An electrocardiogram generation and classification method based on a generative adversarial network. An electrocardiogram curve is trained by means of a generative adversarial network (GAN) to obtain a generative model and a discriminative model, and more pieces of electrocardiogram curve data are mined and integrated, wherein the discriminative model uses a convolutional neural network (CNN) structure, such that the generated electrocardiogram curve is more authentic; and the electrocardiogram curve is trained by means of an RBF neural network to obtain a classification model, and the classification model is tested by means of the generated electrocardiogram curve, such that the classification model can be used in clinical research, thereby realizing precise medical treatment and intelligent medical treatment.
ELECTROCARDIOGRAM GENERATION AND CLASSIFICATION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK