Beyond Sleep Staging: Advancing End-to-End Event Scoring in Sleep Medicine

Background: Sleep disorders are a global health concern, with polysomnography (PSG), the gold standard for diagnosis. Traditional PSG analysis involves manual annotation of various physiological signals, including EEG, EOG, EMG, ECG, airflow, and oximetry making the process time-consuming. Recent advances in deep learning have enabled automated sleep staging using EEG data. However, existing solutions are limited to specific data subsets. This study aims to expand deep-learning applications to utilize the full spectrum of PSG data for a comprehensive analysis of sleep-related events. Material and Methods: We developed an end-to-end machine learning system that leverages all available PSG sensor channels. Our approach scores 19 different events across six event groups, such as breathing patterns, heart rate, and movement during sleep. We explored two models: one trained separately per event group using selected PSG channels, and a joint model predicting all events simultaneously using all channels. To address overfitting issues in the joint model, we implemented curriculum learning, which introduces tasks in a structured manner to improve model generalization. Results: Systematic experimentation demonstrated that curriculum learning effectively reduced overfitting in the joint model, closing the performance gap between the joint and multiple models. Additionally, a qualitative analysis of channel importance highlighted the advantages of curriculum learning in model training. Our approach outperformed commercial systems based on hand-crafted rules, showing significant improvement in event scoring accuracy. Conclusion: This study presents the first comprehensive machine learning system utilizing full PSG data, bridging the gap between deep learning research and its practical application in clinical sleep medicine. The use of curriculum learning enhances model performance, offering a promising tool for more accurate and efficient diagnosis of sleep disorders.