The Insitute of Systems and Robotics from Coimbra, Portugal (ISRUC) publishes a full sleep dataset under open-access terms . A dedicated website lists available files and documentation for all to download and use freely. As for many sleep resources like SleepEDF  or Sleepdata.org  , signals, scoring and metadata are distributed in separate files and differents formats.
This article provides an easy workflow to download, read and visualise the ISRUC sleep dataset. Examples are implemented using the R programming language  and the SleepR library  .
3 recordings subgroups of differents size and characteristics are available:
- Subgroup 1: 100 records from 100 subjects, many with sleep apnea.
- Subgroup 2: 8 subjects with 2 records each, to study changes between records.
- Subgroup 3: 10 records from 10 healthy subjects.
Records archives contains signals in EDF format  as long as scoring in Excel files. Metadata, or biodata, are distributed in Excel files. To download all A useful Sleepr function downloads and expands archives from the 3 subgroups.
# Install latest SleepR version. devtools::install_github("boupetch/sleepr")
## ## checking for file '/tmp/RtmpCuYkZ7/remotes6208a4e66d/boupetch-sleepr-f343c3d/DESCRIPTION' ... v checking for file '/tmp/RtmpCuYkZ7/remotes6208a4e66d/boupetch-sleepr-f343c3d/DESCRIPTION' (471ms) ## - preparing 'sleepr': ## checking DESCRIPTION meta-information ... v checking DESCRIPTION meta-information ## - excluding invalid files ## Subdirectory 'man' contains invalid file names: ## 'hypnogram.jpeg' 'powers.png' 'spectrogram.jpg' 'transitions.png' ## - checking for LF line-endings in source and make files and shell scripts ## - checking for empty or unneeded directories ## - building 'sleepr_0.1.0.tar.gz' ## ##
# Set dataset target directory target <- "/srv/data1/sleep/raw/isruc/"
# Dowload ISRUC dataset sleepr::download_isruc(target)
Reading and plotting sleep stages
Reading scored stages provides an easy way to take a first look at the data. For analysis purposes, sleeps records are traditionaly splited into 30 seconds epochs. These 30 seconds epochs are scored between 5 stages, following the American Association for Sleep Medicine (AASM) manual .
- AWA: Wake, the wake stage.
- REM: Rapid Eye Movement (REM) stage, or paradoxical sleep.
- N1: Non-REM Sleep 1, a transitional stage between sleep and wake.
- N2: Non-REM Sleep 2, the most encountered sleep stage.
- N3: Non-REM Sleep 3, deep sleep.
A hypnogram visualize these stages through the course of the night. Traditionaly, REM sleep is colored in red. The following code chunk plot the hypnogram from the first record from the first subgroup of the ISRUC database. The patient here suffers from obstructive sleep apnea, hence a fragmented sleep with many wake epochs.
hypnogram <- sleepr::read_events_isruc(paste0(target,"1/1"),1) sleepr::plot_hypnogram(hypnogram)
Database metadata and statistics
3 metadata files, one for each subgroup, contain many informations about the subject, the record and its recording conditions.
metadata <- sleepr::read_isruc_metadata(target)
From the global metadata files can be plotted availables stages, records durations and subjects age distributions across the whole database. These informations help assert the database quality.
A sleep database analysis starts by plotting stages distribution, or the number of epochs by sleep stages. Usually, N2 counts for the most epochs. Moreover, a large number of wake epochs could imply too long records, requiring further investigation.
Records durations should be consistent across the database. Vizualizing the distribution highlights outliers requiring attention.
Sleep analysis must take subjects age into account, as sleep evolves throughout lifespan . Over-representation of an age class will enlight class specific features. On the contrary, scattered ages can give an overbroad analysis.
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