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

NCT07547501 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2026-04-29

No results posted yet for this study

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.

Conditions

  • Chronic Insomnia
  • Epilepsy
  • Sleep Disorders

Sponsors & Collaborators

  • Idiap Research Institute, Switzerland

    collaborator UNKNOWN

  • Assistance Publique - Hôpitaux de Paris

    lead OTHER

Principal Investigators

  • Olivier Pallanca, MD, PhD · Idiap Research Institute, Switzerland

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-30
Primary Completion
2027-03-31
Completion
2027-03-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07547501 on ClinicalTrials.gov