SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules

Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover, Jimeng Sun

Research output: Contribution to journalConference articlepeer-review


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 κ.

Original languageEnglish (US)
Pages (from-to)721-739
Number of pages19
JournalProceedings of Machine Learning Research
StatePublished - 2019
Externally publishedYes
Event4th Machine Learning for Healthcare Conference, MLHC 2019 - Ann Arbor, United States
Duration: Aug 9 2019Aug 10 2019

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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