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This paper presents a deterministic, rule-based sleep staging method that explicitly implements the American Academy of Sleep Medicine (AASM) scoring rules, providing epoch-level natural language explanations. It achieves 60.5% epoch-level agreement with a majority-vote consensus on 50 polysomnography recordings, offering transparency as a complement to opaque deep learning models.
This paper investigates the latent structure of multimodal embeddings from a masked autoencoder for pediatric sleep analysis. It shows that augmenting embeddings with geometric, topological, and clinical features improves prediction and calibration for sleep-related events.