RT info:eu-repo/semantics/conferenceObject
T1 Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry
A1 Vaquerizo Villar, Fernando
A1 Álvarez, Daniel
A1 Kheirandish Gozal, Leila
A1 Gutiérrez Tobal, Gonzalo César
A1 Barroso García, Verónica
A1 Campo Matias, Félix del
A1 Gozal, David
A1 Hornero Sánchez, Roberto
AB Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children’s health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.
SN 978-1-5386-1311-5
YR 2019
FD 2019
LK http://uvadoc.uva.es/handle/10324/38010
UL http://uvadoc.uva.es/handle/10324/38010
LA eng
NO Producción Científica
DS UVaDOC
RD 07-ago-2024