The Boston Children's Hospital Sleep Corpus: A Collection of 15,695 Annotated Pediatric Polysomnograms

Ayush Tripathi1,2, Wolfgang Ganglberger1,2, Haoqi Sun1,2

  • 1Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States.

Sleep
|September 19, 2025
PubMed

Insights

This study introduces the largest pediatric polysomnography (PSG) dataset, offering valuable insights into developing sleep patterns and disorders in children. The comprehensive data supports AI-driven analysis for improved pediatric sleep research.

Area of Science:

  • Neuroscience
  • Pediatric Sleep Medicine
  • Biomedical Data Science

Background:

  • Sleep is crucial for child development, yet pediatric sleep research is limited by scarce, high-quality polysomnography (PSG) data.
  • Understanding pediatric sleep patterns and disorders is essential for early diagnosis and intervention.

Purpose of the Study:

  • To introduce the Boston Children's Hospital (BCH) Sleep Corpus, the largest publicly available pediatric PSG dataset.
  • To provide a comprehensive resource for advancing scientific understanding of pediatric sleep.
  • To facilitate the development of AI tools for automated PSG analysis in children.

Main Methods:

  • Compiled 15,695 overnight PSG recordings from 12,640 unique pediatric patients.
  • Annotated 16.7 million sleep stages and 2.25 million respiratory, arousal, and limb movement events.
  • Linked PSG data with over 11,000 de-identified patient diagnoses from electronic health records.

Main Results:

  • The BCH Sleep Corpus is the largest pediatric PSG dataset, with 139,208 hours of EEG data.
  • Identified age-related trends in sleep stages (e.g., decreasing REM, increasing N2 sleep).
  • Documented changes in respiratory events (e.g., declining central apneas, rising obstructive hypopneas) and limb movements with age.

Conclusions:

  • The BCH Sleep Corpus is a significant resource for pediatric sleep research and AI development.
  • The dataset enables the establishment of normative EEG spectral data and respiratory event trends across pediatric ages.
  • Public availability of this data will accelerate discoveries in pediatric sleep and sleep disorder diagnosis.

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