  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.
