pretty_name: >-
DREAMT: Dataset for Real-time sleep stage Estimation using Multisensor
wearable Technology
tags:
- timeseries
- health
- sleep-staging
- wearables
- physionet
- medical
license: other
task_categories:
- other
Dataset Description
DREAMT (Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology) is a dataset designed to facilitate the development and evaluation of machine learning models for sleep stage estimation using data from multisensor wearable devices.
- Version: 2.1.0
- Repository: PhysioNet: DREAMT v2.1.0
Access Policy & Licensing
Due to the sensitive nature of health data, this dataset is restricted and cannot be downloaded directly without authorization.
- Access Policy: Only registered PhysioNet users who sign the specified Data Use Agreement (DUA) can access the data files.
- License: PhysioNet Restricted Health Data License 1.5.0
- Data Use Agreement: PhysioNet Restricted Health Data Use Agreement 1.5.0
Before accessing the data, please visit the dataset page on PhysioNet and follow the instructions to sign the DUA.
Citation Information
If you use this dataset in your research, you must include the following citations.
1. Cite the dataset resource:
Wang, K., Yang, J., Shetty, A., & Dunn, J. (2025). DREAMT: Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology (version 2.1.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/7r9r-7r24
2. Cite the original publication:
Will Ke Wang, Jiamu Yang, Leeor Hershkovich, Hayoung Jeong, Bill Chen, Karnika Singh, Ali R Roghanizad, Md Mobashir Hasan Shandhi, Andrew R Spector, Jessilyn Dunn. (2024). Proceedings of the fifth Conference on Health, Inference, and Learning, PMLR 248:380-396.
3. Include the standard citation for PhysioNet:
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.