Atrial fibrillation (AF) is the most common and sustained heart rhythm disorder, increasing the risk of stroke and death, and its incidence is destined to increase as the population ages. Current diagnostic methods are primarily through symptom or other indirect medical assessment methods. The fast-developing wearable technologies significantly promote the progress in ambulatory electrocardiogram (ECG) monitoring. This is a challenge to develop the devices that can detect AF in wearable electronic devices, with accessibility, sensitivity, ease of use, low-cost efficiency, and high computing power. Here, we first give a brief introduction to physiological concepts for development of detection algorithms. Then, we describe several kinds of AF features in dynamic signals. These features are important part of the automatic detection of AF, and a thorough understanding of these concepts can help researchers gain better insight into AF detection. Finally, seven AF features were extracted from the RR interval time series and were input into a SVM model to train AF/non-AF classifier. The results on the wearable ECGs verified that the proposed model could provide good identification for AF events.