To solve the problem of realtime arrhythmia diagnosis, this paper designs and works out a realtime arrhythmia diagnosis system and proposes a realtime arrhythmia diagnosis algorithm based on feed forward neural networks(FFNN) and onedimensional convolution neural network (1DCNN). The system uses a wearable ECG (electrocardiogram) acquisition device to collect ECG signals, and then wirelessly transmits the ECG to the client software in real time for arrhythmia diagnosis, and finally automatically uploads the diagnosis results to the server. The algorithm takes raw ECG signals as input and segments with a 200ms time window, then uses a classifier based on the FFNN model to detect the position of the R peak in real time, the algorithm extracts the ECG sequence between three R peak and resamples it to a length of 360 as the ECG_RRR feature, finally uses a classifier based on the 1DCNN model for realtime arrhythmia detection. This paper uses the MITBIH database to train the algorithm model and test the system. The results show that the realtime arrhythmia diagnosis system and algorithm proposed in this paper have the characteristics of high accuracy, strong realtime performance and easy deployment. The recall rate, precision rate, and the overall accuracy of the system’s interpatient R peak position predictions are 980%, 995%, and 976% respectively. The overall accuracy for 5class interpatient arrhythmia classification is 915%.