SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
This addresses the need for interpretable sleep staging models to assist in diagnosing sleep disorders, offering an incremental improvement by integrating expert rules into deep learning for better transparency.
The paper tackles the problem of automating sleep staging for diagnosing sleep disorders by proposing SLEEPER, a method that combines deep learning with expert rules to create interpretable models, achieving about 85% ROC-AUC and 0.7 kappa, comparable to human experts and deep neural networks.
Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram (PSG) from a single night. Although deep learning models have demonstrated state-of-the-art performance in automating sleep staging, interpretability which defines other desiderata, has largely remained unexplored. In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models. In particular, SLEEPER utilizes sleep scoring rules and expert defined features to derive prototypes which are embeddings of PSG data fragments via convolutional neural networks. The final models are simple interpretable models like a shallow decision tree defined over those phenotypes. We evaluated SLEEPER using two PSG datasets collected from sleep studies and demonstrated that SLEEPER could provide accurate sleep stage classification comparable to human experts and deep neural networks with about 85% ROC-AUC and .7 kappa.