Sleep apnea is a very common respiratory disorder in the community that includes a range from upper airway obstruction to respiratory abnormalities and the absence of a breathing effort, which can lower people's standard of living and even cause death. Therefore, the sleep apnea needs to be diagnosed in a practical way and with high accuracy. The diagnosis of apnea is made by recording the physiological parameters of the patient with polysomnography (PSG) method and by examination of these parameters by specialist physicians, but it is a tedious and time consuming process. In order to simplify the apnea diagnosis process, phospletismography (PPG) signals are used instead of PSG records. PPG signals are suitable for diagnosis of apnea because they reflect changes in respiration. In the proposed study, a decision support system was developed to automatically diagnose apnea and to make apnea diagnosis easier and more objective using PPG signals. In the decision support system, the peaks of the PPG signal were determined and the heart rate variability (HRV) vector was generated depending on the time difference between these peaks. The mean and standard deviation values of the generated vector are determined as features for each epoch. The presence of the apnea at each epoch is classified using "Subspace K Nearest Neighbor (Subspace KNN)" and specified features. The "Subspace KNN" classifier was trained with 85% accuracy and then system was tested. As a result, sensitivity, accuracy and specificity rates were calculated as 91%, 95% and 90% respectively.