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.

Inclusion Criteria

• 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.

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.

Patients evaluated for sleep disorders, including treatment-resistant chronic insomnia, sleep-wake rhythm disorders, chronic fatigue, and/or patients with epilepsy presenting with sleep disturbances