  Automatic sleep scoring is essential for the diagnosis and treatment of sleep
disorders and enables longitudinal sleep tracking in home environments.
Conventionally, learning-based automatic sleep scoring on single-channel
electroencephalogram (EEG) is actively studied because obtaining multi-channel
signals during sleep is difficult. However, learning representation from raw
EEG signals is challenging owing to the following issues: 1) sleep-related EEG
patterns occur on different temporal and frequency scales and 2) sleep stages
share similar EEG patterns. To address these issues, we propose a deep learning
framework named SleePyCo that incorporates 1) a feature pyramid and 2)
supervised contrastive learning for automatic sleep scoring. For the feature
pyramid, we propose a backbone network named SleePyCo-backbone to consider
multiple feature sequences on different temporal and frequency scales.
Supervised contrastive learning allows the network to extract class
discriminative features by minimizing the distance between intra-class features
and simultaneously maximizing that between inter-class features. Comparative
analyses on four public datasets demonstrate that SleePyCo consistently
outperforms existing frameworks based on single-channel EEG. Extensive ablation
experiments show that SleePyCo exhibits enhanced overall performance, with
significant improvements in discrimination between the N1 and rapid eye
movement (REM) stages.
