AI RESEARCH
Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings
arXiv CS.LG
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ArXi:2605.14156v1 Announce Type: new While generative models have shown promise in pediatric sleep analysis, the latent structure of their multimodal embeddings remains poorly understood. This work investigates session-wide diagnostic information contained in the sequences of 30-second pediatric PSG epochs embedded by a multimodal masked autoencoder. We test whether augmenting embeddings with PHATE-derived per-epoch coordinates and whole-night movement descriptors, persistent homology summaries of the embedding cloud, and EHR yields task-relevant signals.