NIDRA 0.2.3

NIDRA v0.2.3 - super simple sleep scoring An easy way to use powerful machine learning models to autoscore sleep recordings with excellent accuracy. No programming required, but Python endpoints are available. NIDRA can accurately score recordings from 2-channel EEG wearables such as ZMax (using ezscore-f models), as well as full PSG recordings (using U-Sleep 2.0 via sleepyland). Please see the NIDRA Manual for a detailed user guide, installation, and examples. Download the NIDRA GUI installer for Windows 10/11 Or install from PyPI: (clean virtual environment recommended) pip install nidra Then launch the GUI: nidra Or use as Python package: import NIDRA scorer = NIDRA.scorer( type = 'psg', input = '/path/to/recording.edf' ) scorer.score()

Features

• Uses state-of-the-art, validated, high-accuracy deep learning models to reliably classify sleep stages.
• ezscore-f models for 2-channel EEG wearables (e.g., ZMax).
• u-sleep-nsrr-2024 (U-Sleep 2.0) model for full polysomnography.
• Simple point-and-click interface (GUI) for non-programmers.
• Flexible NIDRA.scorer() endpoint for developers and data pipelines.
• Autoscore single or multiple files or folders.
• Accepts in-memory numpy arrays for real-time applications.
• Automatic channel detection.
• Outputs sleep stages, classifier probabilities (hypnodensity), and sleep statistics.
• Visual reports with spectrograms and hypnograms.
• Runs on Windows, macOS, and Linux.

How to cite NIDRA

If you use NIDRA in your research, please cite both the NIDRA software itself and the paper for the specific model you used.

Zerr, P. (2025). NIDRA: super simple sleep scoring. GitHub. https://github.com/paulzerr/nidra

Attribution

ezscore-f (ez6 and ez6moe) models were developed by Coon et al., see:

Coon WG, Zerr P, Milsap G, Sikder N, Smith M, Dresler M, Reid M.
ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG.

https://www.biorxiv.org/content/10.1101/2025.06.02.657451v1
https://ezgithub.com/coonwg1/ezscore

U-Sleep models were developed by Perslev et al., see:

Perslev, M., Darkner, S., Kempfner, L., Nikolic, M., Jennum, P. J., & Igel, C. (2021).
U-Sleep: resilient high-frequency sleep staging.

https://www.nature.com/articles/s41746-021-00440-5
https://github.com/perslev/U-Time

The U-Sleep model weights used in this repo were re-trained by Rossi et al., see:

Rossi, A. D., Metaldi, M., Bechny, M., Filchenko, I., van der Meer, J., Schmidt, M. H., ... & Fiorillo, L. (2025).
SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models.

https://arxiv.org/abs/2506.08574v1
https://github.com/biomedical-signal-processing/sleepyland

License

This project is licensed under the MIT License. See the LICENSE file for details.