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Study Objectives: <p>U-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to (1) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance dataset of 50 pediatric polysomnogram excerpts scored by multiple trained scorers, and (2) identify clinical and demographic characteristics that impact U-Sleep accuracy, using a clinical dataset of 3,114 polysomnograms from a tertiary center.</p> Methods: <p>Agreement between U-Sleep and “gold” 30-second epoch sleep staging was determined across both datasets. Utilizing the concordance dataset, the hypothesis of equivalence between human scorers and U-Sleep was tested using a Wilcoxon 2 1-sided test. Multivariable regression and generalized additive modeling were used on the clinical dataset to estimate the effects of age, comorbidities, and polysomnographic findings on U-Sleep performance.</p> Results: <p>The median (interquartile range) Cohen’s kappa agreement of U-Sleep and individual trained humans relative to “gold” scoring for 5-stage sleep staging in the concordance dataset were similar, kappa = 0.79 (0.19) vs 0.78 (0.13), respectively, and satisfied statistical equivalence (2 1-sided test <i>P</i> &lt; .01). Median (interquartile range) kappa agreement between U-Sleep 2.0 and clinical sleep-staging was kappa = 0.69 (0.22). Modeling indicated lower performance for children &lt; 2 years, those with medical comorbidities possibly altering sleep electroencephalography (kappa reduction = 0.07–0.15) and those with decreased sleep efficiency or sleep-disordered breathing (kappa reduction = 0.1).</p> Conclusions: <p>While U-Sleep algorithms showed statistically equivalent performance to trained scorers, accuracy was lower in children &lt; 2 years and those with sleep-disordered breathing or comorbidities affecting electroencephalography. U-Sleep is suitable for pediatric clinical utilization provided automated staging is followed by expert clinician review.</p> Citation: <p>Kevat A, Steinkey R, Suresh S, et&#xa0;al. Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network. <i>J Clin Sleep Med</i>. 2025;21(2):277–285.</p>

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Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Ajay Kevat,
  • Rylan Steinkey,
  • Sadasivam Suresh,
  • Warren R. Ruehland,
  • Jasneek Chawla,
  • Philip I. Terrill,
  • Andrew Collaro,
  • Kartik Iyer

摘要

Study Objectives:

U-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to (1) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance dataset of 50 pediatric polysomnogram excerpts scored by multiple trained scorers, and (2) identify clinical and demographic characteristics that impact U-Sleep accuracy, using a clinical dataset of 3,114 polysomnograms from a tertiary center.

Methods:

Agreement between U-Sleep and “gold” 30-second epoch sleep staging was determined across both datasets. Utilizing the concordance dataset, the hypothesis of equivalence between human scorers and U-Sleep was tested using a Wilcoxon 2 1-sided test. Multivariable regression and generalized additive modeling were used on the clinical dataset to estimate the effects of age, comorbidities, and polysomnographic findings on U-Sleep performance.

Results:

The median (interquartile range) Cohen’s kappa agreement of U-Sleep and individual trained humans relative to “gold” scoring for 5-stage sleep staging in the concordance dataset were similar, kappa = 0.79 (0.19) vs 0.78 (0.13), respectively, and satisfied statistical equivalence (2 1-sided test P < .01). Median (interquartile range) kappa agreement between U-Sleep 2.0 and clinical sleep-staging was kappa = 0.69 (0.22). Modeling indicated lower performance for children < 2 years, those with medical comorbidities possibly altering sleep electroencephalography (kappa reduction = 0.07–0.15) and those with decreased sleep efficiency or sleep-disordered breathing (kappa reduction = 0.1).

Conclusions:

While U-Sleep algorithms showed statistically equivalent performance to trained scorers, accuracy was lower in children < 2 years and those with sleep-disordered breathing or comorbidities affecting electroencephalography. U-Sleep is suitable for pediatric clinical utilization provided automated staging is followed by expert clinician review.

Citation:

Kevat A, Steinkey R, Suresh S, et al. Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network. J Clin Sleep Med. 2025;21(2):277–285.