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Automatic sleep scoring is essential for the diagnosis and treatment of sleepdisorders and enables longitudinal sleep tracking in home environments.Conventionally, learning-based automatic sleep scoring on single-channelelectroencephalogram (EEG) is actively studied because obtaining multi-channelsignals during sleep is difficult. However, learning representation from rawEEG signals is challenging owing to the following issues: 1) sleep-related EEGpatterns occur on different temporal and frequency scales and 2) sleep stagesshare similar EEG patterns. To address these issues, we propose a deep learningframework named SleePyCo that incorporates 1) a feature pyramid and 2)supervised contrastive learning for automatic sleep scoring. For the featurepyramid, we propose a backbone network named SleePyCo-backbone to considermultiple feature sequences on different temporal and frequency scales.Supervised contrastive learning allows the network to extract classdiscriminative features by minimizing the distance between intra-class featuresand simultaneously maximizing that between inter-class features. Comparativeanalyses on four public datasets demonstrate that SleePyCo consistentlyoutperforms existing frameworks based on single-channel EEG. Extensive ablationexperiments show that SleePyCo exhibits enhanced overall performance, withsignificant improvements in discrimination between the N1 and rapid eyemovement (REM) stages.

SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning