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3. Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome. Vaquerizo-Villar F, Álvarez D, Kheirandish-Gozal L, Gutiérrez-Tobal GC, Barroso-García V, Crespo A, Del Campo F, Gozal D, Hornero R. PLoS One; 2018; 13(12):e0208502. PubMed ID: 30532267 [Abstract] [Full Text] [Related]
4. Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost. Vaquerizo-Villar F, Alvarez D, Kheirandish-Gozal L, Gutierrez-Tobal GC, Barroso-Garcia V, Crespo A, Del Campo F, Gozal D, Hornero R. Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():167-170. PubMed ID: 30441742 [Abstract] [Full Text] [Related]
12. A Bayesian neural network approach to compare the spectral information from nasal pressure and thermistor airflow in the automatic sleep apnea severity estimation. Gutierrez-Tobal GC, de Frutos J, Alvarez D, Vaquerizo-Villar F, Barroso-Garcia V, Crespo A, Del Campo F, Hornero R. Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():3741-3744. PubMed ID: 29060711 [Abstract] [Full Text] [Related]
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20. [Sequential use of nocturnal pulse oximetry and respiratory polygraphy (AutoSet) for diagnosing sleep apnea/hypopnea syndrome in high risk patients]. Martínez García MA, Soler Cataluña JJ, Román Sánchez P. Arch Bronconeumol; 2003 Feb 14; 39(2):74-80. PubMed ID: 12586047 [Abstract] [Full Text] [Related] Page: [Next] [New Search]