SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning

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.

Intra- and Inter-epoch Temporal Context Network (IITNet) for Automatic Sleep Stage Scoring

This study proposes a novel deep learning model, called IITNet, to learn intra- and inter-epoch temporal contexts from a raw single channel electroencephalogram (EEG) for automatic sleep stage scoring. When sleep experts identify the sleep stage of a 30-second PSG data called an epoch, they investigate the sleep-related events such as sleep spindles, K-complex, and frequency components from local segments of an epoch (sub-epoch) and consider the relations between sleep-related events of successive epochs to follow the transition rules. Inspired by this, IITNet learns how to encode sub-epoch into representative feature via a deep residual network, then captures contextual information in the sequence of representative features via BiLSTM. Thus, IITNet can extract features in sub-epoch level and consider temporal context not only between epochs but also in an epoch. IITNet is an end-to-end architecture and does not need any preprocessing, handcrafted feature design, balanced sampling, pre-training, or fine-tuning. Our model was trained and evaluated in Sleep-EDF and MASS datasets and outperformed other state-of-the-art results on both the datasets with the overall accuracy (ACC) of 84.0% and 86.6%, macro F1-score (MF1) of 77.7 and 80.8, and Cohen's kappa of 0.78 and 0.80 in Sleep-EDF and MASS, respectively.