RT info:eu-repo/semantics/bookPart
T1 Multi-Class AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome Severity from Oximetry Recordings Obtained at Home
A1 Gutiérrez Tobal, Gonzalo César
A1 Álvarez González, Daniel
A1 Crespo Senado, Andrea
A1 Arroyo Domingo, Carmen Ainhoa
A1 Vaquerizo Villar, Fernando
A1 Barroso García, Verónica
A1 Campo Matias, Félix del
A1 Hornero Sánchez, Roberto
K1 Oximetry
AB This paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<5 events/hour), mild-SAHS (5≤AHI<15 e/h), moderate-SAHS (15≤AHI<30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohen’s kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.
PB Institute of Electrical and Electronics Engineers (IEEE)
SN 978-1-5090-2484-1
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21750
UL http://uvadoc.uva.es/handle/10324/21750
LA eng
NO Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2016 Global, Institute of Electrical and Electronics Engineers (IEEE) , 2016, p. 95-99
NO Producción Científica
DS UVaDOC
RD 18-nov-2024