Due to adverse social and environmental factors, Black children and/or those living in lower socioeconomic status (SES) contexts are more likely to experience sleep disorders, including obstructive sleep apnea syndrome (OSAS), as well as short and irregular sleep duration. Children with OSAS also often have behavioral difficulties, but little is known about whether these challenges are worse when children also experience poor sleep, which is linked to similar child outcomes. Keep reading
We aim to provide a realistic assessment of sleep stage classification (SSC) models in real-world label-scarce scenarios. Most of the proposed deep learning-based models assume access to a vast amount of labeled data, which is not usually achievable. What was the approach to solving the problem? We explored the efficacy of self-supervised learning (SSL) algorithms in scenarios with limited labeled data. Keep reading
Dr. Susan Redline and Dr. Shaun Purcell give a talk for the “TUM Chronobiology and Health” series entitled “Open Science in Sleep Research: Tools and Resources, Promises and Challenges." Check it out here! Keep reading
Sleep disturbances in children and adolescents are important to identify as they affect daytime function, and, although extremely common, can be especially hard to diagnose in those with autism. For this reason, the description of the type and frequency of sleep diagnoses in the pediatric autism population has been elusive to researchers, clinicians, and caregivers. Keep reading
AI for automated sleep staging is considered mature and has found its way to commercial sleep evaluation systems. Systems underpinned by deep learning require large datasets providing a broad sample of sleep stages the machine seeks to ‘learn’. Of concern, most databases used for developing AI sleep stagers include only recordings from adults, creating a significant inherent sample bias when applied to when applied in the pediatric or geriatric sleep setting. Keep reading
Want to be an early adopter of two new web-based tools (Moonlight & Moonbeam) for sleep signals, specifically designed to view NSRR data? If so, 1) read on, 2) head to https://remnrem.net/ to play with them and 3) please do give us any feedback. What is Moonlight? Moonlight is an interactive viewer for polysomnographic data. It is built on top of the command-line Luna package (https://zzz.bwh.harvard.edu/luna/), written using R and the Shiny library. Keep reading
Guest blogger: Grégory Hammad, Ir, PhD GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium, Chair of Neurogenetics, Faculty of Medicine, Technical University of Munich, Munich, Germany PyActigraphy is an open-source Python software for actigraphy and light data analysis. Keep reading
Any researchers who have tried to combine multiple datasets or validate findings in another dataset know how heterogeneity across datasets can make the process difficult or even impossible. At NSRR, we are working to address these challenges by standardizing and harmonizing important sleep measures and non-sleep covariates retrospectively. Standardization aims to reach uniformity in metadata across datasets, be it channel labels, annotations, variable definitions, sleep terminology, etc. Keep reading
Guest Blogger: Diego R. Mazzotti, Ph.D. University of Kansas Medical Center In this post, we will discuss some of the highlights of a Workshop Report recently published in SLEEP1 by our colleagues at the Sleep Research Network (SRN), a Task Force from the Sleep Research Society (SRS). This report summarizes a discussion panel held at the World Sleep Congress in Vancouver, Canada in 2019, that brought together leaders in sleep, circadian sciences, and biomedical informatics. Keep reading
Rao, G., S. Redline, F. Schilbach, H. Schofield, and M. Toma. (2021). Informing Sleep Policy Through Field Experiments, Science, Volume 374, Issue 6567, https://doi.org/10.1126/science.abk2594 Bessone, P., G. Rao, F. Schilbach, H. Schofield, and M. Toma. (2021). The Economic Consequences of Increasing Sleep Among the Urban Poor, Quarterly Journal of Economics, Volume 136, Issue 3, https://doi.org/10. Keep reading