current research needs but is not without challenges and continued need for ethics evaluation.

Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style.

Sleep-wake scoring is a time-consuming, tedious but essential component of clinical and pre-clinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new data sets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold-standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Net (SDN) creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential records via transfer learning of GoogleNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. SDN then automates scoring of the remainder of the EEG/LFP record. A novel REM scoring correction procedure further enhanced accuracy. SDN reliably scores EEG and LFP data and retains sleep-wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer's disease and in a genetic knock-down study, when compared to manual scoring. SDN reduced manual scoring time to 1/12. Since SDN uses transfer learning on each independent recording, it is not biased by previously scored existing data sets. Thus, we find SDN performs well when used on signals altered by a drug, disease model or genetic modification.

Can we put the first night effect to bed? An analysis based on a large sample of healthy adults.

The 'first night effect' refers to individuals experiencing poorer sleep during their first night in a laboratory. The effect is attributed to sleeping in a new environment, as well as wearing electrodes on the head and face, and is often cited as a reason for including an adaptation night in sleep research protocols. However, in the time since the 'first night effect' was initially reported, the conditions and equipment used in modern sleep laboratories have changed considerably, which may reduce the 'first night effect.' The aim of this study was to examine the impact of the 'first night effect' on sleep in a sample of healthy adults. Participants (n = 124; 22.7 ± 3.6 years) were given a 9-hour sleep opportunity (23:00-08:00 h) on two consecutive nights in a time-isolated sleep laboratory with sleep measured via polysomnography. Differences in dependent sleep variables between Night 1 and Night 2 were examined using paired t-tests. There was no difference in sleep onset latency (p = .295), total sleep time (p = .343), wake after sleep onset (p = .410), or sleep efficiency (p = .342) between Nights 1 and 2. However, participants spent more time in stage one (p = .001), and less time in stages two (p = .029) and three (p = .013) on Night 1 compared with Night 2. This suggests that, where primary sleep variables are the focus and not sleep architecture or arousals (e.g., where sleep is used as an independent variable), including an adaptation night may not be necessary.

The history of sleep research and sleep medicine in Europe.

Sleep became a subject of scientific research in the second half of the 19th century. Since sleep, unlike other physiological functions, cannot be attributed to a specific organ, there was no distinct method available to study sleep until then. With the development of physiology and psychology, and a rapidly increasing knowledge of the structure and functioning of the nervous system, certain aspects of sleep became accessible to objective study. A first step was to measure responsiveness to external stimuli systematically, during sleep, allowing a first representation of the course of sleep (Schlaftiefe = sleep depth). A second method was to register continuously the motor activity across the sleep-wake cycle, which allowed the documentation in detail of rest-activity patterns of monophasic and polyphasic sleep-wake rhythms, or between day or night active animals. The central measurement for sleep research, however, became the electroencephalogram in the 1930s, which allowed observation of the sleeping brain with high temporal resolution. Beside the development of instruments to measure sleep, prolonged sleep deprivation was applied to study physiological and psychological effects of sleep loss. Another input came from clinical and neuropathological observations of patients with pronounced disorders of the sleep-wake cycle, which for the first time allowed localisation of brain areas that are essentially involved in the regulation of sleep and wakefulness. Experimental brain stimulation and lesion studies were carried out with the same aim at this time. Many of these activities came to a halt on the eve of World War II. It was only in the early 1950s, when periods with rapid eye movements during sleep were recognised, that sleep became a research topic of itself. Jouvet and his team explored the brain mechanisms and transmitters of paradoxical sleep, and experimental sleep research became established in all European countries. Sleep medicine evolving simultaneously in different countries, with early centres in Italy and France. In the late 1960s sleep research and chronobiology began to merge. In recent decades, sleep research, dream research, and sleep medicine have benefited greatly from new methods in genetic research and brain imaging techniques. Genes were identified that are involved in the regulation of sleep, circadian rhythms, or sleep disorders. Functional imaging enabled a high spatial resolution of the activity of the sleeping brain, complementing the high temporal resolution of the electroencephalogram.

The European Sleep Research Society - past, present and future.

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.

Simulation of the electrical stimulation of the rat brain using sleep frequencies: A finite element modeling approach.

A realistic rat brain model was used to simulate current density and electric field distributions under frequencies characteristic of sleeping states (0.8, 5, and 12 Hz). Two anode-electrode setups were simulated: plate vs. screws-anode, both with a cephalic cathode. Our simulations showed that these frequencies have limited impact on electric field and current density; however, the highest frequency evidenced higher values for both variables. The type of electrode setup had a greater effect on current distribution and induced fields. In that sense, the screws setup resulted in higher values of the modeled variables. The numeric results obtained are within the range of available data for rodent models using the finite elements method. These modeled effects should be analyzed regarding anatomical consequences (depth of penetration of the currents) and purpose of the experiment (i.e., entrainment of brain oscillations) in the context of sleep research.

