A commonly used AHI variable in SHHS is: https://sleepdata.org/datasets/shhs/variables/ahi_a0h4
If you are using the XML files then you can add up the number of "Apnea" and "Hypopnea" events to compute your own indices.
Thanks for your interest in the resource. To access data files you need an approved data request. Please click Request Data Access next to the dataset of interest from the datasets index. Most data requests are reviewed and receive a response within 1-2 weeks of submission.
Note: HAASSA has additional access requirements, so I might suggest starting with a different dataset (e.g. SHHS, MESA).
Thanks for your interest in the resource. Have a look around some of our datasets, particularly the montage/sampling pages, e.g. CFS, MESA, etc. Many of the datasets will have plethysmography and ECG, though the participant populations vary in their levels of healthiness. You may supplement your analysis with other variables (e.g. medical history) to decide which participants to include.
Hey - sorry about that. HCHS used a sleep monitor with limited capabilities. I see your new data request came through, hopefully you'll hear about that one soon. Thanks for using the site!
The "Polysomnography introduction" pages (e.g. https://sleepdata.org/datasets/shhs/pages/05-polysomnography-introduction.md) have links that describe the XML files.
Thanks for the inquiry. I will email a dataset to you with the start month of each subject's actigraphy recording.
The XML annotation files contain timestamped markers for apneas and hypopneas. For SHHS Visit 1, see here - https://sleepdata.org/datasets/shhs/files/polysomnography/annotations-events-profusion/shhs1
I received a response from the analyst. Posted below.
Does this have to do with artefacts?
Why did you decide exactly for 0.35, 2.5, 180 and 1000? Are these set empirically by you or based on a paper?
They were set empirically.
These thresholds were set empirically with the objective of removing artifacts.
Even though automated QRS annotations were corrected as appropriate by a trained technician, a residual number of beats could have been incorrectly annotated. NN intervals < 0.35 s are artifact because they fall on the refractory period of the heart. There is also a very high chance that NN intervals > 2.5 s (heart rate less than 24 bpm) are misdetections rather then long pauses. These thresholds were meant to exclude misdetections and mislabeled beats.
“180”: 180 beats in 5 minutes corresponds to a heart rate of 36 bpm. If a 5-min window did not have at least 180 beats most likely that was due to artifact and/or the presence of non-sinus beats. For some perspective, the population median [25th and 75th percentiles] of the average NN interval was 939 [859 – 1033] ms, which in terms of heart rate corresponds to 63.9 [58 – 69.8] bpm.
Note that HRV is a technique that only applies to NN interval time series. Furthermore, there is no consensus on how to perform frequency analysis of “discontinuous” (due to the deletion of non-sinus beats) time series. Thus we wanted to limit the number of these windows.
“1000”: Participants with less than 1000 NN intervals (~15 mins) over the full night were immediately excluded. Either they were not in sinus rhythm or signal quality was an issue. Criteria for analyzing participants with at least 2h of combined N1, N2, N3-N4, REM was based on the idea that if we wanted to compare HRV for different sleep stages we needed a minimum amount of data. In addition, for the generation of full night (from sleep onset to sleep termination) summary statistics we wanted to avoid putting together those who spend most of the sleep period awake with those who slept “much more”.
Thanks for your inquiry. I reached out to the leader of the MESA HRV analysis with some of your questions. I will post again when I hear back.
Thanks for your inquiry. Your course of action is exactly what I would recommend. We believe the respiratory event annotations and timestamps are generally correct.
The "ahi_a0h3a" variable was computed from a set of component variables that were output soon after the original scoring of the study (i.e. between 1995-2005 for SHHS1/SHHS2). These component variables should be more intact than the XML annotation exports, which were done many years after in a different version of the Profusion software. Hence, we have a component variable that tells us "# of hypopneas with >=4% desaturation", instead of having to try and recompute this tally from the underlying XML file, which we know for certain has degraded SpO2 information, so to speak.
I seem to recall discussion about the SpO2 desaturations being shifted 30 seconds, though I can't say for certain. Regardless, our suggestion is to "re-detect" the SpO2 desaturations and align/link them with the scored respiratory events for analyses like the one you describe.
Good luck and stay well!