Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost
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 objec...
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doaj-4bc3e8eb381b4dd88e53dfaba226d7052020-11-25T03:23:05ZengMDPI AGEntropy1099-43002020-06-012267067010.3390/e22060670Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoostJorge Jiménez-García0Gonzalo C. Gutiérrez-Tobal1María García2Leila Kheirandish-Gozal3Adrián Martín-Montero4Daniel Álvarez5Félix del Campo6David Gozal7Roberto Hornero8Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainBiomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainBiomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainDepartment of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USABiomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainBiomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainBiomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainDepartment of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USABiomedical Engineering Group, University of Valladolid, 47011 Valladolid, SpainThe 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 (SpO<sub>2</sub>) 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 SpO<sub>2</sub> 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 SpO<sub>2</sub> was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO<sub>2</sub> enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.https://www.mdpi.com/1099-4300/22/6/670sleep apnea–hypopnea syndromeairflowoximetryAdaBoostspectral analysisnonlinear analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jorge Jiménez-García Gonzalo C. Gutiérrez-Tobal María García Leila Kheirandish-Gozal Adrián Martín-Montero Daniel Álvarez Félix del Campo David Gozal Roberto Hornero |
spellingShingle |
Jorge Jiménez-García Gonzalo C. Gutiérrez-Tobal María García Leila Kheirandish-Gozal Adrián Martín-Montero Daniel Álvarez Félix del Campo David Gozal Roberto Hornero Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost Entropy sleep apnea–hypopnea syndrome airflow oximetry AdaBoost spectral analysis nonlinear analysis |
author_facet |
Jorge Jiménez-García Gonzalo C. Gutiérrez-Tobal María García Leila Kheirandish-Gozal Adrián Martín-Montero Daniel Álvarez Félix del Campo David Gozal Roberto Hornero |
author_sort |
Jorge Jiménez-García |
title |
Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost |
title_short |
Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost |
title_full |
Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost |
title_fullStr |
Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost |
title_full_unstemmed |
Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost |
title_sort |
assessment of airflow and oximetry signals to detect pediatric sleep apnea-hypopnea syndrome using adaboost |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2020-06-01 |
description |
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 (SpO<sub>2</sub>) 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 SpO<sub>2</sub> 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 SpO<sub>2</sub> was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO<sub>2</sub> enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children. |
topic |
sleep apnea–hypopnea syndrome airflow oximetry AdaBoost spectral analysis nonlinear analysis |
url |
https://www.mdpi.com/1099-4300/22/6/670 |
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