Learning to Detect Sleep Micro-Events From Coarse Sleep Stage Annotations

Sleep micro-events, such as sleep spindles and K-complexes, are closely associated with neurological cognitive functions. While artificial intelligence (AI)-assisted sleep micro-event detection provides automated annotation to reduce reliance on labor-intensive expert labeling, current supervised approaches require precisely annotated datasets that remain scarce in clinical practice. To overcome this data bottleneck, this paper introduces a Weakly Supervised Sleep Micro-Event Detector (WSSMED) that leverages readily available coarse sleep stage annotations. The proposed WSSMED features a dual-branch architecture, consisting of a wave prototype module and a cluster module, designed to capture the fine-grained sleep micro-event patterns experts rely on for sleep staging. This framework infers expert logic from coarse annotations while mitigating performance degradation caused by annotation inconsistencies arising from inter-rater variability. Experiments conducted on two public datasets and one clinical dataset demonstrate that WSSMED achieves state-of-the-art performance in detecting sleep spindles and K-complexes, as evaluated at both sample-level and event-level in terms of precision, recall and F1-score metrics. Furthermore, subject-level evaluation demonstrates that the density and duration of micro-events detected by WSSMED-key metrics linked to cognitive function and neurological status-align more closely with expert annotations than those of other reported methods. These results highlight the clinical potential of WSSMED for reliable sleep micro-event analysis.