2026-04-14T15:31:35Zhttps://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 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. eng 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