Abstract:To solve the problem of realtime arrhythmia diagnosis, this paper designs and works out a realtime arrhythmia diagnosis system and proposes a realtime arrhythmia diagnosis algorithm based on feed forward neural networks(FFNN) and onedimensional convolution neural network (1DCNN). 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 200ms 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 1DCNN model for realtime arrhythmia detection. This paper uses the MITBIH database to train the algorithm model and test the system. The results show that the realtime arrhythmia diagnosis system and algorithm proposed in this paper have the characteristics of high accuracy, strong realtime performance and easy deployment. The recall rate, precision rate, and the overall accuracy of the system’s interpatient R peak position predictions are 980%, 995%, and 976% respectively. The overall accuracy for 5class interpatient arrhythmia classification is 915%.