PREDiction of Different Variants of Sleep Stages for the Diagnosis Support of Chronic Insomnia and Epilepsy
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,500
- 主要终点
- Prediction accuracy of sleep stages and sub-stages
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
- •Age ≥18 and ≤65 years at the time of the polysomnography recording.
排除标准
- •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.
结局指标
主要结局
Prediction accuracy of sleep stages and sub-stages
时间窗: 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.
次要结局
- 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))