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A noise reduction method for ECG signals based on group sparsity

An ECG signal, group sparse technology, applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve the problems of underestimation of ECG signal peak value, loss of original signal information, and insufficient smooth signal waveform, and achieve accurate and efficient reduction. noise, promoting group sparsity, and improving effectiveness

Active Publication Date: 2021-06-15
SHANDONG ARTIFICIAL INTELLIGENCE INST
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Problems solved by technology

The traditional filtering method has the phenomenon of discarding the detailed information of the signal together with the noise, and fails to make good use of the sparse characteristics of the ECG signal itself.
The sparse noise reduction method based on the L1 norm can guarantee the sparsity of the solution, but it is easy to cause underestimation of the peak value of the ECG signal and lose the original signal information
The full variation noise reduction method improves the underestimation of the signal peak value and retains more signal details, while the full variation noise reduction method is prone to produce jagged waveforms, resulting in unsmooth signal waveforms

Method used

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Embodiment Construction

[0021] The present invention will be further described below.

[0022] A kind of electrocardiographic signal denoising method based on group sparsity characteristic, comprises the steps:

[0023] a) Establish a mathematical model of ECG signal such as y=x+w, wherein, y∈R N is a noisy ECG signal, x is a clean ECG signal, x=[x(0),x(1),...,x(N)] T ∈ R N , w∈R N is the added noise signal, R N is an N-dimensional real number space, x n,K =[x(n),...,x(n+K-1)] T ∈ R K , 0≤n≤N-K, x n,K Indicates the vector 0≤n≤N-K whose initial subscript is n and consists of K points in the vector x;

[0024] b) Due to the ECG signal x and its differential They are all group sparse. In order to promote the group sparsity of the solution, the ECG signal group sparsity measurement function is selected to establish a convex optimization problem about the variable x. By solving the optimal solution x of the convex optimization problem * , get a clean ECG signal x, that is, x * =x;

[0025] c) m...

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Abstract

A method for denoising ECG signals based on the group sparsity property, which makes full use of the group sparsity property of ECG signals and promotes the understanding of group sparsity by rationally selecting the signal group sparsity measurement function. Based on the characteristics of the strip system, only one tridiagonal equation needs to be solved in each iteration, which improves the effectiveness and computational efficiency of the algorithm. It is not only suitable for non-overlapping group sparse signals, but also when the group sparse signals overlap. still works. By selecting a strictly convex cost function, the convergence of the algorithm is guaranteed, and finally converges to the only optimal solution. While maintaining the waveform characteristics of the original ECG signal, accurate and efficient noise reduction is achieved.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic signal denoising, in particular to a method for denoising electrocardiographic signals based on group sparsity characteristics. Background technique [0002] Factors such as population aging and people's unhealthy lifestyle have led to a sharp increase in the number of residents suffering from cardiovascular diseases in my country in recent years. Nowadays, cardiovascular disease has become the number one killer that threatens human life and health, and has the characteristics of high morbidity, disability, and fatality, as well as low treatment and control rates. Electrocardiogram (ECG), as a bioelectrical signal to record the electrical activity of the heart, has been widely used in the diagnosis of cardiovascular diseases because of its simplicity and non-invasiveness. However, the ECG signal itself is an extremely weak electrophysiological signal, and is easily affected by variou...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/00A61B5/318
CPCA61B5/7203
Inventor 陈长芳舒明雷刘瑞霞杨媛媛魏诺孔祥龙
Owner SHANDONG ARTIFICIAL INTELLIGENCE INST
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