Density Spectral Array EEG for Sleep Staging in Pediatric Patients.

Abstract

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.