Many convenient wearable devices are being used for medical purposes, like measuring the heart rate (HR), blood pressure. With the sleep quality monitor problem, the key point is how to discriminate the sleeping state from waking one out of these signals. This paper proposes a Bayesian approach based on dynamic time warping (DTW) method for sleeping and waking classification. It uses HR and surplus pulse O₂ (SpO₂) signals to analyze the sleeping states and the occurrence of some sleep-related problems. The DTW is used to extract features from the original HR and SpO₂ signals. Then a Bayesian classification method is introduced for the discrimination of sleeping and waking states. Finally, a case study from a real-world applications, collected from the website of the Sleep Heart Health Study, is presented to show the feasibility and advantages of the DTW-based Bayesian approach.