RT info:eu-repo/semantics/article
T1 A convolutional neural network architecture to enhance oximetry ability to diagnose pediatric obstructive sleep apnea
A1 Vaquerizo Villar, Fernando
A1 Álvarez González, Daniel
A1 Kheirandish-Gozal, Leila
A1 Gutierrez Tobal, Gonzalo César
A1 Barroso García, Verónica
A1 SantaMaría Vazquez, Eduardo
A1 Campo Matias, Félix del
A1 Gozal, David
A1 Hornero Sánchez, Roberto
AB This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen’s kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.
PB IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC
SN 2168-2194
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/74125
UL https://uvadoc.uva.es/handle/10324/74125
LA eng
NO IEEE Journal of Biomedical and Health Informatics, Agosto 2021, vol. 25, n. 8. p. 2906-2916.
NO Producción Científica
DS UVaDOC
RD 22-feb-2025