SleepAC: Less Dependency on Manual Annotations, More Reliable Sampling for Automatic Sleep Staging.

Abstract

Accurate sleep staging is essential for assessing sleep quality and diagnosing sleep disorders, yet it heavily depends on large-scale, expertly labeled datasets, which are costly and time-consuming to produce. While existing methods aim to reduce this reliance, they often utilize data from a limited number of subjects, thereby restricting data diversity and hindering model generalization. To address these challenges, we propose SleepAC, a novel model designed to reduce the dependence on extensive manual annotations. It employs an adaptive sample selection strategy that prioritizes informative and diverse samples, starting with simpler ones and gradually adding more complex ones, while incorporating sleep-specific factors., enabling accurate classification with fewer labeled samples. Furthermore, SleepAC integrates a contrastive learning framework that generates hard negative samples across different sleep stages, effectively enhancing the classification of transitional stages, which are particularly difficult due to limited annotations. Experiments on four public datasets demonstrate that SleepAC achieves competitive accuracy and F1-scores, attaining approximately 95% of the fully supervised performance using only 20% of labeled data. These results underscore its effectiveness in low-resource settings, showcasing promising generalization across complex sleep dynamics while significantly reducing annotation costs.