AI Generalisation Gap In Comorbid Sleep Disorder Staging
Summary: arXiv:2603.23582v1 Announce Type: cross
Accurate sleep staging is crucial for diagnosing obstructive sleep apnea (OSA) and hypopnea in patients who have suffered strokes. While polysomnography (PSG) is considered a reliable method for sleep analysis, it comes with significant drawbacks including high costs, labor-intensive processes, and the necessity for manual scoring. As deep learning techniques become increasingly prevalent, particularly in automating EEG-based sleep staging for healthy individuals, emerging evidence suggests these methods may not generalize well to clinical populations experiencing disrupted sleep patterns.
Key Findings
This article highlights a significant generalization gap in the application of AI for sleep staging in patients with comorbid sleep disorders, particularly those who have experienced ischemic strokes. The study employs Grad-CAM interpretations to systematically illustrate this limitation. Key findings include:
- Introduction of iSLEEPS, a newly clinically annotated ischemic stroke dataset that will be publicly released.
- Evaluation of a Single-Channel EEG Sleep Staging Model: The study assesses a model based on SE-ResNet combined with a bidirectional LSTM.
- Cross-Domain Performance Challenges: Results indicate poor performance when comparing healthy subjects to those with ischemic strokes.
- Attention Visualizations: Expert feedback revealed that the model tends to focus on EEG regions that do not provide physiologically informative data.
- Significant Sleep Architecture Differences: Statistical and computational analyses show marked differences in sleep architecture between healthy individuals and ischemic stroke patients.
The Need for Subject-Aware Models
The findings underscore the necessity for developing subject-aware or disease-specific models that undergo clinical validation before being deployed in real-world settings. The generalization gap observed in this study emphasizes the challenges faced when using AI-based methods that have primarily been trained on datasets comprising healthy subjects. As sleep disorders can significantly vary from one population to another, it becomes essential to tailor AI algorithms to accurately diagnose and treat patients with specific conditions.
Future Directions
In light of the findings presented, future research should focus on the following:
- Enhancing datasets like iSLEEPS to include diverse clinical populations for better model training.
- Developing AI models that explicitly account for the unique characteristics of EEG data from patients with sleep disorders.
- Collaborating with clinical experts to refine model training and interpretation, ensuring that AI tools are clinically relevant and actionable.
- Implementing robust validation protocols to test the efficacy of AI models in real-world clinical settings.
Conclusion
The study highlights the critical need for advancements in AI methodologies to bridge the generalization gap in sleep disorder staging. By addressing the challenges identified, researchers can pave the way for more effective diagnostic tools that improve patient outcomes in clinical settings. For more information, a summary of the paper and the code is available at iSLEEPS Explainability.