Key differences in pediatric versus adult sleep

Voleti, Deepa; Himelfarb, Madeline; Patel, N.B.; Shatkin, Jess P.

doi:10.1016/b978-0-12-822963-7.00163-8

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…To explore the relation between SWS deficiency and presleep resting EEG, we divided these records into three groups based on [44], which indicates that a young adult's normal SWS percentage is around 13-23% per sleep. Therefore, we defined records with less than 13% of SWS as Low SWS subjects, records with 13%-23% of SWS as Mid SWS subjects, and records with over 23% of SWS as High SWS subjects.…”

Section: A Subject and Data Acquisitionmentioning

confidence: 99%

Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

Su,

Ko,

Jung

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stressrelated neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.

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Section: A Subject and Data Acquisitionmentioning

confidence: 99%