# PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals

arXiv: https://arxiv.org/abs/2411.00718 · HTML: https://mlsanity.com/paper/2411.00718/

## 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 (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://mlsanity.com/paper/2405.17766/) (2024)
- [A Large Collection of Real-world Pediatric Sleep Studies](https://mlsanity.com/paper/2102.13284/) (2021)
- [Statistical power for cluster analysis](https://mlsanity.com/paper/2003.00381/) (2020)
- [Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG](https://mlsanity.com/paper/2207.06921/) (2022)
- [Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals](https://mlsanity.com/paper/2405.09566/) (2024)

## Built on by (most cited first, 4 of 4)

- [SleepLM: Natural-Language Intelligence for Human Sleep](https://mlsanity.com/paper/2602.23605/) (2026)
- [OSF: On Pre-training and Scaling of Sleep Foundation Models](https://mlsanity.com/paper/2603.00190/) (2026)
- [BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep](https://mlsanity.com/paper/2602.02769/) (2026)
- [Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings](https://mlsanity.com/paper/2605.14156/) (2026)

## 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.