# A Large Collection of Real-world Pediatric Sleep Studies

arXiv: https://arxiv.org/abs/2102.13284 · HTML: https://mlsanity.com/paper/2102.13284/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 20 of 20)

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- [Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG](https://mlsanity.com/paper/2207.06921/) (2022)
- [PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals](https://mlsanity.com/paper/2411.00718/) (2024)
- [Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals](https://mlsanity.com/paper/2405.09566/) (2024)
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- [Multimodal Sleep Apnea Detection with Missing or Noisy Modalities](https://mlsanity.com/paper/2402.17788/) (2024)
- [NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals](https://mlsanity.com/paper/2511.03488/) (2025)
- [BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep](https://mlsanity.com/paper/2602.02769/) (2026)
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- [Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout](https://mlsanity.com/paper/2409.01962/) (2024)
- [Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves](https://mlsanity.com/paper/2411.07964/) (2024)
- [PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning](https://mlsanity.com/paper/2504.13229/) (2025)
- [Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets](https://mlsanity.com/paper/2508.08159/) (2025)
- [Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data](https://mlsanity.com/paper/2603.28122/) (2026)
- [Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings](https://mlsanity.com/paper/2605.14156/) (2026)
- [Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals](https://mlsanity.com/paper/2607.15477/) (2026)
- [BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment](https://mlsanity.com/paper/2609.31546/) (2026)

## Abstract (arXiv)

Despite being crucial to health and quality of life, sleep -- especially pediatric sleep -- is not yet well understood. This is exacerbated by lack of access to sufficient pediatric sleep data with clinical annotation. In order to accelerate research on pediatric sleep and its connection to health, we create the Nationwide Children's Hospital (NCH) Sleep DataBank and publish it at Physionet and the National Sleep Research Resource (NSRR), which is a large sleep data common with physiological data, clinical data, and tools for analyses. The NCH Sleep DataBank consists of 3,984 polysomnography studies and over 5.6 million clinical observations on 3,673 unique patients between 2017 and 2019 at NCH. The novelties of this dataset include: 1) large-scale sleep dataset suitable for discovering new insights via data mining, 2) explicit focus on pediatric patients, 3) gathered in a real-world clinical setting, and 4) the accompanying rich set of clinical data. The NCH Sleep DataBank is a valuable resource for advancing automatic sleep scoring and real-time sleep disorder prediction, among many other potential scientific discoveries.