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Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Año del Documento
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.
This work was supported by 'Ministerio de Ciencia, Innovación y Universidades' and ‘European Regional Development Fund (FEDER)’ under projects DPI2017-84280-R and RTC-2017-6516-1, and by ‘European Commission’ and ‘FEDER’ under project ‘POCTEP 0378_AD_EEGWA_2_P’. F. Vaquerizo-Villar was in receipt of a ‘Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)’ grant from the Ministerio de Educación, Cultura y Deporte (FPU16/02938). V. Barroso-García was in a receipt of a ‘Ayuda para financiar la contratación predoctoral de personal investigador’ grant from the Consejería de Educación de la Junta de Castilla y León and the European Social Fund. L. Kheirandish-Gozal and D. Gozal were supported by National Institutes of Health (NIH) grant HL130984.
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