Please use this identifier to cite or link to this item: https://dora.health.qld.gov.au/qldresearchjspui/handle/1/7306
Title: Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network
Authors: Kevat, Ajay
Steinkey, Rylan
Suresh, Sadasivam
Ruehland, Warren R.
Chawla, Jasneek 
Terrill, Philip I.
Collaro, Andrew 
Iyer, Kartik
Issue Date: 2024
Source: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2024
Journal Title: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Abstract: Study Objectives: U-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to a) 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 b) identify clinical and demographic characteristics that impact U-Sleep accuracy, using a clinical dataset of 3114 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 two one-sided test (TOST). Multivariable regression and generalized additive modelling 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 (TOST p < 0.01). Median (interquartile range) kappa agreement between U-Sleep 2.0 and clinical sleep-staging was kappa=0.69 (0.22). Modelling 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. (© 2024 American Academy of Sleep Medicine.)
DOI: 10.5664/jcsm.11362
Resources: https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,athens&db=mdc&AN=39324691&site=ehost-live
Appears in Sites:Children's Health Queensland Publications
Queensland Health Publications

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