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

Abstract

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.