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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)Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults., , , , , , , and . Appl. Soft Comput., (2021)Automatic Assessment of Pediatric Sleep Apnea Severity Using Overnight Oximetry and Convolutional Neural Networks., , , , , , , , and . EMBC, page 633-636. IEEE, (2020)Bispectral Analysis to Enhance Oximetry as a Simplified Alternative for Pediatric Sleep Apnea Diagnosis., , , , , , , , and . EMBC, page 175-178. IEEE, (2018)Automated analysis of nocturnal oximetry as screening tool for childhood obstructive sleep apnea-hypopnea syndrome., , , , , , , , and . EMBC, page 2800-2803. IEEE, (2015)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)Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost., , , , , , , , and . EMBC, page 167-170. IEEE, (2018)Analysis and classification of oximetry recordings to predict obstructive sleep apnea severity in children., , , , , , , , and . EMBC, page 4540-4543. IEEE, (2015)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)Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis., , , , , , , and . EMBC, page 4580-4583. IEEE, (2019)