Key result
Bispectral analysis of heart rate variability using OSA-specific regions accurately detected moderate (Acc 81.0%, AUC 0.791) and severe (Acc 89.3%, AUC 0.841) pediatric obstructive sleep apnea.
Why the study?
Conventional spectral analysis is commonly used to assess HRV in children, but bispectral analysis provides linearity and stationarity information and had not been applied to pediatric OSA.
Does bispectral analysis of heart rate variability accurately diagnose pediatric obstructive sleep apnea?
Observational (n=1,738)
Does bispectral analysis of heart rate variability accurately diagnose pediatric obstructive sleep apnea?
Effect estimate: Acc 81.0%, AUC 0.791 (moderate); Acc 89.3%, AUC 0.841 (severe)
Bispectral analysis of heart rate variability combined with neural networks provides high diagnostic accuracy for moderate and severe pediatric obstructive sleep apnea.
Bispectral HRV analysis in pediatric OSA is feasible; leaves open diagnostic utility pending validation.
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.
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Martín‐Montero et al. (2021) conducted an observational in Pediatric obstructive sleep apnea (n=1,738). Bispectral analysis of heart rate variability vs. Classic spectral analysis regions was evaluated on Detection of moderate and severe pediatric OSA (Acc 81.0%, AUC 0.791 (moderate); Acc 89.3%, AUC 0.841 (severe)). Bispectral analysis of heart rate variability using OSA-specific regions accurately detected moderate (Acc 81.0%, AUC 0.791) and severe (Acc 89.3%, AUC 0.841) pediatric obstructive sleep apnea.
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