Sleep and Circadian Informatics Data Harmonization: A Workshop Report from the Sleep Research Society and Sleep Research Network

The increasing availability and complexity of sleep and circadian data are equally exciting and challenging. The field is in constant technological development, generating better high-resolution physiological and molecular data than ever before. Yet, the promise of large-scale studies leveraging millions of patients is limited by suboptimal approaches for data sharing and interoperability. As a result, integration of valuable clinical and basic resources is problematic, preventing knowledge discovery and rapid translation of findings into clinical care. To understand the current data landscape in the sleep and circadian domains, the Sleep Research Society (SRS) and the Sleep Research Network (now a task force of the SRS) organized a workshop on informatics and data harmonization, presented at the World Sleep Congress 2019, in Vancouver, Canada. Experts in translational informatics gathered with sleep research experts to discuss opportunities and challenges in defining strategies for data harmonization. The goal of this workshop was to fuel discussion and foster innovative approaches for data integration and development of informatics infrastructure supporting multi-site collaboration. Key recommendations included collecting and storing findable, accessible, interoperable, and reusable data; identifying existing international cohorts and resources supporting research in sleep and circadian biology; and defining the most relevant sleep data elements and associated metadata that could be supported by early integration initiatives. This report introduces foundational concepts with the goal of facilitating engagement between the sleep/circadian and informatics communities and is a call to action for the implementation and adoption of data harmonization strategies in this domain.

GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.

An open-source, high-performance tool for automated sleep staging

The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.

Dreams and Trauma Changes in the Manifest Dreams in Psychoanalytic Treatments – A Psychoanalytic Outcome Measure

Although psychoanalysts are interested in symptom reduction as an outcome, they are looking for instruments to measure sustaining changes in the unconscious mental functioning. In this article it is discussed that conceptually well-founded transformation of manifest dreams analyzed with precise empirical methods could be considered as a promising indicator for such therapeutic changes. We are summarizing a dream generation model by Moser and von Zeppelin which has integrated a large interdisciplinary knowledge base of contemporary dream and sleep research. Based on this model the authors have developed a valid and reliable coding system for analyzing manifest dreams, the Zurich Dream Process Coding System (ZDPCS). One exemplary dream from the beginning and one from the third year of a severely traumatized, chronic depressed patient from the LAC Depression Study collected in psychoanalytic sessions as well as in the sleep laboratory have been analyzed applying the ZDPCS. Authors hypothesize that transformation in dreams as measured with the ZDPCS is the result of memory processes of traumatic embodied memories in the state of dreaming.

The Emblems of Sleep Societies in Asia and the Republic of Korea

An emblem is a symbolic representational image that stands for a certain organization, concept, team, or society. This study investigated the emblems of the sleep societies in South Korea and the international sleep societies in which they were registered as members. Three South Korean sleep societies were found by searching for the keyword ‘sleep’ in the Korea Citation Index. Subsequently, we identified three international societies in which the three South Korean conferences participate. The emblems can be classified according to their composition. Taegeuk patterns represent yin and yang, electroencephalography that stands for the objective indicator of sleep, and the acronym or abbreviation indicating the name of the society. All emblems in this study were combinations of pictorial images and letters. The pictorial image of the Korean Sleep Research Society is the only emblem representing an inset with Hangeul. The emblem is a medium that conveys diverse meanings beyond representation. The societies have attempted to embody the identity as well as their directions.

International Sleep Research Training Program: An Opportunity for Advanced Career Development in Sleep Medicine

The article's abstract is not available.