A Large Collection of Real-world Pediatric Sleep Studies
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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.
Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). First posted on arXiv 26 Feb 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 6; 2025: 5; 2026: 7.
20 papers.
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- 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 91
- 2026OSF: On Pre-training and Scaling of Sleep Foundation Modelscited by 10
- 2025SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging modelscited by 7
- 2022Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEGcited by 5
- 2024PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signalscited by 4
- 2024Detection of Sleep Oxygen Desaturations from Electroencephalogram Signalscited by 3
- 2025Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Modelscited by 3
- 2024Multimodal Sleep Apnea Detection with Missing or Noisy Modalitiescited by 2
- 2026BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleepcited by 1
- 2022Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Predictioncited by 0
- 2024Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layoutcited by 0
- 2024Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curvescited by 0
8 more
- 2025PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learningcited by 0
- 2025Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasetscited by 0
- 2025NAPS: Attention-Based Fusion of Heterogeneous Physiological Signalscited by 0
- 2026Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Datacited by 0
- 2026Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddingscited by 0
- 2026Next-Token Prediction Learns Generalisable Representations of Sleep Physiologycited by 0
- 2026Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signalscited by 0
- 2026BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environmentcited by 0
- 2026BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment25 Sep 2026
- 2026Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals16 Jul 2026
- 2026Next-Token Prediction Learns Generalisable Representations of Sleep Physiology8 Jun 2026
- 2026Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings13 May 2026
- 2026Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data30 Mar 2026
- 2026OSF: On Pre-training and Scaling of Sleep Foundation Models27 Feb 2026
- 2026BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep2 Feb 2026
- 2025Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models10 Dec 2025
- 2025NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals5 Nov 2025
- 2025Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets11 Aug 2025
- 2025SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models10 Jun 2025
- 2025PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning17 Apr 2025
8 more
- 2024Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves12 Nov 2024
- 2024PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals1 Nov 2024
- 2024Deep Multimodal Learning with Missing Modality: A Survey12 Sep 2024
- 2024Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout21 Aug 2024
- 2024Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals8 May 2024
- 2024Multimodal Sleep Apnea Detection with Missing or Noisy Modalities24 Feb 2024
- 2022Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction20 Nov 2022
- 2022Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG30 Jun 2022
- 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 91
- 2026OSF: On Pre-training and Scaling of Sleep Foundation Modelscited by 10
- 2025SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging modelscited by 7
- 2022Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEGcited by 5
- 2024PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signalscited by 4
- 2024Detection of Sleep Oxygen Desaturations from Electroencephalogram Signalscited by 3
- 2025Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Modelscited by 3
- 2024Multimodal Sleep Apnea Detection with Missing or Noisy Modalitiescited by 2
- 2026BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleepcited by 1
- 2022Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Predictioncited by 0
- 2024Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layoutcited by 0
- 2024Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curvescited by 0
12 most cited of 20 All 20 (JSON)
- 2026BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment25 Sep 2026
- 2026Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals16 Jul 2026
- 2026Next-Token Prediction Learns Generalisable Representations of Sleep Physiology8 Jun 2026
- 2026Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings13 May 2026
- 2026Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data30 Mar 2026
- 2026OSF: On Pre-training and Scaling of Sleep Foundation Models27 Feb 2026
- 2026BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep2 Feb 2026
- 2025Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models10 Dec 2025
- 2025NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals5 Nov 2025
- 2025Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets11 Aug 2025
- 2025SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models10 Jun 2025
- 2025PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning17 Apr 2025
12 newest of 20 All 20 (JSON)