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Scoring sleep stages in polysomnography recordings is a time-consuming taskplagued by significant inter-rater variability. Therefore, it stands to benefitfrom the application of machine learning algorithms. While many algorithms havebeen proposed for this purpose, certain critical architectural decisions havenot received systematic exploration. In this study, we meticulously investigatethese design choices within the broad category of encoder-predictorarchitectures. We identify robust architectures applicable to both time seriesand spectrogram input representations. These architectures incorporatestructured state space models as integral components, leading to statisticallysignificant advancements in performance on the extensive SHHS dataset. Theseimprovements are assessed through both statistical and systematic errorestimations. We anticipate that the architectural insights gained from thisstudy will not only prove valuable for future research in sleep staging butalso hold relevance for other time series annotation tasks.

S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models