DOI: 10.5281/zenodo.16919070
title: Aalborg University Wearable Sleep Study (AAUWSS)

description: Abstract

This project contains an overnight stay of 13 subjects wearing PSG and the wearable Empatica E4. The subject's sleep was scored by a manual rater according to the AASM guidelines. Code to process these files is available on the accompanying GitHub for the paper “Towards Real-Time Sleep Stage Classification: A Deep Learning Approach Leveraging PPG and ECG”. This dataset will be referred to as the Aalborg University Wearable Sleep Study (AAUWSS).

Background

There is a lack of consumer sleep technology datasets that are gathered alongside a PSG and sleep-staged according to the AASM guidelines. The focus of this dataset was to use ECG and PPG data, but for completeness’s sake, the rest of the data gathered is also added, as the other sensors might aid further research into sleep stage classification. The data in this study were used to classify sleep using deep learning.

Methods

We gathered 13 subjects with no prior diagnosed sleep issues and had them stay overnight sleeping with a PSG, which included EEG, ECG, EMG, and EOG. They simultaneously wore the Empatica E4 wearable sensor which includes a PPG, accelerometer, EDA, and temperature sensors. The PSG recordings were sleep staged according to the AASM guidelines by a single rater. A more detailed description of the subjects can be found in "Towards Real-Time Sleep Stage Classification: A Deep Learning Approach Leveraging PPG and ECG". Subject 12 and 13 had their PSG reboot during their overnight stay, so part of their data is missing, which is why they have multiple PSG recordings. The files used to train and test the sleep stage classification model can be found in the “aligned_sleep_data_set”. The data was gathered at Respiration Center West at Aarhus University Hospital between the 24th of March and the 4th of April 2023.

Data Description

The data is structured in the following way:

Sleep_Study_Participant_info.csv contain anonymized data of the subjects which includes age, gender, Epworth Sleepiness Scale, date of participation, and subject ID.

Annotations:

Contains the sleep stage annotation for each sleep epoch. The data is stored in an .xslx file where each row is a sleep epoch. It contains the columns [Event Start Time, Sleep Stage, Epoch Number, Subject]. Sleep is classified as Wake, N1, N2, N3, REM and Missing for when sleep staging was not possible.

Empatica:

TEMP.csv

Data from temperature sensor expressed degrees on the Celsius (°C) scale.

EDA.csv

Data from the electrodermal activity sensor expressed as microsiemens (μS).

BVP.csv

Data from photoplethysmograph.

ACC.csv

Data from 3-axis accelerometer sensor. The accelerometer is configured to measure acceleration in the range [-2g, 2g]. Therefore the unit in this file is 1/64g.

Data from x, y, and z axis are respectively in first, second, and third column.

IBI.csv

Time between individuals heart beats extracted from the BVP signal.

No sample rate is needed for this file.

The first column is the time (respect to the initial time) of the detected inter-beat interval expressed in seconds (s).

The second column is the duration in seconds (s) of the detected inter-beat interval (i.e., the distance in seconds from the previous beat).

HR.csv

Average heart rate extracted from the BVP signal.The first row is the initial time of the session expressed as unix timestamp in UTC.

The second row is the sample rate expressed in Hz.

tags.csv

Event mark times.

Each row corresponds to a physical button press on the device; the same time as the status LED is first illuminated.

The time is expressed as a unix timestamp in UTC and it is synchronized with initial time of the session indicated in the related data files from the corresponding session.

Edfs:

Contains the PSG recordings stored in the .edf format. These contains EEG, EOG, ECG, and EMG recorded at 200 Hz. An accompanying file “edf_channel_types.json” specify which channels correspond to which sensor.

aligned_sleep_data_set

Contains the ECG and PPG recordings synchronized and aligned with their respective sleep stage labels. Subject 12 and 13 had their PSGs reboots during their overnight stay, which is why they have multiple .edf files. In the aligned dataset these have been stitched together into one continuous file denoting the missing segments. The files are stored as a pickle file and contain where each row is 30 second sleep epoch. The columns to correspond to the samples so for the ECG files there er 30*200Hz (6000) columns and for the PPG there are 30*64 Hz (1920) columns. Further there are three additional columns appended for the labels, epoch number and the actual time stamps for each sample. The folder “alignment_figures” contains figures showing how long each recording is and at what point they were aligned.

Usage 

The software used to process the AAUWSS dataset can be found at the accompanying GitHub for the paper github.com/sdjanian/sf_sleep . Specifically the file “aauwss_sleep_dataset_alignment.py” was used to process and align the ECG and PPG recordings. 

Acknowledgements

This study was funded by SoundFocus ApS and Innovation Fund Denmark under grant number 1044-00079B. The sleep study was conducted in collaboration with Dr. Pia Dreyer, Aarhus University, and Respiration Center West at Aarhus University Hospital.