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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)

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Apnea-hypopnea index estimation from spectral analysis of airflow recordings., , , , , and . EMBC, page 3444-3447. IEEE, (2012)Automated analysis of nocturnal oximetry as screening tool for childhood obstructive sleep apnea-hypopnea syndrome., , , , , , , , and . EMBC, page 2800-2803. IEEE, (2015)Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment., , , , , , and . Entropy, 20 (1): 35 (2018)Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis., , , , , , , and . Comput. Biol. Medicine, (2021)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)Characterization of Cardiopulmonary Coupling in Pediatric Patients with Obstructive Sleep Apnea., , , , , , , , , and 1 other author(s). CinC, 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)Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home., , , , , , , , and . Entropy, 19 (6): 284 (2017)A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea., , , , , , , , and . IEEE J. Biomed. Health Informatics, 25 (8): 2906-2916 (2021)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)