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Myocardial infarction detection method based on BiGRU depth neural network

A deep neural network and detection method technology, applied in the field of heart beat detection and classification, can solve problems such as difficult to use big data, achieve the effects of improving detection efficiency, effective deep learning classification, and improving accuracy

Inactive Publication Date: 2019-06-07
ZHENGZHOU UNIV
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Problems solved by technology

[0004] Manual design features mainly rely on the prior knowledge of the designer, and it is difficult to take advantage of the advantages of big data. Due to the manual adjustment of parameters, only a small number of parameters are allowed in feature design

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  • Myocardial infarction detection method based on BiGRU depth neural network
  • Myocardial infarction detection method based on BiGRU depth neural network
  • Myocardial infarction detection method based on BiGRU depth neural network

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Embodiment Construction

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0047] A method for detecting myocardial infarction based on BiGRU deep neural network, comprising the following steps:

[0048] 1) Data preprocessing, using median filter to filter out baseline drift in the original ECG signal, using Butterworth digital band-stop filter to filter out power frequency interference in the original ECG signal, using Chebyshev digital low-pass The filter filters out myoelectric interference;

[0049] 2) Segmentation of cardiac be...

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Abstract

The invention relates to a myocardial infarction detection method based on a BiGRU depth neural network. The method comprises the following steps: 1) data pretreatment: filtering baseline drift in anoriginal electrocardiosignal by using median filtering, filtering industrial frequency interference in the original electrocardiosignal by using a Butterworth digital band-stop filter, and filtering myoelectric interference by using a Chebyshev digital low-pass filter; 2) heart beat segmentation: detecting R wave peak value through dyadic spline wavelet transform, further calculating RR interval and extracting QRS wave group data; and 3) model training: carrying out depth learning classification on the waveform detected in the step 2) through the BiGRU depth neural network. The method has theadvantages of carrying out accurate detection and classification on myocardial infarction and effectively carrying out deep learning and classification on the electrocardiosignals.

Description

technical field [0001] The invention belongs to the technical field of cardiac beat detection and classification, and in particular relates to a method for detecting myocardial infarction based on a BiGRU deep neural network. Background technique [0002] Cardiovascular disease is one of the diseases that seriously threaten human health. In my country, cardiovascular disease is gradually becoming a high incidence. Myocardial infarction refers to the occurrence of atherosclerotic changes in the coronary arteries that nourish the myocardium. The cholesterol plaque deposited on the inner wall of the lumen falls off and forms a thrombus, which blocks a certain coronary artery and prevents a certain part of the myocardium from receiving blood supply for a long time. Myocardial infarction occurs. ischemia, injury or even necrosis. Myocardial infarction has a very high mortality and disability rate. In the past, myocardial infarction mostly occurred in the elderly in their 60s an...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402A61B5/0472A61B5/366
Inventor 李润川张行进王宗敏
Owner ZHENGZHOU UNIV
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