Academic

AI Generalisation Gap In Comorbid Sleep Disorder Staging

arXiv:2603.23582v1 Announce Type: new 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 he

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Saswata Bose, Suvadeep Maiti, Shivam Kumar Sharma, Mythirayee S, Tapabrata Chakraborti, Srijitesh Rajendran, Raju S. Bapi · · 1 min read · 3 views
#cs.LG #cs.AI

arXiv:2603.23582v1 Announce Type: new 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 with clinical validation before deployment. A summary of the paper and the code is available at https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/

Executive Summary

This article investigates the limitations of deep learning-based AI models in generalizing sleep staging from healthy subjects to clinical populations with disrupted sleep, specifically in ischemic stroke patients. Using a newly clinically annotated dataset, iSLEEPS, and a SE-ResNet plus bidirectional LSTM model, the authors demonstrate poor cross-domain performance and highlight the importance of subject-aware or disease-specific models. Statistical and computational analyses reveal significant sleep architecture differences between healthy and ischemic stroke cohorts, underscoring the need for clinical validation before deploying AI models in clinical settings. The study's findings have significant implications for the development and implementation of AI-powered sleep staging tools in healthcare.

Key Points

  • ▸ Deep learning-based AI models struggle to generalize sleep staging from healthy subjects to clinical populations with disrupted sleep.
  • ▸ The study introduces iSLEEPS, a clinically annotated ischemic stroke dataset, and evaluates a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging.
  • ▸ Attention visualizations show the model focuses on physiologically uninformative EEG regions in patient data, highlighting the need for subject-aware or disease-specific models.

Merits

Strength in Methodology

The study's use of Grad-CAM interpretations, attention visualizations, and clinical expert feedback provides a comprehensive understanding of the model's limitations and strengths.

Contribution to Field

The introduction of iSLEEPS, a clinically annotated ischemic stroke dataset, fills a significant gap in the existing literature on sleep staging in clinical populations.

Demerits

Limitation in Generalizability

The study's findings may not be generalizable to other clinical populations with disrupted sleep, highlighting the need for further research in this area.

Insufficient Discussion of Potential Solutions

The article could benefit from a more in-depth discussion of potential solutions, such as the development of subject-aware or disease-specific models, to address the generalization gap.

Expert Commentary

This study highlights the critical importance of considering the limitations of AI models in generalizing to clinical populations with disrupted sleep. The findings have significant implications for the development and implementation of AI-powered sleep staging tools in healthcare. While the study's methodology is robust, the lack of discussion on potential solutions to address the generalization gap is a notable limitation. To address this, researchers and developers should prioritize the development of subject-aware or disease-specific models that can accurately capture the complexities of sleep staging in clinical populations. Furthermore, regulatory frameworks should be established to ensure the safe and effective deployment of AI-powered sleep staging tools in clinical settings.

Recommendations

  • ✓ Develop subject-aware or disease-specific models that can accurately capture the complexities of sleep staging in clinical populations.
  • ✓ Establish regulatory frameworks to ensure the safe and effective deployment of AI-powered sleep staging tools in clinical settings.

Sources

Original: arXiv - cs.LG

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