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 general

SUBMITTER: Kevat A 

PROVIDER: S-EPMC11789265 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature