AI在共病睡眠障碍分期中的泛化能力差距

摘要

精准睡眠分期对脑卒中患者阻塞性睡眠呼吸暂停（OSA）与低通气的诊断至关重要。尽管多导睡眠监测（PSG）结果可靠，但存在成本高昂、操作繁复且需人工判读的局限性。虽然深度学习技术已实现健康人群基于脑电图（EEG）的自动睡眠分期，但我们的分析表明该技术对睡眠结构紊乱的临床人群泛化能力较差。通过Grad-CAM可解释性技术，我们系统论证了这一局限性。本文发布新标注的缺血性脑卒中临床数据集iSLEEPS（将公开共享），并评估了SE-ResNet结合双向LSTM模型在单通道EEG睡眠分期中的表现。如预期所示，健康人群与患者间的跨领域模型性能较差。结合临床专家反馈的注意力可视化显示，模型在患者数据中聚焦于缺乏生理学意义的EEG区域。统计与计算分析进一步证实健康人群与缺血性脑卒中队列存在显著睡眠结构差异，强调需在临床验证后部署具备受试者感知或疾病特异性的模型。论文摘要与代码详见https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/

English

Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep learning enables automated EEG-based sleep staging in healthy subjects, our analysis shows poor generalization to clinical populations with disrupted sleep. Using Grad-CAM interpretations, we systematically demonstrate this limitation. We introduce iSLEEPS, a newly clinically annotated ischemic stroke dataset (to be publicly released), and evaluate a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging. As expected, cross-domain performance between healthy and diseased subjects is poor. Attention visualizations, supported by clinical expert feedback, show the model focuses on physiologically uninformative EEG regions in patient data. Statistical and computational analyses further confirm significant sleep architecture differences between healthy and ischemic stroke cohorts, highlighting the need for subject-aware or disease-specific models with clinical validation before deployment. A summary of the paper and the code is available at https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/

AI在共病睡眠障碍分期中的泛化能力差距

AI Generalisation Gap In Comorbid Sleep Disorder Staging

摘要

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