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00925njm 22002777a 4500 dc Marcos Martín, José Víctor author Hornero Sánchez, Roberto author Álvarez González, Daniel author Aboy, M. author Campo Matias, Félix del author 2012 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. 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 Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings