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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.
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.
Abstract:
Sleep is a fundamental biological process essential to health, particularly during early life when sleep patterns are developing and sleep disorders are common. Yet pediatric sleep research is hindered by a lack of large-scale, high-quality polysomnography (PSG) datasets. To address this need, we introduce the Boston Children's Hospital (BCH) Sleep Corpus-the largest pediatric PSG dataset available-comprising 15 695 overnight recordings from 12 640 unique patients (median age ~ 6 years). The dataset includes 16.7 million annotated sleep stages, 2.25 million respiratory, arousal, and limb movement events, and over 11 000 patient diagnoses linked through de-identified electronic health records. Each PSG has a median duration of 8.9 hours, totaling 139 208 hours of EEG data. Sleep staging follows American Academy of Sleep Medicine guidelines and reveals age-related trends: REM sleep decreases from 33.5% in neonates to 16.3% in teenagers, while N2 sleep increases from 21.7% to 35.4%. Central apneas decline with age, while obstructive hypopneas and respiratory effort related arousals events rise. Limb movements are not scored in <1 yr but remain at around 30 per PSG across older age groups. We also present age- and region-specific EEG spectral norms and respiratory event trends across the pediatric age range. The dataset is organized in Brain Imaging Data Structure (BIDS) format and publicly available via the Brain Data Science Platform. The dataset provides a valuable resource for improving our scientific understanding of pediatric sleep and developing automated PSG analysis with artificial intelligence tools.
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