Probabilistic neural network approach for the detection of SAHS from overnight pulse oximetry
Diagnosis of sleep apnea hypopnoea syndrome (SAHS) depends on the apneahypopnea index determined by the standard in-laboratory overnight polysomnography (PSG). PSG is a costly, labor intensive and, at times, inaccessible approach. Because of the high demand, the need for timely diagnosis and the associated costs, novel methods for SAHS detection are required. In this study, a novel multivariate system is proposed for SAHS detection from the analysis of overnight blood oxygen saturation (SpO2). 115 subjects with SAHS suspicion were studied. A starting set of 17 time domain, stochastic, frequency-domain and nonlinear features were initially computed from SpO2 recordings. Sequential forward feature selection and a probabilistic neural network with leave-one-out cross-validation were applied. Oxygen desaturations below a 4% threshold within 30s (ODI430), restorations of 4% within 10s (RES4), median value (Sat50), SD1 Poincar descriptor and the relative power in the 0.0130.067Hz frequency band (PSD15/75) formed the optimum features subset. 92.4% sensitivity and 95.9% specificity were achieved. Results significantly outperformed the univariate and multivariate approaches reported in literature. The outcome is a simple cost-effective tool that could be used as an alternative or supplementary method in a domiciliary approach to early diagnosis of SAHS.
probabilistic neural network sahs pulse oximetry sleep apnea multivariate spo2 cross-validation screening multiparametric