2026-04-27T23:22:00Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/380102025-02-20T11:45:35Zcom_10324_23459com_10324_954com_10324_894col_10324_23462
UVaDOC author Vaquerizo Villar, Fernando author Álvarez González, Daniel author Kheirandish Gozal, Leila author Gutierrez Tobal, Gonzalo César author Barroso García, Verónica author Campo Matias, Félix del author Gozal, David author Hornero Sánchez, Roberto 2019-09-19T07:18:28Z 2019-09-19T07:18:28Z 2019 978-1-5386-1311-5 http://uvadoc.uva.es/handle/10324/38010 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. eng Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry info:eu-repo/semantics/conferenceObject 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URL https://uvadoc.uva.es/bitstream/10324/38010/1/EMBC19_VaquerizoVillar_v2.pdf File MD5 b344df6a8f85cf32f22bf8320f037936 309262 application/pdf EMBC19_VaquerizoVillar_v2.pdf