TY - GEN
T1 - Slow EEG Oscillation to Characterize Pediatric Sleep Apnea and Associated Cognitive Impairments
AU - Gutierrez-Tobal, Gonzalo C.
AU - Gomez-Pilar, Javier
AU - Kheirandish-Gozal, Leila
AU - Martin-Montero, Adrian
AU - Poza, Jesus
AU - Alvarez, Daniel
AU - Del Campo, Felix
AU - Gozal, David
AU - Hornero, Roberto
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Previous studies have suggested that the typical slow oscillations (SO) characteristics during sleep could be modified in the presence of pediatric obstructive sleep apnea (OSA). Here, we evaluate whether these modifications are significant and if they may reflect cognitive deficits. We recorded the overnight electroencephalogram (EEG) of 294 pediatric subjects (5-9 years old) using eight channels. Then, we divided the cohort in three OSA severity groups (no OSA, mild, and moderate/severe) to characterize the corresponding SO using the spectral maximum in the slow wave sleep (SWS) band δ1: 0.1-2 Hz (Maxs o), as well as the frequency where this maximum is located (FreqMaxso). Spectral entropy (SpecEn) from δ1 was also included in the analyses. A correlation analysis was performed to evaluate associations of these spectral measures with six OSA-related clinical variables and six cognitive scores. Our results indicate that Maxso could be used as a moderate/severe OSA biomarker while providing useful information regarding verbal and visuo-spatial impairments, and that FreqMaxso emerges as an even more robust indicator of visuospatial function. In addition, we uncovered novel insights regarding the ability of SpecEn in δ1 to characterize OSA-related disruption of sleep homeostasis. Our findings suggest that the information from SO may be useful to automatically characterize moderate/severe pediatric OSA and its cognitive consequences.
AB - Previous studies have suggested that the typical slow oscillations (SO) characteristics during sleep could be modified in the presence of pediatric obstructive sleep apnea (OSA). Here, we evaluate whether these modifications are significant and if they may reflect cognitive deficits. We recorded the overnight electroencephalogram (EEG) of 294 pediatric subjects (5-9 years old) using eight channels. Then, we divided the cohort in three OSA severity groups (no OSA, mild, and moderate/severe) to characterize the corresponding SO using the spectral maximum in the slow wave sleep (SWS) band δ1: 0.1-2 Hz (Maxs o), as well as the frequency where this maximum is located (FreqMaxso). Spectral entropy (SpecEn) from δ1 was also included in the analyses. A correlation analysis was performed to evaluate associations of these spectral measures with six OSA-related clinical variables and six cognitive scores. Our results indicate that Maxso could be used as a moderate/severe OSA biomarker while providing useful information regarding verbal and visuo-spatial impairments, and that FreqMaxso emerges as an even more robust indicator of visuospatial function. In addition, we uncovered novel insights regarding the ability of SpecEn in δ1 to characterize OSA-related disruption of sleep homeostasis. Our findings suggest that the information from SO may be useful to automatically characterize moderate/severe pediatric OSA and its cognitive consequences.
UR - https://www.scopus.com/pages/publications/85138128404
U2 - 10.1109/EMBC48229.2022.9871469
DO - 10.1109/EMBC48229.2022.9871469
M3 - Conference contribution
C2 - 36085956
AN - SCOPUS:85138128404
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2957
EP - 2960
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -