A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea.

ABSTRACT: 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 (AHI_CNN) was obtained by aggregati

SUBMITTER: Vaquerizo-Villar F 

PROVIDER: S-EPMC8460136 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature