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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)

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Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry., , , , , and . Medical Biol. Eng. Comput., 46 (4): 323-332 (2008)Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow., , , and . IEEE Trans. Biomed. Eng., 63 (3): 636-646 (2016)Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings., , , , and . IEEE Trans. Biomed. Eng., 59 (1): 141-149 (2012)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)Exploring the spectral information of airflow recordings to help in pediatric Obstructive Sleep Apnea-Hypopnea Syndrome diagnosis., , , , , and . EMBC, page 2298-2301. IEEE, (2014)Diagnosis of pediatric obstructive sleep apnea: Preliminary findings using automatic analysis of airflow and oximetry recordings obtained at patients' home., , , , , and . Biomed. Signal Process. Control., (2015)An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea., , , , , , , and . Comput. Biol. Medicine, (October 2023)Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost., , , , , , , , and . Entropy, 22 (6): 670 (2020)Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea., , , , , , , and . Entropy, 23 (8): 1016 (2021)