Density Spectral Array EEG for Sleep Staging in Pediatric Patients.

Autor:	Rudock RJ; Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A., Turner AD; Division of Pediatric Critical Care, Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri, U.S.A.; and., Binkley M; Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A., Landre R; St. Louis Children's Hospital, St. Louis, Missouri, U.S.A., Morrissey MJ; Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A., Tomko SR; Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A., Guerriero RM; Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A.
Jazyk:	angličtina
Zdroj:	Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society [J Clin Neurophysiol] 2024 Oct 01. Date of Electronic Publication: 2024 Oct 01.
DOI:	10.1097/WNP.0000000000001117
Abstrakt:	Purpose: Sleep is an essential physiologic process, which is frequently disrupted in children with illness and/or injury. Accurate identification and quantification of sleep may provide insights to improve long-term clinical outcomes. Traditionally, however, the identification of sleep stages has relied on the resource-intensive and time-consuming gold standard polysomnogram. We sought to use limited EEG data, converted into density spectrum array EEG, to accurately identify sleep stages in a clinical pediatric population. Methods: We reviewed 87 clinically indicated pediatric polysomnographic studies with concurrent full montage EEG, between March 2017 and June 2020, of which 11 had normal polysomnogram and EEG interpretations. We then converted the EEG data of those normal studies into density spectral array EEG trends and had five blinded raters classify sleep stage (wakefulness, nonrapid eye movement [NREM] 1, NREM 2, NREM 3, and rapid eye movement) in 5-minute epochs. We compared the classified sleep stages from density spectral array EEG to the gold standard polysomnogram. Results: Inter-rater reliability was highest (κ = 0.745, P < 0.0001) when classifying state into wakefulness, NREM sleep, and rapid eye movement sleep. Agreement between group classification and polysomnogram was highest (κ = 0.873, [0.819, 0.926], P < 0.0001) when state was classified into wakefulness and sleep and was lowest (κ = 0.674 [0.645, 0.703], P < 0.0001) when classified into wakefulness, NREM 1, NREM 2, NREM 3, and rapid eye movement. The most common error that raters made was overscoring of NREM 1. Conclusions: Density spectral array EEG can be used to identify sleep stages in clinical pediatric patients without relying on traditional polysomnography. Competing Interests: The authors have no funding or conflicts of interest to disclose. (Copyright © 2024 by the American Clinical Neurophysiology Society.)
Databáze:	MEDLINE
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