2026-04-14T19:46:42Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/313402025-02-20T11:47:36Zcom_10324_23459com_10324_954com_10324_894col_10324_23460

Gutierrez Tobal, Gonzalo César Álvarez González, Daniel Crespo Senado, Andrea Campo Matias, Félix del Hornero Sánchez, Roberto 2018-08-31T10:38:19Z 2018-08-31T10:38:19Z 2019 IEEE Journal of Biomedical and Health Informatics, In Press http://uvadoc.uva.es/handle/10324/31340 Producción Científica Complexity, costs, and waiting lists issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients’ home. They were used to automatically obtain statistical, spectral, non-linear, and clinical SAHS-related information. Relevant and non-redundant data from these analyses were subsequently used to train and validate four machine-learning methods with ability to classify SpO2 signals into one out of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multi-layer perceptron, and AdaBoost), outperformed the diagnostic ability of the conventionally-used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen’s  in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine learning can be used along with SpO2 information acquired at patients’ home to help in SAHS diagnosis simplification. This research has been supported by the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León, the project 265/2012 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), the projects RTC-2015-3446-1 and TEC2014-53196-R from the Ministerio de Economía y Competitividad, and the European Regional Development Fund (FEDER). D. Álvarez was in receipt of a Juan de la Cierva grant from the Ministerio de Economía y Competitividad application/pdf eng IEEE info:eu-repo/semantics/restrictedAccess IEEE Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity from at-Home Oximetry Recordings info:eu-repo/semantics/article https://ieeexplore.ieee.org/document/8331839/ SI