RT info:eu-repo/semantics/article
T1 A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry
A1 Jiménez García, Jorge
A1 García, María
A1 Gutiérrez Tobal, Gonzalo César
A1 Kheirandish Gozal, Leila
A1 Vaquerizo Villar, Fernando
A1 Álvarez, Daniel
A1 Campo Matias, Félix del
A1 Gozal, David
A1 Hornero Sánchez, Roberto
K1 Obstructive sleep apnea
K1 Apnea obstructiva del sueño
K1 Airflow
K1 Flujo aéreo
K1 Oximetry
K1 Oximetría
AB The gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%–90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.
PB Elsevier
SN 0010-4825
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/55620
UL https://uvadoc.uva.es/handle/10324/55620
LA eng
NO Computers in Biology and Medicine, 2022, vol. 147, 105784
NO Producción Científica
DS UVaDOC
RD 26-abr-2024