  Sleep staging is essential for sleep assessment and plays a vital role as a
health indicator. Many recent studies have devised various machine learning as
well as deep learning architectures for sleep staging. However, two key
challenges hinder the practical use of these architectures: effectively
capturing salient waveforms in sleep signals and correctly classifying
confusing stages in transitioning epochs. In this study, we propose a novel
deep neural network structure, TransSleep, that captures distinctive local
temporal patterns and distinguishes confusing stages using two auxiliary tasks.
In particular, TransSleep adopts an attention-based multi-scale feature
extractor module to capture salient waveforms; a stage-confusion estimator
module with a novel auxiliary task, epoch-level stage classification, to
estimate confidence scores for identifying confusing stages; and a context
encoder module with the other novel auxiliary task, stage-transition detection,
to represent contextual relationships across neighboring epochs. Results show
that TransSleep achieves promising performance in automatic sleep staging. The
validity of TransSleep is demonstrated by its state-of-the-art performance on
two publicly available datasets, Sleep-EDF and MASS. Furthermore, we performed
ablations to analyze our results from different perspectives. Based on our
overall results, we believe that TransSleep has immense potential to provide
new insights into deep learning-based sleep staging.
