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

Sponsor
Assistance Publique - Hôpitaux de Paris
Study ID
NCT07547501
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - 65 Years
Healthy Volunteers
Not accepted

Study Details

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 Dates

Start date
Jun 30, 2026
Status verified
Apr 2026
Primary completion
Mar 31, 2027
Completion
Mar 31, 2027

Study Design

Enrollment
1,500 participants (estimated)

Primary Outcome Measure

Prediction accuracy of sleep stages and sub-stages [ Time Frame: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours) ]

Central Contacts

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