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Artificial intelligence-based heart disease auxiliary detection method

A heart disease, auxiliary detection technology, applied in the directions of diagnostic recording/measurement, medical science, diagnosis, etc., to achieve the effect of reducing the time of diagnosis, improving the performance such as accuracy, and enriching the characteristics of dynamic pathological information

Inactive Publication Date: 2020-06-26
河北默代健康科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although some progress has been made in the study of electrocardiographic data, the applicant found that the domestic market is still in short supply of heart disease detection products with relatively high detection efficiency and accuracy when mining the electrocardiographic data of domestic hospitals

Method used

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  • Artificial intelligence-based heart disease auxiliary detection method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] Example 1. Sample set construction and sample data preprocessing

[0043] 1. Establishment of sample set

[0044] First, construct the sample set data for the convolutional neural network recognition model, the specific construction method is as follows:

[0045] 1.1 Composition of sample set

[0046] Include n clinically known heart healthy individuals (n>500) and m clinically known certain heart disease individuals (m>1000) as the sample population; collect image data of the sample population related to the specific heart disease as the sample set data; wherein, the image data of the sample population includes but not limited to vector cardiogram and electrocardiogram.

[0047] 1.2 Setting of sample label

[0048] The index data of the gold standard index of heart disease is used, and the expert consensus in the industry and the diagnosis information of doctors in different tertiary hospitals are used to jointly determine the disease label of the sample data. Lab...

Embodiment 2

[0055] Example 2. Acquisition of image data of various pathological features related to attributes of a specific cardiac disease

[0056] After obtaining the preprocessed sample set data in Embodiment 1, the pathological feature image data of a specific cardiac disease is obtained. The pathological feature image data of a specific heart disease includes an electrocardiogram and a vector cardiogram, etc.; the specific operation process is carried out according to the following steps:

[0057] Acquisition of vector cardiogram: collect labeled cardiac electrical signal data, perform preprocessing such as median filtering and wavelet transform filtering on the collected cardiac electrical signal data, and then convert to obtain vector cardiogram (VCG). The conversion method adopts KorsJ.A. et al. published in 1990 in European Heart Journal Journal of the 11(12):1083 paper describes the method and parameters to simultaneously obtain multidimensional X(t) , Y(t) and Z(t) ECG...

Embodiment 3

[0059] Example 3. Constructing and optimizing the convolutional neural network model on the preprocessed image data of pathological characteristics of cardiac diseases

[0060] 1. Constructing a Convolutional Neural Network Recognition Model for Specific Cardiac Diseases

[0061] Using the image data of the pathological features of the specific heart disease obtained in Example 2 as input data, machine learning is carried out to construct a convolutional neural network model adapted to the specific heart disease, and to realize the correlation between the quantitative index data of each pathological feature and the specific heart disease. One-to-one correspondence between attributes; specifically: building a convolutional neural network recognition model for electrocardiograms and electrocardiograms. A convolutional neural network recognition model of electrocardiogram or electrocardiogram, comprising at least one input layer, at least one hidden layer and at least one output ...

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Abstract

The invention provides an artificial intelligence-based heart disease auxiliary detection method. The method comprises the steps of training a convolution neural network identification model of a three-dimensional image such as an electrokinetic cardiogram, adjusting parameters of the convolution neural network identification model by means of migration learning, a Monte Carlo tree search algorithm and the like, and fusing detection information of entropy of a specific heart disease to obtain a comprehensive judgment result of heart disease detection. The method can solve a technical problem of model processing method of dynamic signal images with continuous cardiac electrical activity and fusion judgment of different output results. The method completes automatic extraction and intelligent diagnosis of the pathological features of heart diseases, and has high accuracy and detection efficiency.

Description

technical field [0001] The invention relates to the field of heart disease detection, in particular to an artificial intelligence-based auxiliary heart disease detection method. Background technique [0002] Cardiac disease is a relatively common disease related to the heart, blood vessels and neurohumoral tissues that regulate blood circulation, which can significantly affect the quality of life of patients. With the continuous development of the domestic economy, the eating habits of social residents have changed, the number of mental workers has continued to increase, and the incidence of cardiovascular diseases in China has continued to rise; therefore, accurate medical screening and testing for heart disease patients has become very important. However, the existing heart disease monitoring and detection methods are cumbersome and complicated, and early heart disease with weak symptoms cannot be identified in time and effectively, which seriously endangers the life safe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
CPCA61B5/7267A61B5/72A61B5/341A61B5/316A61B5/318
Inventor 袁坤生易力徐赤坤李伟何俊德
Owner 河北默代健康科技有限公司
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