Overview AASM guidelines are the result of decades of efforts aimed at standardizing sleep scoring procedures, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules according to age. Automated sleep scoring systems - e.g. Keep reading
Overview The Greifswald Sleep Stage Classifier (GSSC) is a highly accurate, deep learning-based automatic sleep stage classifier that priortises ease of use as well as accessibility for a broad range of potential applications and recording environments. Sleep staging can be performed within a simple GUI, the command-line, or integrated directly into the user's own Python code. Keep reading
Overview: Textbook depictions of aging suggest a linear, almost deterministic, worsening of sleep architecture and sleep physiology in older adulthood. However, such conceptualizations of aging arise from group-averaged cross-sectional polysomnography data. What was the approach to solving the problem? We used a large-sample, longitudinal dataset to investigate individual trajectories in spectral power. Keep reading
Overview Obstructive sleep apnea (OSA) affects more than 20% of the global adult population, with approximately 80% of cases remaining undiagnosed. Although home sleep test solutions have existed for almost two decades, they suffer from significant limitations. A recent review reported a misdiagnosis rate of 39% for home sleep tests. Keep reading
Overview 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
Overview 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
Overview 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
Overview 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
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 Overview PyActigraphy is an open-source Python software for actigraphy and light data analysis. Keep reading