One aspect of the present invention is to assess the performance of automated analysis of blood oxygen saturation (SpO2) recordings as a screening tool for OSAHS. As an initial step, statistical, spectral and nonlinear features are estimated to compose an initial feature set. Then, a fast correlation-based filter (FCBF) is next applied to search for the optimum subset. Finally, the discrimination power (OSAHS negative vs. OSAHS positive) of three pattern recognition algorithms is assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and logistic regression (LR). According to another aspect of the invention, oximetry is used to determine the OSAHS severity in children. For testing the severity of OSAHS, first spectral analysis is conducted to define and characterize a frequency band of interest in SpO2. Then the spectral data is combined with 3% oxygen desaturation index (ODI3) by means of a multi-layer perceptron (MLP) neural network, in order to classify children into one of the three OSAHS severity groups.
