SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning

Abstract

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.

14 pages, 3 figures, 8 tables

References in corpus (4)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
• SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

Cited by in corpus (3)

• Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
• DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
• NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring