Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls

IntroductionVisual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to di...

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Bibliographic Details
Main Authors: Pranavan Somaskandhan, Timo Leppänen, Philip I. Terrill, Sigridur Sigurdardottir, Erna Sif Arnardottir, Kristín A. Ólafsdóttir, Marta Serwatko, Sigurveig Þ. Sigurðardóttir, Michael Clausen, Juha Töyräs, Henri Korkalainen
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-04-01
Series:Frontiers in Neurology
Subjects:
Pediatric sleep staging
preadolescent cohort
inter-rater reliability
pediatric sleep-disordered breathing
community controls
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2023.1162998/full

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https://www.frontiersin.org/articles/10.3389/fneur.2023.1162998/full

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