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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)Statistical and nonlinear analysis of oximetry from respiratory polygraphy to assist in the diagnosis of Sleep Apnea in children., , , , , and . EMBC, page 1860-1863. IEEE, (2014)Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults., , , , , , , and . Appl. Soft Comput., (2021)Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses., , , , , , , and . Entropy, 21 (4): 381 (2019)Bispectral Analysis to Enhance Oximetry as a Simplified Alternative for Pediatric Sleep Apnea Diagnosis., , , , , , , , and . EMBC, page 175-178. IEEE, (2018)Automatic Assessment of Pediatric Sleep Apnea Severity Using Overnight Oximetry and Convolutional Neural Networks., , , , , , , , and . EMBC, page 633-636. IEEE, (2020)Automated analysis of nocturnal oximetry as screening tool for childhood obstructive sleep apnea-hypopnea syndrome., , , , , , , , and . EMBC, page 2800-2803. IEEE, (2015)Apnea-hypopnea index estimation from spectral analysis of airflow recordings., , , , , and . EMBC, page 3444-3447. IEEE, (2012)Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment., , , , , , and . Entropy, 20 (1): 35 (2018)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)