Obstructive sleep apnea (OSA) is a common respiratory disorder during sleep, in which the airways are collapsed and impair the respiration. Apnea is s cessation of airflow to the lungs which lasts at least for 10s. The current gold standard method for OSA assessment is full night polysomnography (PSG); however, its high cost, inconvenience for patients and immobility have persuaded researchers to seek simple and portable devices to detect OSA. In this paper, we report on developing a new system for OSA detection and monitoring, which only requires two data channels: tracheal breathing sounds and the blood oxygen saturation level (S(a)O(2)). A fully automated method was developed that uses the energy of breathing sounds signals to segment the signals into sound and silent segments. Then, the sound segments are classified into breath, snore (if exists) and noise segments. The S(a)O(2) signal is analyzed to find the rises and drops in the S(a)O(2) signal. Finally, a fuzzy algorithm was developed to use this information and detect apnea and hypopnea events. The method was evaluated on the data of 40 patients simultaneously with full night PSG study, and the results were compared with those of the PSG. The results show high correlation (96%) between our system and PSG. Also, the method has been found to have sensitivity and specificity values of more than 90% in differentiating simple snorers from OSA patients.