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This paper presents a comprehensive comparison of deep learning architectures, including Vision Transformers and Graph Attention Networks, for automated sleep apnea detection from multichannel EEG signals, achieving a best test AUC of 0.750 using a vision transformer trained on topological data analysis features.
Omni-Sleep is a sleep foundation model that uses hierarchical contrastive learning to capture CNS-ANS dynamics from multimodal polysomnography signals, outperforming strong baselines on sleep staging and multi-disease classification.
This paper proposes an interpretable causal-discovery-guided framework for deriving a Sleep Recovery Score (SRS) from multimodal polysomnography data, demonstrating up to 2.5× stronger alignment with perceived recovery than the traditional Apnea–Hypopnea Index (AHI), with potential applications in connected health.
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.