A patient's apnea-hypopnea index (AHI) determines whether or not they are eligible for reimbursable treatment. But how certain can we be that a patient's AHI is actually above or below a clinical threshold? In this study using 2049 subjects from MESA dataset from NSRR, we develop a metric for computing a confidence interval around a patient's AHI. Keep reading
Brigham and Women's Hospital, June 9, 2020 – The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health (NIH), has announced that the National Sleep Research Resource (NSRR), hosted by Brigham and Women's Hospital, was awarded a five-year contract, facilitating an expansion of capabilities and providing support for increased community involvement. Keep reading
The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b), which began in 2010, studies pregnant women who will be delivering for the first time (nulliparous women). This prospective cohort study evaluates the underlying, interrelated mechanisms of several common adverse pregnancy outcomes, which can be unpredictable in women who have little or no pregnancy history, to help guide their treatment. Keep reading
To paraphrase the adage, a picture is worth a thousand numbers. In order to investigate some basic properties of NSRR datasets, here we generate a number of whole-dataset visualizations. To make sense of these images, we’ll employ a remarkably complex computational pattern recognition and dimension reduction framework, a.k.a. the human visual system. Keep reading
The NSRR is revamping its Tools pages and needs your help! Have you developed a tool for the analysis of sleep data that you'd like others to know about and use? Do you have some tricks and tips for using existing packages that you'd like to share? What about a write-up listing your favorite tools, explaining how you use them and what's good about them? Or perhaps you'd like to share some data analytic problems that aren't met by existing tools? If so, we'd love for you to submit a gues. Keep reading
Visualizing data is almost always useful, but this can be difficult when you have a lot of complex data. This is where dimension reduction techniques can play an important role. As described here, we applied one such technique to sleep EEG spectra from over 16,000 individuals in the NSRR, to get a feel for some of the sources of individual differences (both physiological and artefactual) in these data. Keep reading
Luna is a C/C++ library focused on the analysis of large numbers of sleep studies, such as those available from the NSRR. Luna is completely open-source and not dependent on proprietary software. If you're interested in analyzing sleep signals from the NSRR, you might want to start by looking at Luna. Currently, there is a command-line tool (lunaC) and an extension library for R (lunaR). Keep reading
Many analyses of sleep EEG data are effectively agnostic to the polarity of the EEG signal. That is, you could flip the signal (i.e. multiply every sample value by -1) and still obtain equivalent results, e.g. from most spectral analyses. For certain analyses that consider the phase of a signal, however, it will in fact matter that the polarity of the signal is correct. Keep reading
The NSRR gem v5.0.0 has been released. This new version of the gem support Ruby 2.4.6 or greater, and has added support for Ruby 2.6.3. To update to the latest version of the gem, type: gem install nsrr --no-document Please note that we are also dropping support for v0.3.0 and v0.4.0 of the gem that rely on an older data access API that is being removed. Leave a reply below or email us at support@sleepdata.org if you have any questions or issues using the new release. Keep reading
Spout is an open-source Ruby tool that helps the National Sleep Research Resource team curate, manage, and version-control data dictionaries that describe underlying datasets on the site. These data dictionaries provide metadata about the variables (columns) within a dataset. A well-defined data dictionary is an essential tool for researchers who want to understand and analyze a given dataset. Background The NSRR team started development on Spout in 2013. Version 1.0. Keep reading