A universal, open-source, high-performance tool for automated sleep staging
Abstract
The creation of a completely automated sleep-scoring system that is highly accurate, flexible, well validated, free and simple to use by anyone has yet to be accomplished. In part, this is due to the difficulty of use of existing algorithms, algorithms having been trained on too small samples, and paywall demotivation. Here we describe a novel algorithm trained and validated on +27,000 hours of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy matching or exceeding human accuracy and interscorer agreement no matter the population kind. The software is easy to use, computationally low-demanding, open source, and free. Such software has the potential to facilitate broad adoption of automated sleep staging with the hope of becoming an industry standard.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 367
- FWCI
- 22.83
- Field percentile
- top 1%
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- An accessible and versatile deep learning-based sleep stage classifier
Jevri Hanna, Agnes Flöel·Frontiers in NeuroinformaticsPeer-reviewed·Mar 1, 2023
- Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data
Carlos S. Caldart, Raymond E.A. Sanchez, … Horacio O. de la Iglesia·bioRxiv·Jul 6, 2020
- Nyx: a flexible framework for sleep scoring across species, lifespan and modalities
Letizia Signorelli, Solomiia Korchynska, … Charlotte N. Boccara·bioRxiv·Jul 28, 2026
- The potential of ensemble-based automated sleep staging on single-channel EEG signal from a wearable device
Federico Salfi, Domenico Corigliano, … M. Ferrara·Journal of Sleep ResearchPeer-reviewed·Jan 19, 2026
- Robust, automated sleep scoring by a compact neural network with distributional shift correction
Zeke Barger, Charles G. Frye, … Kristofer E. Bouchard·PLoS ONEPeer-reviewed·Dec 13, 2019
- Lack of Consensus for Manual Mouse Sleep Scoring Limits Implementation of Automatic Deep Learning Models
Laura Rose, Alexander Neergaard Zahid, … Birgitte Rahbek Kornum·bioRxiv·Mar 30, 2026