Intelligent identification method for electrocardiogram data based on residual network

Abstract

The invention discloses an intelligent identification method for electrocardiogram data based on a residual network. The intelligent identification method comprises the steps of S1, according to the Rwave position, intercepting a map in front and behind the heartbeat signal R wave position of an electrocardiogram, and dividing the map into a training set and a test set proportionally; S2, constructing a deep convolutional neural network structure with a residual structure, and adding dense connection based on the original residual network; S3, inputting the training set into the network structure for iterative training, and conducting iteration continually until the layer of the network structure is increased to 152; S4, saving a model and testing the output. The intelligent identification method has high feature level and overall importance, and achieves intelligent identification of the electrocardiogram data, and the recognition accuracy is high.

Description

technical field [0001] The invention relates to the field of intelligent medical technology, in particular to an intelligent identification method of electrocardiographic data based on a residual network. Background technique [0002] The residual network (Residul Networks ResNets for short) is proposed to solve the problem of deep network optimization. The output of the residual structure is expressed as F(x)+x, compared to using multiple stacked nonlinear layers to directly learn the identity mapping F(x)=x, the residual structure directly learns F(x)=0 to make training easier. [0003] Electrocardiogram (ECG) refers to the graph that the heart is excited successively by the pacemaker, atrium, and ventricle in each cardiac cycle, accompanied by changes in bioelectricity, and various forms of potential changes are drawn from the body surface through the electrocardiogram recorder. Due to the morphological changes of ECG wave groups, including Q wave, wide deformity, M wave...

AI Technical Summary

Problems solved by technology

Although the classic classifier has achieved the effectiveness on the standard data set or its subset, the generalization ability of the algorithm is difficult to be guaranteed

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