Electrocardiosignal identification method based on generative adversarial networks and convolution recurrent neural networks

A technology of ECG signal and identification method, which is applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve the problems of data set sample imbalance, sample imbalance, etc., achieve reliable assistance and reference, improve accuracy, and accurately rate-boosting effect

A technology of ECG signal and identification method, which is applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve the problems of data set sample imbalance, sample imbalance, etc., achieve reliable assistance and reference, improve accuracy, and accurately rate-boosting effect

CN111990989APending Publication Date: 2020-11-27WUHAN UNIV
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  • Electrocardiosignal identification method based on generative adversarial networks and convolution recurrent neural networks
  • Electrocardiosignal identification method based on generative adversarial networks and convolution recurrent neural networks
  • Electrocardiosignal identification method based on generative adversarial networks and convolution recurrent neural networks

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Embodiment Construction

[0048] Exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures indicate functionally identical or similar elements. While various aspects of the embodiments are shown in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0049] Specifically, the present invention provides a single-lead ECG abnormal signal recognition method based on generative confrontation network and convolutional cyclic neural network, taking the kaggle data set as an example, such as figure 1 As shown, it includes the following steps:

[0050] Step 1: Denoising of data; before and after denoising figure 2 As shown, the ECG signal is a kind of bioelectrical signal collected from the human body surface, which has the common characteristics of bioelectrical signals: weak amplitude, low frequency, large impedance, randomness, etc. Most...

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Abstract

The invention provides a single-lead electrocardio abnormal signal identification method based on generative adversarial networks and convolution recurrent neural networks, and mainly solves the problem that data concentration samples are unbalanced. Categories being low in data concentration data volume are subjected to data enhancement, and then identification and classification of electrocardioabnormal signals are performed, so that reference is provided for assistance of doctors, the wrong diagnosis rate and missed diagnosis rate are reduced, and the workload of the doctor can be alleviated. The generative adversarial networks are used for enabling the data concentration samples to achieve the relative balance, so that training the convolution recurrent neural networks can be realized, and good classification effects can be achieved.

Description

technical field [0001] The invention relates to the field of electrocardiographic signal identification and classification, in particular to a single-lead electrocardiographic abnormal signal identification method based on a generative confrontation network and a convolutional cyclic neural network. Background technique [0002] Cardiovascular disease (CVD for short) refers to a series of diseases related to the heart or blood vessels, also known as circulatory system diseases. For the diagnosis of heart disease, electrocardiogram (Electrocardiogram, ECG or EKG) is a transthoracic method that records the electrophysiological activity of the heart in units of time, captures its electrical signal through electrodes placed on the skin, and draws it into a line and records it. Diagnostic technology. As a non-invasive recording method, the electrocardiogram is the most widely used and authoritative. [0003] In recent years, with the improvement of fuzzy recognition, artificial...

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Application Information

Patent Timeline
27 Nov 2020
Publication
CN111990989A
IPC
A61B5/0402; A61B5/0452; A61B5/04
CPC
A61B5/7267
Inventors
刘娟; 胡鹏