SAS has become an increasingly important public-health problem in recent years. It can abversely affect neurocognitive, cardiovascular, respiratory diseases and can also cause behavior disorder. Moreover, up to 90¢H of these cases are obstructive sleep apnea (OSA). Therefore, it is important that how to diagnose, detect and treat OSA. The respiratory disturbance index is one parameter of estimating OSA. Polysomnography can monitor the OSA with relatively fewer invasive techniques. However, polysomnography-based sleep studies are expensive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel. Based on the digital oximetry, this work introduces the estimating respiratory disturbance index. In particular, via signal processing, feature parameters and artificial intelligence, this thesis describes an off-line SpO2-based RDI estimating system.