Chronic InsomniaPREDSomADICENCT07547501
PREDiction of Different Variants of Sleep Stages for the Diagnosis Support of Chronic Insomnia and Epilepsy
An observational study of Chronic Insomnia and Epilepsy, run by Assistance Publique - Hôpitaux de Paris.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Chronic Insomnia, Epilepsy, Sleep Disorders
- How many people
- 1,500 participants
- Where
- No sites listed yet
- Study dates
- Jun 2026to Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
Jinmi BAEK contact
About this study
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
- Medical subject headings
- Sleep Initiation and Maintenance Disorders, Epilepsy, Sleep Wake Disorders
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)
Secondary outcomes (2)
Prediction accuracy of chronic insomnia profiles
Evaluation of the deep learning algorithms' accuracy in identifying and predicting chronic insomnia profiles based on the electroencephalographic (EEG) analysis of polysomnographies. Performance will be assessed by comparing the automated predictions against established clinical diagnoses using standard machine learning classification metrics (such as macro F1-score and Cohen's Kappa).
Measured: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)
Prediction accuracy of epilepsy profiles
Evaluation of the deep learning algorithms' accuracy in identifying and predicting different epilepsy profiles based on the electroencephalographic (EEG) analysis of polysomnographies. Performance will be assessed by comparing the automated predictions against established clinical diagnoses using standard machine learning classification metrics (such as macro F1-score and Cohen's Kappa).
Measured: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Apr 2026
- Study startJun 2026 (estimated)
- Primary completionMar 2027 (estimated)
- Study completionMar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chronic InsomniaStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- Idiap Research Institute, Switzerland
- Responsible party
- Sponsor
- Investigators
- Olivier Pallanca, MD, PhD, Study director, Idiap Research Institute, Switzerland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07547501
- Sponsor's protocol ID
- APHP241330
- Acronym
- PREDSomADICE
- Status last verified
- Apr 2026
- First submitted
- 17 Apr 2026
- Last updated
- 29 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 29 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- PREDiction of Different Variants of Sleep Stages for the Diagnosis Support of Chronic Insomnia and Epilepsy
- ClinicalTrials.gov ID
- NCT07547501
- Acronym / sponsor ID
- PREDSomADICE / APHP241330
- Conditions
- Chronic Insomnia, Epilepsy, Sleep Disorders
- Keywords
- Sleep staging, Polysomnography, EEG, Deep learning, Artificial intelligence, Sleep disorders, Chronic insomnia, Epilepsy
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- Idiap Research Institute, Switzerland
- Study start
- Jun 2026 (estimated)
- Primary completion
- Mar 2027 (estimated)
- Study completion
- Mar 2027 (estimated)
- First posted
- 23 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction accuracy of sleep stages and sub-stages
Time frame: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)
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.
2 secondary outcomes
Prediction accuracy of chronic insomnia profiles
Time frame: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)
Prediction accuracy of epilepsy profiles
Time frame: Single overnight polysomnography recording per participant (duration of approximately 8 to 12 hours)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Apr 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jinmi BAEK contact
Olivier Pallanca, MD, PhD study director
Idiap Research Institute, Switzerland
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Insomnia and Epilepsy, run by Assistance Publique - Hôpitaux de Paris.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 29 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Vincent Navarro, MD, PhD (+33 1 42 16 18 11, vincent.navarro@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Apr 2026.