This is an online service for automated sleep staging of paediatric polysomnography (PSG) in children aged 2-18 years. It utilises machine learning models to classify all stages of sleep (including Stage Wake, N1, N2, N3, and REM) using any combination of EEG, EOG, and Chin EMG. Sleep staging data, including detailed per-epoch analysis, may be visualised through this platform and downloaded in various formats for use in commercial PSG software.

Learn more about the machine learning models used, including peer-reviewed validation studies.

Substantial emphasis is placed on explainable AI. Measures of uncertainty, global and local explanations, and data visualisation accompany ML-generated predictions. Furthermore, models include clinically relevant features that are familiar to sleep paediatricians.

Accepted uploads are European Data Format (EDF/EDF+) files exported from commercial PSG software. EDF files should be fully anonymised prior to upload. Pre-processing of EEG, EOG, and Chin EMG data is handled automatically by this platform.

No suitable PSG data on-hand? Sample open source PSG data are available to try.

This service is currently for academic and non-commercial purposes only. It is not approved for clinical use.

Models are trained and evaluated on PSG from a variety of open source paediatric sleep datasets. Performance cannot be guaranteed in children aged less than two years, children with atypical EEG, complex comorbidities, for poor quality recordings, or in adults.