Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings

作者：Victor Marcos J; Hornero Roberto; Alvarez Daniel; Aboy Mateo; Del Campo Felix

来源：IEEE Transactions on Biomedical Engineering, 2012, 59(1): 141-149.

DOI：10.1109/TBME.2011.2167971

• Apnea-hypopnea index
• multiple linear regression
• multilayer perceptron neural networks
• oxygen saturation
• regression analysis
• sleep apnea-hypopnea syndrome

Nocturnal polysomnography (PSG) is the gold-standard 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 (SaO(2)) recordings and the results of an assessment study designed to characterize its performance. A set of 240 SaO(2) 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 SaO(2) 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.

• 出版日期2012-1