I am not aware of any datasets like that available right now (here or elsewhere). We are starting some collaborations with researchers who do in-lab studies related to circadian rhythms. Some of these protocols involve forced desynchrony and varying light exposures. I don't have a firm idea about when any of these data might be posted, but you might want to check back occasionally. Thanks and good luck!
The scoring manuals should describe the scoring procedures for different datasets, e.g.
There isn't a "final diagnosis" per se since the sleep studies for most datasets were conducted for research purposes, so there wasn't an MD interpretation at the end. Users could try computing their own diagnosis variable from the indexes of interest and questionnaire data.
SHHS was an observational study, so participants were not explicitly chosen as case/control (or unhealthy/healthy; with a certain disease or without). As such, I think my recommendations from the discussion thread you linked are still accurate. If you want to stratify by Apnea-Hypopnea Index (AHI) severity, then AHI_A0H3 is an AHI variable that is very commonly used (in research and diagnostic settings). We create a lot of variations of the AHI for different research purposes, but AHI_A0H3 is arguably the "most important".
Thanks again for checking out the resource!
For most datasets you should be able to get an idea of the number of datasets/records by reading the documentation, dataset introduction pages, and looking at variable distributions. For instance, in SHHS, you could look at these links to get a sense of how many subjects participated at each visit.
Thanks for your interest in the site and good luck on your sleep stage classification project.
I just tested this from home and was getting around 50-60 Mbps, which is about what I would expect given my typical connection speeds from this location. I hope your IT team is able to able to help, otherwise it may just be some poor routing or something.
There should be no restrictions. We have not had reports from other users about slow download speeds.
How are you downloading files? Through the NSRR gem or through your web browser? I will make a note to test downloading from an external location to see if I am also affected.
The scorer reviewed mesaid = 2 and noted that there is artifact on the ECG channel that starts in epoch 156 and ends in epoch 158. The HRV reviewing/editing program (Somte) presumably censored the Rpoint data from those segments. I think it's reasonable to assume this (i.e. excluded ECG artifact) will be the common thread across other missing segments/areas of the files. Note that the ECG beats were only reviewed/analyzed in the sleep period between sleep onset and lights on.
Hope this helps in your work!
At a glance I see that the HRV CSV file does not contain any Rpoints from the 157th epoch (wake according to Profusion) for mesaid = 2. Would this account for your sequences "missing" a 0? There are 22 zeros in the HRV array and 23 in the Profusion array you posted. I was looking at the "epoch" column in the HRV CSV file. There would appear to be a 38.84 second "jump" in time (seconds column) between Rpoints.
My guess would be that this area was scrubbed/censored during the HRV Rpoint identification. Maybe there was artifact in the signal. I could have a PSG scorer here look it up to confirm. Let me know if this sort of missingness would account for the sequence shifts you are seeing across subjects.
Thanks for raising the issue. I will look into this. Can you post two IDs that don't have any mismatches (assuming you cut off longer sequence) and another ID (like mesaid = 2) where there are mismatches?
Is the sequence you posted starting at position/epoch 153 for mesaid = 2? Just wanted to confirm I'm looking at the same snippet.
The RDI* variables in SHHS are not necessarily "AHI + arousals". They are more like AHI variables, but all events (apneas and hypopneas) are required to be associated with a desaturation (of 4%, for instance, in RDI4P).
I assume there are people trying to "diagnose" sleep apnea using only an oximetry channel. Naturally, the ODI closely aligns with AHI in many cases.
Do you have any specific data questions? Or inconsistent data findings? I wasn't sure exactly what I was looking at in your image.
Note that if you derived "new" oxygen desaturations from the raw signal then you would want to be sure your methodology matched with whatever was used by the original sleep scoring software. Otherwise your new metrics would not be comparable.