Not Yet RecruitingNot Applicable

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

1,500 participantsStarted 2026-06

Plain-language summary

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

Age range18 Years – 65 Years

SexALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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.

What they're measuring

Prediction accuracy of sleep stages and sub-stages

Timeframe: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)

Trial details

NCT IDNCT07547501

SponsorAssistance Publique - Hôpitaux de Paris

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2027-03

Contact for this trial

Vincent Navarro, MD, PhD

+33 1 42 16 18 11 vincent.navarro@aphp.fr