RT info:eu-repo/semantics/article
T1 Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity from at-Home Oximetry Recordings
A1 Gutiérrez Tobal, Gonzalo César
A1 Álvarez González, Daniel
A1 Crespo Senado, Andrea
A1 Campo Matias, Félix del
A1 Hornero Sánchez, Roberto
AB 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.
PB IEEE
YR 2019
FD 2019
LK http://uvadoc.uva.es/handle/10324/31340
UL http://uvadoc.uva.es/handle/10324/31340
LA eng
NO IEEE Journal of Biomedical and Health Informatics, In Press
NO Producción Científica
DS UVaDOC
RD 23-nov-2024