Atrial Fibrillation (AF) is probably the most common serious abnormal heart rhythm. It affects about 2% to 3% of the population in Europe and North America. In this study, we proposed an effective Atrial Fibrillation (AF) identification system based on RR interval (RRI) analysis. Two preprocessing methods were employed to remove the motion artifacts and ectopic beats. Three categories of RRI features, including base, bispectrum, and histogram features, were proposed to enhance the performance of the identifier. The roles of different feature categories were evaluated. The combination of the three categories of features were demonstrated to compensate with one another to construct an effective feature set for AF identification. When compared to other representative AF identifiers in the literature, the proposed method outperforms them with superior recognition rates by using much larger number of testing data.