Lost in translation-the influence of language on infant sleep research.

OBJECTIVES: We consider whether language shapes cultural interpretations of sleep in the family context using ethnographic data from the Czech Republic to explore one of the methods employed by Czech parents in helping their children aged 0-3 years to fall asleep. METHODS: Multi-methodological ethnographic data were collected in the Czech Republic during 2015-2018 with supplemental online data obtained in 2020. This involved focus groups with 90 participants in mother-baby centers, and interviews with 30 families, supplemented with 468 online responses. RESULTS: In the Czech Republic the use of parental presence with or without physical contact to help a child to fall asleep is a widespread practice. It is well-embedded within Czech culture and referred to by a widely known term: Uspávání. Parents expressed multiple motivations for using Uspávání to help their child sleep. DISCUSSION: Within much of the Anglophone sleep literature the practice of actively helping a child to fall asleep is perceived as problematic. A child who cannot fall asleep alone is considered to exhibit "behavioral insomnia of childhood," and parents are advised to prevent this "sleep problem" by promoting self-soothing techniques in infancy. We suggest that as there is no English-language equivalent for the word Uspávání the concept it encapsulates is under-valued by sleep researchers, and the practice and its consequences are insufficiently researched. CONCLUSIONS: Some important variations in parental sleep practices that are embedded in everyday family systems lack English terminology; Uspávání is one such example. This may lead to researchers overlooking or rejecting the validity of such diverse family sleep practices. There is a need for more ethnographic research of sleep in the context of different cultural environments and family systems to explore how language constrains understanding of parent-child sleep.

New Trends and New Technologies in Sleep Medicine: Expanding Accessibility.

New trends in sleep medicine make use of the increased computational power of digital transformation. A current trend toward fewer sensors on the body of the sleeper and to more data processing from derived signals is observed. Telemedicine technologies are used for data transmission and for better patient management in terms of diagnosis and in terms of treatment of chronic conditions.

Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm.

BACKGROUND: In recent years, with the acceleration of life rhythm and increased pressure, the problem of sleep disorders has become more and more serious. It affects people's quality of life and reduces work efficiency, so the monitoring and evaluation of sleep quality is of great significance. Sleep staging has an important reference value in sleep quality assessment. This article starts with the study of sleep staging to detect and analyze sleep quality. For the purpose of sleep quality detection, this article proposes a sleep quality detection method based on electroencephalography (EEG) signals. MATERIALS AND METHODS: This method first preprocesses the EEG signals and then uses the discrete wavelet transform (DWT) for feature extraction. Finally, the transfer support vector machine (TSVM) algorithm is used to classify the feature data. RESULTS: The proposed algorithm was tested using 60 pieces of data from the National Sleep Research Resource Library of the United States, and sleep quality was evaluated using three indicators: sensitivity, specificity, and accuracy. Experimental results show that the classification performance of the TSVM classifier is significantly higher than those of other comparison algorithms. This further validated the effectiveness of the proposed sleep quality detection method.

luox: validated reference open-access and open-source web platform for calculating and sharing physiologically relevant quantities for light and lighting.

Light exposure has a profound impact on human physiology and behaviour. For example, light exposure at the wrong time can disrupt our circadian rhythms and acutely suppress the production of melatonin. In turn, appropriately timed light exposure can support circadian photoentrainment. Beginning with the discovery that melatonin production is acutely suppressed by bright light more than 40 years ago, understanding which aspects of light drive the 'non-visual' responses to light remains a highly active research area, with an important translational dimension and implications for "human-centric" or physiologically inspired architectural lighting design. In 2018, the International Commission on Illumination (CIE) standardised the spectral sensitivities for predicting the non-visual effects of a given spectrum of light with respect to the activation of the five photoreceptor classes in the human retina: the L, M and S cones, the rods, and the melanopsin-containing intrinsically photosensitive retinal ganglion cells (ipRGCs). Here, we described a novel, lean, user-friendly, open-access and open-source platform for calculating quantities related to light. The platform, called luox, enables researchers and research users in vision science, lighting research, chronobiology, sleep research and adjacent fields to turn spectral measurements into reportable quantities. The luox code base, released under the GPL-3.0 License, is modular and therefore extendable to other spectrum-derived quantities. luox calculations of CIE quantities and indices have been endorsed by the CIE following black-box validation.

Self-reported sleep duration and timing: A methodological review of event definitions, context, and timeframe of related questions.

