2026-04-14T15:10:41Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/655972025-02-20T11:57:03Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Marcos Martín, José Víctor Hornero Sánchez, Roberto Álvarez González, Daniel Aboy, M. Campo Matias, Félix del 2024-02-02T16:18:24Z 2024-02-02T16:18:24Z 2012 IEEE Transactions on Biomedical Engineering, 2012, vol. 59, n. 1, p. 141-149. 0018-9294 https://uvadoc.uva.es/handle/10324/65597 10.1109/TBME.2011.2167971 141 1 149 IEEE Transactions on Biomedical Engineering 59 1558-2531 Nocturnal polysomnography (PSG) is the goldstandard for sleep apnea-hypopnea syndrome (SAHS) diagnosis. It provides the value of the apnea-hypopnea index (AHI), which is used to evaluate SAHS severity. However, PSG is costly, complex, and time-consuming. We present a novel approach for automatic estimation of the AHI from nocturnal oxygen saturation (SaO2 ) recordings and the results of an assessment study designed to characterize its performance. A set of 240 SaO2 signals was available for the assessment study. The data were divided into training (96 signals) and test (144 signals) sets for model optimization and validation, respectively. Fourteen time-domain and frequency-domain features were used to quantify the effect of SAHS on SaO2 recordings. Regression analysis was performed to estimate the functional relationship between the extracted features and the AHI. Multiple linear regression (MLR) and multilayer perceptron (MLP) neural networks were evaluated. The MLP algorithm achieved the highest performance with an intraclass correlation coefficient (ICC) of 0.91. The proposed MLP-based method could be used as an accurate and cost-effective procedure for SAHS diagnosis in the absence of PSG. eng info:eu-repo/semantics/restrictedAccess IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings info:eu-repo/semantics/article