RT info:eu-repo/semantics/article
T1 Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings
A1 Marcos, J. V.
A1 Hornero, R.
A1 Álvarez, D.
A1 Aboy, M.
A1 Del Campo, F.
AB 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.
PB IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC
SN 0018-9294
YR 2012
FD 2012
LK https://uvadoc.uva.es/handle/10324/65597
UL https://uvadoc.uva.es/handle/10324/65597
LA eng
NO IEEE Transactions on Biomedical Engineering, 2012, vol. 59, n. 1, p. 141-149.
NO Producción Científica
DS UVaDOC
RD 16-ago-2024