Study Objectives: Clinical and population health recommendations are derived from studies that include self-report. Differences in question wording and response scales may significantly affect responses. We conducted a methodological review assessing variation in event definition(s), context (i.e., work- versus free-day), and timeframe (e.g., "in the past 4 weeks") of sleep timing/duration questions. Methods: We queried databases of sleep, medicine, epidemiology, and psychology for survey-based studies and/or publications with sleep duration/timing questions. The text of these questions was thematically analyzed. Results: We identified 53 surveys with sample sizes ranging from 93 to 1,185,106. For sleep duration, participants reported nocturnal sleep (24/44), sleep in the past 24-hours (14/44), their major sleep episode (3/44), or answered unaided (3/44). For bedtime, participants reported time into bed (19/47), first attempt to sleep (16/40), or fall-asleep time (12/47). For wake-time, participants reported wake-up time (30/43), the time they "get up" (7/43), or their out-of-bed time (6/43). Context guidance appeared in 18/44 major sleep duration, 35/47 bedtime, and 34/43 wake-time questions. Timeframe was provided in 8/44 major sleep episode duration, 16/47 bedtime, and 10/43 wake-time questions. One question queried the method of awakening (e.g., by alarm clock), 18 questions assessed sleep latency, and 12 measured napping. Conclusion: There is variability in the event definition(s), context, and timeframe of questions relating to sleep. This work informs efforts at data harmonization for meta-analyses, provides options for question wording, and identifies questions for future surveys.

How to Report Light Exposure in Human Chronobiology and Sleep Research Experiments.

Exposure to light has short- and long-term impacts on non-visual responses in humans. While many aspects related to non-visual light sensitivity have been characterised (such as the action spectrum for melatonin suppression), much remains to be elucidated. Here, we provide a set of minimum reporting guidelines for reporting the stimulus conditions involving light as an intervention in chronobiology, sleep research and environmental psychology experiments. Corresponding to the current state-of-the-art knowledge (June 2019), these are (i) measure and report the spectral power distribution of the acute stimulus from the observer's point of view; (ii) measure and report the spectral power distribution of the background light environment from the observer's point of view; (iii), make spectra available in tabulated form, (iv) report α-opic (ir)radiances and illuminance; (v) describe the timing properties of stimulus (duration and pattern); (vi) describe the spatial properties of stimulus (spatial arrangement and extent), and (vii) report measurement conditions and equipment. We supplement the minimum reporting guidelines with optional reporting suggestions and discuss limitations of the reporting scheme.

Sleep Parameter Assessment Accuracy of a Consumer Home Sleep Monitoring Ballistocardiograph Beddit Sleep Tracker: A Validation Study.

STUDY OBJECTIVES: Growing interest in monitoring sleep and well-being has created a market for consumer home sleep monitoring devices. Additionally, sleep disorder diagnostics, and sleep and dream research would benefit from reliable and valid home sleep monitoring devices. Yet, majority of currently available home sleep monitoring devices lack validation. In this study, the sleep parameter assessment accuracy of Beddit Sleep Tracker (BST), an unobtrusive and non-wearable sleep monitoring device based on ballistocardiography, was evaluated by comparing it with polysomnography (PSG) measures. We measured total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE). Additionally, we examined whether BST can differentiate sleep stages. METHODS: We performed sleep studies simultaneously with PSG and BST in ten healthy young adults (5 female/5 male) during two non-consecutive nights in a sleep laboratory. RESULTS: BST was able to distinguish SOL with some accuracy. However, it underestimated WASO and thus overestimated TST and SE. Also, it failed to discriminate between non-rapid eye movement sleep stages and did not detect the rapid eye movement sleep stage. CONCLUSIONS: These findings indicate that BST is not a valid device to monitor sleep. Consumers should be careful in interpreting the conclusions on sleep quality and efficiency provided by the device.

Sleep tracking: A systematic review of the research using commercially available technology.

PURPOSE OF REVIEW: To systematically review the available research studies that characterize the benefits, uncertainty, or weaknesses of commercially-available sleep tracking technology. RECENT FINDINGS: Sleep is a vital component of health and well-being. Research shows that tracking sleep using commercially available sleep tracking technology (e.g., wearable or smartphone-based) is increasingly popular in the general population. METHODS: Systematic literature searches were conducted using PubMed/Medline, Embase (Ovid) the Cochrane Library, PsycINFO (Ovid), CINAHL, and Web of Science Plus (which included results from Biosis Citation Index, INSPEC, and Food, Science & Technology Abstracts) (n=842). STUDY INCLUSION AND EXCLUSION CRITERIA: Three independent reviewers reviewed eligible articles that administered a commercially-available sleep tracker to participants and reported on sleep parameters as captured by the tracker, including either sleep duration or quality. Eligible articles had to include sleep data from users for >=4 nights.