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.
Who can participate
Inclusion Criteria
1. 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.
2. Age ≥18 and ≤65 years at the time of the polysomnography recording. Presence of at least one of the following clinical conditions: chronic insomnia with attention disorder, chronic insomnia with unexplained chronic fatigue and/or excessive daytime sleepiness, or chronic insomnia with epilepsy.
Exclusion Criteria 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.
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