MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification with Spatial-Temporal Hypergraph Enhanced Meta-Learning

• URL: http://arxiv.org/abs/2505.17142v1
• Date: Thu, 22 May 2025 07:09:03 GMT
• Title: MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification with Spatial-Temporal Hypergraph Enhanced Meta-Learning
• Authors: Jingyu Li, Tiehua Zhang, Jinze Wang, Yi Zhang, Yuhuan Li, Yifan Zhao, Zhishu Shen, Jiannan Liu,
• Abstract summary: We propose MetaSTH-Sleep, a few-shot sleep stage classification framework based on spatial-temporal hypergraph enhanced meta-learning.<n>Our approach enables rapid adaptation to new subjects using only a few labeled samples, while the hypergraph structure effectively models complex spatial interconnections and temporal dynamics simultaneously in EEG signals.
• Score: 15.98498716083678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate classification of sleep stages based on bio-signals is fundamental for automatic sleep stage annotation. Traditionally, this task relies on experienced clinicians to manually annotate data, a process that is both time-consuming and labor-intensive. In recent years, deep learning methods have shown promise in automating this task. However, three major challenges remain: (1) deep learning models typically require large-scale labeled datasets, making them less effective in real-world settings where annotated data is limited; (2) significant inter-individual variability in bio-signals often results in inconsistent model performance when applied to new subjects, limiting generalization; and (3) existing approaches often overlook the high-order relationships among bio-signals, failing to simultaneously capture signal heterogeneity and spatial-temporal dependencies. To address these issues, we propose MetaSTH-Sleep, a few-shot sleep stage classification framework based on spatial-temporal hypergraph enhanced meta-learning. Our approach enables rapid adaptation to new subjects using only a few labeled samples, while the hypergraph structure effectively models complex spatial interconnections and temporal dynamics simultaneously in EEG signals. Experimental results demonstrate that MetaSTH-Sleep achieves substantial performance improvements across diverse subjects, offering valuable insights to support clinicians in sleep stage annotation.

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