Applying Neural Network Classifiers in the Diagnosis of the Obstructive Sleep Apnea Syndrome from Nocturnal Pulse Oximetric Recordings

The aim of this study was to assess the ability of neural networks as an assistant tool for the diagnosis of the obstructive sleep apnea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. Our method was based on spectral and nonlinear features extracted from overnight arterial oxygen saturation (SaO₂) recordings. A seven-element input vector was used for patient classification. We selected four spectral features from the estimated power spectral density (PSD) of SaO₂. In addition, three input features were computed from non-linear analysis of SaO₂. Two neural classifiers were assessed: the multilayer perceptron (MLP) network and the radial basis function (RBF) network. The RBF classifier provided the best diagnostic performance with an accuracy of 86.3% (89.9% sensitivity and 81.1% specificity).