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

NCT07547501 | Not Yet Recruiting

• 1 other identifier: APHP241330
• Study Type: observational
• Target: 1,500
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

Sleep staging; Polysomnography; EEG; Deep learning; Artificial intelligence; Sleep disorders; Chronic insomnia; Epilepsy

Outcome Measures

Primary Outcomes (1)

• Prediction accuracy of sleep stages and sub-stages

  Evaluation of the deep learning model's performance in accurately classifying different sleep stages and sub-stages compared to expert manual scoring. The metrics used to characterize this outcome are the macro F1-score and/or Cohen's Kappa (κ) score, with a target prediction accuracy of \>80%. The macro F1-score measures the model's ability to correctly recognize each sleep stage while compensating for the imbalance between frequent and rare classes. Cohen's Kappa quantifies the degree of agreement between automatic predictions and human annotations by correcting for the agreement expected by chance. The combination of these two metrics offers a robust and balanced evaluation.

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

Secondary Outcomes (2)

• Prediction accuracy of chronic insomnia profiles

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

• Prediction accuracy of epilepsy profiles

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

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

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.

Sponsors & Collaborators

• Assistance Publique - Hôpitaux de Paris: lead
• Idiap Research Institute, Switzerland: collaborator

MeSH Terms

Conditions

Sleep Initiation and Maintenance Disorders; Epilepsy; Sleep Wake Disorders

Condition Hierarchy (Ancestors)

Sleep Disorders, Intrinsic; Dyssomnias; Nervous System Diseases; Mental Disorders; Brain Diseases; Central Nervous System Diseases; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Olivier Pallanca, MD, PhD

  Idiap Research Institute, Switzerland

  STUDY DIRECTOR

Central Study Contacts

Vincent Navarro, MD, PhD

CONTACT

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

Jinmi BAEK

CONTACT

jinmi.baek@aphp.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 17, 2026
• First Posted: April 23, 2026
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): March 1, 2027
• Study Completion (Estimated): March 1, 2027
• Last Updated: April 29, 2026

Record last verified: 2026-04