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ECG signal classification and diagnosis method based on deep learning

An ECG signal and deep learning technology, applied in the field of medical signal processing, can solve problems such as low accuracy rate, achieve convenient detection and early warning, reduce work burden, and improve accuracy rate

Inactive Publication Date: 2019-06-18
WUHAN UNIV
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Problems solved by technology

[0012] In order to overcome the shortcomings of low accuracy in traditional ECG signal processing, the present invention proposes a deep learning-based ECG signal classification and diagnosis method

Method used

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  • ECG signal classification and diagnosis method based on deep learning
  • ECG signal classification and diagnosis method based on deep learning

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Embodiment Construction

[0037] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0038] figure 1 is a flow chart of the method of the present invention, figure 2 It is an overall block diagram of the technical solution of the present invention, and the present invention is a method for diagnosing heart diseases based on electrocardiographic signals of deep learning. Except for the input and output parts, it contains the following three modules: data preprocessing module, one-dimensional signal conversion to time-frequency diagram module and disease diagnosis module. The main function of the data preprocessing module is to use signal pr...

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Abstract

The invention provides an ECG signal classification and diagnosis method based on deep learning. The method comprises the steps as follows: colleting an ECG signal between the upper left limb and theupper right limb by ECG collecting equipment to obtain an original ECG signal; preprocessing the original ECG signal to obtain a denoised ECG signal, and extracting a characteristic wave of the denoised ECG signal; performing wavelet transform of fourth-order Daubechies wavelet on the characteristic wave of the ECG signal, and constructing a transform matrix for wavelet coefficients according to acertain rule; regarding the transform matrix as a time-frequency diagram corresponding to the characteristic wave of the ECG signal, transmitting the transform matrix to a deep learning module to learn diseases possibly suffered by a person whose ECG signal is collected. Characteristics of the signals can be analyzed and highlighted in the frequency domain, preliminary diagnosis for various heartdiseases on the basis of the ECG signal is realized, and accuracy of diagnosis is improved.

Description

technical field [0001] The invention relates to the field of medical signal processing, in particular to a method for classifying and diagnosing electrocardiographic signals based on deep learning. Background technique [0002] The heart is one of the most important organs of the human being, it provides the power for the blood to flow and transports the blood to every part of the body. Heart-related diseases have rapid onset and serious consequences, and have become the number one killer of human life in recent years. [0003] Arrhythmia is the main cause of cardiovascular disease, specifically refers to the irregular change of heart rate, including atrial fibrillation, ventricular extrasystoles, ventricular fibrillation and tachycardia. Sustained arrhythmia may have long-term effects on human health. Therefore, regular monitoring of heart rate is very important for the prevention and management of cardiovascular diseases. The body fluid around the heart can conduct elect...

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Application Information

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IPC IPC(8): A61B5/0402
Inventor 张捷李博豪向可馨施雪港范赐恩邹炼
Owner WUHAN UNIV
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