The objective of this study is to develop and validate deep learning algorithms for automated sleep stage and sub-stage classification using overnight polysomnography data. The models will be trained and evaluated on at least three independent datasets to ensure generalizability.
\- Primary Outcome Measure : Accuracy of deep learning-based sleep stage classification compared to expert manual scoring (\>80% target agreement), evaluated across multiple polysomnography datasets including AP-HP (Assistance Publique - Hôpitaux de Paris) data.
This is a retrospective, observational study.
Patients with chronic insomnia and/or epilepsy who underwent polysomnography in a neurophysiology or neurology setting under the responsibility of Pr Navarro between 01 September 2011 and 31 December 2024.
Age ≥18 and ≤65 years at the time of the polysomnography recording.
Severe psychiatric disorder, including decompensated psychotic disorder, manic episode, or major depressive episode with melancholic features.
Use of continuous positive airway pressure (CPAP) therapy during the night of recording.
Patient refusal or documented opposition to data use.
Assistance Publique - Hôpitaux de Paris
Lead sponsor
Idiap Research Institute, Switzerland
Collaborator