PREDiction of Different Variants of Sleep Stages for the Diagnosis Support of Chronic Insomnia and Epilepsy

Starting soon

Conditions studied: Chronic Insomnia, Epilepsy, Sleep Disorders

In brief

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.

Key facts

Study ID

NCT07547501

Run by

Assistance Publique - Hôpitaux de Paris

People needed

1500

Starts

2026-06-01

Expected to finish

2027-03-01

Last updated by the study team

2026-04-29

Who can join

Age: 18 and older, up to 65. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 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.

You may not qualify if…

• 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.

Full record on ClinicalTrials.gov

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