Heart valve abnormality analysis method, system and device based on convolutional neural network

A convolutional neural network and heart valve technology, applied in the field of deep learning, can solve the problems of undistributed risk of misdiagnosis and low accuracy, and achieve the effect of reducing the risk of misdiagnosis and improving the accuracy.

Active Publication Date: 2020-10-13
BEIJING KEXIN TECH
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Problems solved by technology

[0004] The purpose of this application is to overcome the existing problems of low accuracy and undispersed ris...

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  • Heart valve abnormality analysis method, system and device based on convolutional neural network
  • Heart valve abnormality analysis method, system and device based on convolutional neural network
  • Heart valve abnormality analysis method, system and device based on convolutional neural network

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[0020] The technical solutions of the present application will be described in further detail below with reference to the drawings and embodiments.

[0021] The concept of the present application is introduced before the technical solution of the present application is further described in detail.

[0022] The purpose of this application is to reduce the risk of misdiagnosis in the existing analysis of heart valve abnormalities by using heart sounds. According to this purpose, a two-stage analysis of the heart sounds collected in different auscultation areas is considered. In the first stage, the convolutional neural network with multi-task learning is used to classify the heart sounds collected in the aortic valve auscultation area, mitral valve auscultation area and tricuspid valve auscultation area, and the output result is normal or abnormal heart valve. For the heart sounds in the auscultation area of ​​the tricuspid valve, since the corresponding abnormality of the tric...

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Abstract

An embodiment of the invention provides a heart valve abnormality analysis method based on a convolutional neural network. The method comprises the steps of segmenting a collected heart sound, and calculating a time-frequency spectrum of each heart sound segment; inputting the time-frequency spectrum of each heart sound segment into the convolutional neural network, and outputting a first result that the heart sound is normal or abnormal; extracting envelope spectrum characteristics and power spectrum characteristics of the heart sound with the first result that the heart sound is abnormal, inputting a logistic regression hidden semi-Markov model for segmentation, and outputting K states to which all frames in a cardiac cycle of the heart sound belong, wherein K is a natural number; extracting an energy feature of each state of the heart sound; and inputting the energy features into a support vector machine to obtain analysis results of aortic valve stenosis and/or aortic valve regurgitation and mitral valve stenosis and/or mitral valve regurgitation. According to the method, the heart valve abnormality is subjected to two-stage analysis, so that the accuracy of heart valve abnormality judgment can be improved, and the misdiagnosis risk is reduced.

Description

technical field [0001] The present application relates to the field of deep learning technology, in particular to a method, system and device for abnormal heart valve analysis based on convolutional neural network. Background technique [0002] Cardiac valve abnormalities are a common type of cardiovascular abnormalities, mainly including aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation and tricuspid regurgitation. Heart sound is a relatively easy to collect human physiological signal, which can reflect the health status of heart valves. Using heart sounds to analyze heart valve abnormalities has the advantages of low cost and easy promotion. [0003] The analysis of heart valve abnormalities based on traditional mechanical stethoscopes mainly relies on the experience of doctors, and it is difficult for ordinary residents to self-examine. Along with the invention and popularization of electronic stethoscope, researchers begin to use computer to ...

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Application Information

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IPC IPC(8): A61B7/04G06K9/62G06N3/04G06N3/08G10L25/03G10L25/30G10L25/66
CPCA61B7/04G06N3/08G10L25/30G10L25/66G10L25/03G06N3/045G06F18/2411G06F18/295
Inventor 颜永红
Owner BEIJING KEXIN TECH
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