Application of Gaussian Distribution and Wavelet Transform in ECG Simulation

For the electrocardiograph correction and equipment maintenance, Traditional electrocardiogram(ECG) simulation methods have defects in fidelity and flexibility. In order to solve this problem, this paper proposes a comprehensive model of wavelet transform and Gaussian distribution, a variety of ECG with freely pre-set features could be simulated by this model, in addition to the flexibility and fidelity in generating simulated ECG. Secondly, the machine learning classification model was used to train the simulated five types of abnormal ECG. MIT-BIH ECG database was used as the test set to obtain the classification results to evaluate the quality of simulated ECG. Finally, the recognition accuracy of five kinds of ECG by the classifier is above 80%. It proves the feasibility of this study, which plays an essential role in the maintenance of a variety of ECG medical equipment and enhancement of ECG data template library.