SAS has became an increasingly important public-health problem since 1970. It can adversely affect neurocognitive, cardiovascular, respiratory diseases and can also cause behavior disorder. Moreover, up to 90% of these cases are obstructive sleep apnea (OSA). Presently, Polysomnography is considered as the gold standard for diagnosing sleep apnea syndrome (SAS). However, Polysomnography-based sleep studies are expensive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel. In this study, based on the nocturnal oxygen saturation (SpO2) signals, this work develops a method to classify patients with different levels of respiratory disturbance index (RDI) values. To achieve this goal, this study uses neural network in conjunction with different sets of feature variables to perform classification.