Effective contextual feature fusion and individualized information for automated sleep staging

Polysomnography (PSG)-based sleep stage interpretation is crucial for diagnosing sleep disorders. Over the past decade, scholars have shown that machine learning offers a faster and more convenient alternative to manual visual interpretation of sleep stages and patterns. However, neglecting contextual features and individual case differences has hindered model application in new sleep staging cases. In this paper, we propose a sleep staging model that integrates contextual feature fusion and an individualized framework. The model incorporates weighted features from multiple epochs into the scoring process, enabling accurate scoring of 30-second epoch signals. Our individualized framework is tailored for emerging cases in real-world scenarios. It aggregates unique case information to derive individualized pseudo-labels, significantly enhancing automatic sleep staging accuracy through non-independent training. This strategy effectively addresses model degradation caused by differences between training cases and single new cases. To demonstrate our approach's efficacy, we evaluated our automated sleep staging algorithm on the Sleep-EDF-20 and Sleep-EDF-78 datasets, achieving accuracy of 85.3% and 80.8%, respectively. Furthermore, our individualized framework achieved 79.1% accuracy on the UCD dataset. These results underscore its potential as an effective tool for sleep stage classification, supporting physicians and neurologists in diagnosing sleep disorders. The proposed framework is lightweight and suitable for integration into clinical decision support system for sleep medicine, with a clear pathway for collaboration with routine laboratory scoring processes to support practical deployment.