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Density Spectral Array EEG for Sleep Staging in Pediatric Patients
Robert J Rudock1, Ashley D Turner2, Michael Binkley1
1Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A.
Insights
Density spectral array EEG effectively identifies sleep stages in children, offering a simpler alternative to polysomnography for clinical use. This method aids in understanding sleep disruptions in pediatric patients.
Area of Science:
- Pediatric Sleep Medicine
- Neurophysiology
- Clinical Diagnostics
Background:
- Sleep is vital for child development and recovery from illness/injury.
- Traditional polysomnography for sleep stage identification is resource-intensive.
- Accurate sleep assessment is crucial for improving pediatric clinical outcomes.
Purpose of the Study:
- To evaluate the efficacy of density spectral array (DSA) EEG for identifying sleep stages in pediatric patients.
- To determine if limited EEG data can replace polysomnography for sleep staging.
- To provide a more accessible method for sleep analysis in children.
Main Methods:
- Reviewed 87 pediatric polysomnography studies with concurrent EEG.
- Converted EEG data from 11 normal studies into DSA EEG trends.
- Five blinded raters classified sleep stages (wake, NREM 1-3, REM) using DSA EEG and compared to polysomnography.
Main Results:
- High inter-rater reliability (κ=0.745) for classifying broad states (wake, NREM, REM).
- Excellent agreement (κ=0.873) between DSA EEG and polysomnography for wakefulness vs. sleep.
- Lower agreement (κ=0.674) when distinguishing all specific NREM stages, with frequent overscoring of NREM 1.
Conclusions:
- Density spectral array EEG is a viable tool for identifying sleep stages in pediatric clinical settings.
- DSA EEG offers a simplified approach, potentially reducing reliance on traditional polysomnography.
- This method can aid in the assessment and management of sleep disturbances in children.
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.
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