  Scoring sleep stages in polysomnography recordings is a time-consuming task
plagued by significant inter-rater variability. Therefore, it stands to benefit
from the application of machine learning algorithms. While many algorithms have
been proposed for this purpose, certain critical architectural decisions have
not received systematic exploration. In this study, we meticulously investigate
these design choices within the broad category of encoder-predictor
architectures. We identify robust architectures applicable to both time series
and spectrogram input representations. These architectures incorporate
structured state space models as integral components, leading to statistically
significant advancements in performance on the extensive SHHS dataset. These
improvements are assessed through both statistical and systematic error
estimations. We anticipate that the architectural insights gained from this
study will not only prove valuable for future research in sleep staging but
also hold relevance for other time series annotation tasks.
