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Analysis and classification of oximetry recordings to predict obstructive sleep apnea severity in children., , , , , , , , and . EMBC, page 4540-4543. IEEE, (2015)Exploring the spectral information of airflow recordings to help in pediatric Obstructive Sleep Apnea-Hypopnea Syndrome diagnosis., , , , , and . EMBC, page 2298-2301. IEEE, (2014)Usefulness of discrete wavelet transform in the analysis of oximetry signals to assist in childhood sleep apnea-hypopnea syndrome diagnosis., , , , , , , , and . EMBC, page 3753-3756. IEEE, (2017)Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers., , , and . CoRR, (2020)Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis., , , , , , , and . EMBC, page 4580-4583. IEEE, (2019)Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry., , , , , , , and . EMBC, page 3555-3558. IEEE, (2019)Cross Approximate Entropy Analysis of Nocturnal Oximetry Signals in the Diagnosis of the Obstructive Sleep Apnea Syndrome., , , , , and . EMBC, page 6149-6152. IEEE, (2006)An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals., , , , , , , , and . Biomed. Signal Process. Control., 87 (Part B): 105490 (January 2024)A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea., , , , , , , and . EMBC, page 1-4. IEEE, (2023)Utility of Approximate Entropy From Overnight Pulse Oximetry Data in the Diagnosis of the Obstructive Sleep Apnea Syndrome., , , , and . IEEE Trans. Biomed. Eng., 54 (1): 107-113 (2007)