AI RESEARCH
Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
arXiv CS.LG
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ArXi:2605.02245v1 Announce Type: new Automated sleep stage classification typically employs a single population-agnostic model, disregarding established graphic variations in sleep architecture. Sleep patterns, however, differ substantially across gender, age, and obstructive sleep apnea (OSA) severity, indicating that a onesize-fits all approach may be suboptimal for diverse clinical populations. In this paper, we propose a two stage