Arrhythmia classification is a hot topic in physiological signal analysis. Arrhythmias are very common in clinical practice, and they are accompanied by abnormal patterns and rhythms in the heartbeat of the electrocardiogram signal. Correct and timely detection of arrhythmias and accurate early warning of cardiovascular diseases are of particular importance in the early stage of clinical diagnosis. However, the lack of real time diagnosis of electrocardiogram may delay the best time for patient treatment. Implementing heart rate disorder classification algorithms at edge-side smart terminals such as wearable devices enable real-time analysis and processing of electrocardiogram signals. In addition, they improve the flexibility and safety of the devices as well. By far, the field programmable gate array devices have been widely used in physiological signal processing as a form of edge computing due to its capability of real-time pipeline operation. Whereas, the field programmable gate array implementation needs a long development cycle, has high cost and is difficult to debug. To address these problems, the new high-level synthesis tool Vivado HLS from Xilinx is used to implement the arrhythmia five classification algorithm based on the MIT-BIH dataset. By using a Xilinx Zynq field programmable gate array, an average classification accuracy of 99.12% on the electrocardiogram signal test set is achieved. Moreover, an average of 3.185 ms required to classify a single heartbeat is realized, which leads to a speedup of more than 5.64 times compared to a single ARM core on the pure PS side.