[2603.23582] AI Generalisation Gap In Comorbid Sleep Disorder Staging

Computer Science > Machine Learning arXiv:2603.23582 (cs) [Submitted on 24 Mar 2026] Title:AI Generalisation Gap In Comorbid Sleep Disorder Staging Authors:Saswata Bose, Suvadeep Maiti, Shivam Kumar Sharma, Mythirayee S, Tapabrata Chakraborti, Srijitesh Rajendran, Raju S. Bapi View a PDF of the paper titled AI Generalisation Gap In Comorbid Sleep Disorder Staging, by Saswata Bose and 6 other authors View PDF HTML (experimental) Abstract: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 wit...