Intelligent identification method for electrocardiogram data based on residual network

A technology of ECG data and intelligent recognition, which is applied in the field of intelligent medical care, can solve the problems that the generalization ability of the algorithm is difficult to be guaranteed, and achieve the effects of high accuracy, good real-time recognition, and high feature level

Inactive Publication Date: 2018-06-19
智慧康源(厦门)科技有限公司
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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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  • Intelligent identification method for electrocardiogram data based on residual network
  • Intelligent identification method for electrocardiogram data based on residual network
  • Intelligent identification method for electrocardiogram data based on residual network

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Embodiment Construction

[0023] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be described in further detail below in conjunction with specific embodiments.

[0024] The invention discloses an intelligent identification method of ECG data based on a residual network, comprising the following steps:

[0025] S1. Select the electrocardiogram of the MIT electrocardiogram database for processing: according to the position of the R wave, the electrocardiogram is used to intercept the atlases of the heartbeat signal at 130 points and 190 points before and after the R wave, and randomly divide the atlases into a training set and a test set at a ratio of 4:1. set. Divide the graph into training set and test set, and generate training set and test set labels respectively. The data of the test set and the training set are converted into the input format of mxnet.

[0026] S2. If figure 1 As shown, a deep convoluti...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
CPCA61B5/7264A61B2576/023A61B5/318
Inventor 赵仲明李端王宇轩崔桐张世影
Owner 智慧康源(厦门)科技有限公司
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