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Open AccessArticle

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

1
Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain
2
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
3
Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA
4
Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, 47012 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(6), 670; https://doi.org/10.3390/e22060670
Received: 30 April 2020 / Revised: 9 June 2020 / Accepted: 15 June 2020 / Published: 17 June 2020
The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO2) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO2 signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens’s kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea–hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO2 was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO2 enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children. View Full-Text
Keywords: sleep apnea–hypopnea syndrome; airflow; oximetry; AdaBoost; spectral analysis; nonlinear analysis sleep apnea–hypopnea syndrome; airflow; oximetry; AdaBoost; spectral analysis; nonlinear analysis
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Jiménez-García, J.; Gutiérrez-Tobal, G.C.; García, M.; Kheirandish-Gozal, L.; Martín-Montero, A.; Álvarez, D.; del Campo, F.; Gozal, D.; Hornero, R. Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost. Entropy 2020, 22, 670.

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