mlsanityData to 14 Oct 2026

PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals

arXiv 2411.00718 ↗First posted 1 Nov 2024cs.LGcited by 4

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Abstract (arXiv)

Pediatric sleep is an important but often overlooked area in health informatics. We present PedSleepMAE, a generative model that fully leverages multimodal pediatric sleep signals including multichannel EEGs, respiratory signals, EOGs and EMG. This masked autoencoder-based model performs comparably to supervised learning models in sleep scoring and in the detection of apnea, hypopnea, EEG arousal and oxygen desaturation. Its embeddings are also shown to capture subtle differences in sleep signals coming from a rare genetic disorder. Furthermore, PedSleepMAE generates realistic signals that can be used for sleep segment retrieval, outlier detection, and missing channel imputation. This is the first general-purpose generative model trained on multiple types of pediatric sleep signals.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

12 of 29 references.

References · 12

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  12. 2024Detection of Sleep Oxygen Desaturations from Electroencephalogram Signalscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026SleepLM: Natural-Language Intelligence for Human Sleepcited by 12
  2. 2026OSF: On Pre-training and Scaling of Sleep Foundation Modelscited by 12
  3. 2026BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleepcited by 1
  4. 2026Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddingscited by 0