发现健康人与中风患者睡眠脑电差异大，现有模型难通用。
AI Generalisation Gap In Comorbid Sleep Disorder Staging
- 构建新临床数据集iSLEEPS，用SE-ResNet+双向LSTM做单导睡眠分期。
- 跨群体性能差，患者数据上模型关注无生理意义的脑电区域。
- 强调需疾病特异性模型，适合中风睡眠研究与临床部署前验证者。
准确的睡眠分期对诊断中风患者的OSA和低通气至关重要。尽管多导睡眠图（PSG）可靠，但成本高、耗时且需人工评分。虽然深度学习可实现健康人群的自动化脑电睡眠分期，但我们的分析表明其在睡眠紊乱的临床人群中的泛化能力差。通过Grad-CAM解释，系统性地揭示了这一局限。本文引入iSLEEPS，一个新发布的缺血性中风患者临床标注数据集，并评估了基于SE-ResNet与双向LSTM的单通道脑电睡眠分期模型。如预期，健康与病患群体间的跨域性能不佳。注意力可视化结合临床专家反馈显示，模型在患者数据中关注的是生理上无意义的脑电区域。统计与计算分析进一步证实健康与缺血性中风队列间存在显著的睡眠结构差异，强调在部署前需采用受试者感知或疾病特异性模型并进行临床验证。
原文摘要 · Abstract (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/
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