We have created a public Zotero publication list for the NSRR. Please feel free to reach out to us if you have a relevant publication using NSRR data that should be added to this list.
This list is also an opportunity to identify collaborations and areas of overlap in analysis of NSRR data.
Share your research data on the NSRR.
Demographics, anthropometry, questionnaires, outcomes, and more.
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