Automatic diagnosis of ECG disease based on intelligent simulation modeling

In order to better assist doctors in diagnosing cardiovascular diseases, a set of end-to-end automatic diagnosis algorithms for ECG diseases based on intelligent simulation modeling are proposed. Firstly, wavelet transform and threshold method are used to denoise the ECG signal and locate the waveform in this paper. Secondly, waveform features are extracted. Finally, the rule method is used to convert the doctors’ thinking of diagnosing the disease into a description of the ECG characteristics of the disease to diagnose the ECG disease, and the algorithm is verified on the public database CCDD and the private data all-in-one machine data. The results show that this method is not inferior to the deep learning method. Now 11 types of diseases and 10 types of rhythm can be diagnosed.