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<p>We developed and validated PANDA, a deep-learning model for pediatric arousal detection in polysomnography. PANDA uses a U-Net-style encoder-decoder architecture trained on 10-channel PSG signals with 17.5 min contextual windows and 2 Hz arousal outputs. The model was trained on 9150 PSGs from 7604 subjects, validated on 2455 PSGs from 2000 subjects, and evaluated on a held-out Boston Children’s Hospital test set of 3804 recordings from 3000 subjects. To reduce reference-label noise, we constructed a rigorously adjudicated 200-PSG platinum set and quantified inter-rater reliability before and after adjudication. Performance was assessed using sample-wise agreement, event-wise arousal-index agreement, and external validation on CHAT and PATS. On routine labels, PANDA achieved mean subject-wise Cohen’s <i>κ</i> = 0.45 compared with <i>κ</i> = 0.26 for CAISR. Agreement increased to <i>κ</i> = 0.87 on platinum labels, consistent with reduced scoring variability after expert adjudication. PANDA also showed closer event-wise agreement with reference arousal index and lower bias than CAISR across internal and external cohorts. External validation yielded <i>κ</i> = 0.46 on CHAT and <i>κ</i> = 0.38 on PATS, improving to <i>κ</i> = 0.57 and <i>κ</i> = 0.50 after cohort-specific calibration. PANDA enables scalable pediatric arousal detection with improved consistency and reduced manual scoring time.</p>

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PANDA pediatric arousal neural detection architecture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Arnav Gupta,
  • Ayush Tripathi,
  • Wolfgang Ganglberger,
  • Samuel Waters,
  • Haoqi Sun,
  • Samaneh Nasiri,
  • Katie L. Stone,
  • Emmanuel Mignot,
  • Dennis Hwang,
  • Matthew A. Reyna,
  • Lynn Marie Trotti,
  • Gari D. Clifford,
  • Kiran Maski,
  • Umakanth Katwa,
  • Robert J. Thomas,
  • M. Brandon Westover

摘要

We developed and validated PANDA, a deep-learning model for pediatric arousal detection in polysomnography. PANDA uses a U-Net-style encoder-decoder architecture trained on 10-channel PSG signals with 17.5 min contextual windows and 2 Hz arousal outputs. The model was trained on 9150 PSGs from 7604 subjects, validated on 2455 PSGs from 2000 subjects, and evaluated on a held-out Boston Children’s Hospital test set of 3804 recordings from 3000 subjects. To reduce reference-label noise, we constructed a rigorously adjudicated 200-PSG platinum set and quantified inter-rater reliability before and after adjudication. Performance was assessed using sample-wise agreement, event-wise arousal-index agreement, and external validation on CHAT and PATS. On routine labels, PANDA achieved mean subject-wise Cohen’s κ = 0.45 compared with κ = 0.26 for CAISR. Agreement increased to κ = 0.87 on platinum labels, consistent with reduced scoring variability after expert adjudication. PANDA also showed closer event-wise agreement with reference arousal index and lower bias than CAISR across internal and external cohorts. External validation yielded κ = 0.46 on CHAT and κ = 0.38 on PATS, improving to κ = 0.57 and κ = 0.50 after cohort-specific calibration. PANDA enables scalable pediatric arousal detection with improved consistency and reduced manual scoring time.