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

TL;DR

MetaSTH-Sleep introduces a novel hypergraph-enhanced meta-learning framework for few-shot sleep stage classification, effectively capturing spatial-temporal relationships in EEG signals to improve generalization across subjects with limited labeled data.

Contribution

The paper presents a new meta-learning approach incorporating spatial-temporal hypergraphs to enhance sleep stage classification with minimal labeled samples, addressing inter-individual variability.

Findings

Significant accuracy improvements over existing methods

Effective modeling of spatial-temporal dependencies in EEG signals

Rapid adaptation to new subjects with few labeled samples

Abstract

Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sleep monitoring. 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.