RT info:eu-repo/semantics/article
T1 Bispectral analysis of heart rate variability to characterize and help diagnose pediatric sleep apnea
A1 Martín Montero, Adrián
A1 Gutiérrez Tobal, Gonzalo César
A1 Gozal, David
A1 Barroso García, Verónica
A1 Álvarez González, Daniel
A1 Campo Matias, Félix del
A1 Kheirandish Gozal, Leila
A1 Hornero Sánchez, Roberto
K1 Pediatrics
K1 Sleep apnea syndromes
K1 Ritmo cardíaco - Trastornos
K1 Neural networks (Computer science)
K1 Heart rate variability
K1 Bispectral analysis
K1 Perceptron neural network
K1 3201.10 Pediatría
K1 3205.01 Cardiología
AB Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.
PB MDPI
SN 1099-4300
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59564
UL https://uvadoc.uva.es/handle/10324/59564
LA eng
NO Entropy, 2021, Vol. 23, Nº. 8, 1016
NO Producción Científica
DS UVaDOC
RD 23-nov-2024