AI Generalisation Gap In Comorbid Sleep Disorder Staging
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/
69cd52756cdd27348f354980
Saswata Bose
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2026-04-02T08:15:20.958Z
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Saswata Bose
SaswataBose
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SaswataBose
69cd52756cdd27348f354981
Suvadeep Maiti
69cd52756cdd27348f354982
Shivam Kumar Sharma
69cd52756cdd27348f354983
Mythirayee S
69cd52756cdd27348f354984
Tapabrata Chakraborti
69cd52756cdd27348f354985
Srijitesh Rajendran
69cd52756cdd27348f354986
Raju S. Bapi
AI Generalisation Gap In Comorbid Sleep Disorder Staging
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/
Deep learning models designed for healthy subjects show poor performance when applied to stroke patients due to fundamental differences in sleep architecture, necessitating disease-specific approaches.
SE-ResNet bidirectional LSTM EEG sleep staging Grad-CAM attention visualization