Pulse oximetry is a non-invasive method for monitoring how much oxygenated hemoglobin is present in the blood. The principle of pulse oximetry is based on the red infrared light adsorption characteristics of oxygenated and deoxygenated hemoglobin. Even through the convenience of a pulse oximeter, its weak signal-to-noise ratio against motion artifacts and low perfusion makes it difficult to be accepted by execs devices. Several researchers have suggested the use of an adaptive noise cancellation (ANC) algorithm. They have demonstrated that ANC is feasible for reducing the effects of motion artifacts. Masimo Corporation developed a discrete saturation transformation (DST) algorithm that uses a reference signal and ANC. In commercial devices, it is very hard to escape it because Masimo’s patents are very powerful and a better method is yet to be developed. This study proposes a new method that can measure noise saturation as well as accurate oxygen saturation from signals with high motion artifacts without using ANC and DST. The proposed algorithm can extract a normal signal without noise from a signal with motion artifacts. The reference signal from a pulse oximeter simulator was used for the evaluation of our proposed algorithm and achieved good results.