AI在共病睡眠障碍分期中的泛化能力差距

摘要

精準的睡眠分期對於診斷中風患者的阻塞性睡眠呼吸中止症（OSA）與低通氣症至關重要。儘管多導睡眠圖（PSG）具有可靠性，但其成本高昂、耗費人力且需人工評分。雖然深度學習技術已能實現健康受試者基於腦電圖（EEG）的自動化睡眠分期，但我們的分析顯示該技術對睡眠結構紊亂的臨床人群泛化能力較差。通過Grad-CAM可視化解釋，我們系統性論證了這一局限性。本文介紹新標註的缺血性中風臨床數據集iSLEEPS（將公開釋出），並評估採用SE-ResNet與雙向LSTM架構的單通道EEG睡眠分期模型。如預期所示，健康與患病受試者間的跨領域模型性能表現不佳。結合臨床專家反饋的注意力可視化顯示，模型在患者數據中聚焦於缺乏生理學意義的EEG區段。統計與計算分析進一步證實健康人群與缺血性中風隊列存在顯著睡眠結構差異，強調了在部署前需建立具臨床驗證的個體適應性或疾病特異性模型。論文摘要與代碼獲取地址：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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