Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings

Scott Ye, Harlin Lee

Keywords: pediatric sleep, polysomnography, PHATE, physiological time-series, topological data analysis, trajectory analysis, EHR, multimodal representation learning

TL;DR: By analyzing the trajectory and topology of multimodal sleep embeddings, we reveal interpretable markers of pediatric sleep disorders.

Abstract: Generative models have shown promise in pediatric sleep analysis, but the latent structure of their multimodal embeddings remains poorly understood. We study session-wide diagnostic information contained in sequences of 30-second pediatric polysomnography (PSG) epochs embedded by a multimodal masked autoencoder (PedSleepMAE). We test whether augmenting embeddings with (i) PHATE-derived per-epoch coordinates and whole-night movement descriptors, (ii) persistent-homology summaries of the embedding cloud, and (iii) structured EHR yields task-relevant signals. Simple linear and MLP late-fusion models, chosen for interpretability rather than state-of-the-art performance, show that geometric, topological, and clinical features each provide complementary gains. Across four highly imbalanced binary tasks, AUPRC improves from 0.26$\rightarrow$0.34 (desaturation), 0.31$\rightarrow$0.48 (EEG arousal), 0.09$\rightarrow$0.22 (hypopnea), and 0.05$\rightarrow$0.14 (apnea), and the full fusion model achieves the best calibration (Brier score, ECE). Our results indicate that latent geometry/topology and EHR provide complementary, interpretable signals beyond embeddings and can improve calibration under imbalance.

Submission Number: 96