@article{10902/39077,
year = {2020},
month = {6},
url = {https://hdl.handle.net/10902/39077},
abstract = {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.},
organization = {This work was supported by the ‘Ministerio de Ciencia, Innovación y Universidades’ and ‘European
Regional Development Fund (FEDER)’ under projects DPI2017-84280-R and RTC-2017-6516-1, by ‘European
Commission’ and ‘FEDER’ under projects ‘Análisis y correlación entre el genoma completo y la actividad cerebral
para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ and ‘Análisis y correlación entre la epigenética y
la actividad cerebral para evaluar el riesgo de migraña crónica y episódica en mujeres’ (‘Cooperation Programme
Interreg V-A Spain-Portugal POCTEP 2014–20200), and by ‘CIBER en Bioingeniería, Biomateriales y Nanomedicina
(CIBER-BBN)’ through ‘Instituto de Salud Carlos III’ co-funded with FEDER funds. J.J.-G. was in receipt of a
‘Ayudas para la contratación de personal técnico de apoyo a la investigación’ grant from the ’Junta de Castilla y
León’ funded by the European Social Fund and Youth Employment Initiative. A.M.-M. was in receipt of a “Ayudas
para contratos predoctorales para la Formación de Doctores” grant from the Ministerio de Ciencia, Innovación
y Universidades (PRE2018-085219). D.G. and L.K.-G. are supported by US National Institutes of Health grants
HL130984 (L.K.-G.) and HL140548 (D.G.).},
publisher = {MDPI},
publisher = {Entropy, 2020, 22(6), 670},
title = {Assessment of airflow and oximetry signals to detect pediatric sleep apnea-hypopnea syndrome using AdaBoost},
author = {Jiménez García, Jorge and Gutiérrez Tobal, Gonzalo César and García Gadañón, María and Kheirandish Gozal, Leila and Martín Montero, Adrián and Álvarez González, Daniel and Campo Matías, Félix del and Gozal, David and Hornero Sánchez, Roberto},
}