ArticleEntropy (Basel, Switzerland)2021
Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT06589349 (Effects of Kangaroo Care on Circadian Rhythm, Growth and Physiological Effects of Newborns Effect of Parameters on Breast Milk Cortisol Levels), which is not on this map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effects of Kangaroo Care on Circadian Rhythm, Growth and Physiological Effects of Newborns Effect of Parameters on Breast Milk Cortisol Levels: Randomized Controlled Study
Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Physiological and Sleep Profiles in Insomnia, OSA and COMISA: A Comparative Study in Portugal and Spain.Journal of sleep research · 2026Article
- Combined explainable deep learning model to predict pediatric sleep apnea from ECG and SpOMeasurement : journal of the International Measurement Confederation · 2026Article
- Leveraging Synchrosqueezing Transform (SST)-based representations in a dual-stream attention framework to enhance sleep apnea detection and subtyping.Frontiers in neuroscience · 2026Article
- Sleep in Juvenile Idiopathic Arthritis: An Exploratory Investigation of Heart Rate Variability.Brain sciences · 2025Article
- SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.IEEE journal of biomedical and health informatics · 2025Article
- Study of the Synchronization and Transmission of Intracellular Signaling Oscillations in Cells Using Bispectral Analysis.Biology · 2024Article
- An association between heart rate variability and pediatric obstructive sleep apnea.Italian journal of pediatrics · 2024Article
- Review
- ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis.Computers in biology and medicine · 2023Article
- Novel Indices to Improve the Diagnostic Ability of Nocturnal Oximetry in Children with OSAS.Children (Basel, Switzerland) · 2023Article
- Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming.Sensors (Basel, Switzerland) · 2022Review
- Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram.Frontiers in cardiovascular medicine · 2022Article
- Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures.Advances in experimental medicine and biology · 2022Article
- Erratum: Martín-Montero et al. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.