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A Classification Method of Cardiac Electromagnetic Signals Combining Hilbert Curve and Ensemble Learning

An electromagnetic signal, integrated learning technology, applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve problems such as imbalance, difficult categories of cardiac electromagnetic signal classification model training, etc., to improve classification accuracy and solve category imbalance. Problems, the effect of reducing the difficulty of training

Active Publication Date: 2022-03-29
BEIHANG UNIV
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is: to overcome the difficult problem of training the electrocardiogram signal classification model as a time series and the problem of category imbalance, and to provide a kind of electrocardiogram signal classification combined with Hilbert curve and integrated learning for the real-time detection of myocardial infarction method, the classification model obtained by this method has the characteristics of simple training, high classification accuracy, fast detection speed, good adaptability and high reliability

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  • A Classification Method of Cardiac Electromagnetic Signals Combining Hilbert Curve and Ensemble Learning
  • A Classification Method of Cardiac Electromagnetic Signals Combining Hilbert Curve and Ensemble Learning
  • A Classification Method of Cardiac Electromagnetic Signals Combining Hilbert Curve and Ensemble Learning

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[0042] In order to make the object, technical solution and advantages of the present invention more clear, the exemplary embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other. The classification targets involved in the foregoing technical solutions may be electrical cardiac signals (ECG) and magnetic cardiac signals (MCG). The following uses ECG signals as an example to describe the specific implementation process of the present invention.

[0043] Such as figure 1 Shown, the inventive method specifically comprises the following steps:

[0044] (1) Obt...

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Abstract

The invention relates to a method for classifying electrocardiographic signals combined with Hilbert curves and integrated learning, which belongs to the field of electrocardiographic signal classification and has the characteristics of simple training, high classification accuracy, fast detection speed, good adaptability and high reliability. The present invention comprises the following steps: (1) obtaining electrocardiogram signals and performing preprocessing and splitting them into multiple segments of cardiac beat signals; (2) filling each segment of cardiac beat signals into image signals by using a Hilbert curve and reshaping to obtain a data set; (3) Use the EasyEnsemble algorithm to balance the categories of the data set; (4) Use the integrated learning method and the Stacking combination strategy to obtain the classification model, and finally evaluate the classification model.

Description

technical field [0001] The invention relates to the field of electrocardiographic signal classification, in particular to a method for classifying electrocardiographic signals combined with Hilbert curves and integrated learning. Background technique [0002] Coronary heart disease, also known as ischemic heart disease, is the leading cause of death in the world, according to a 2016 World Health Organization (WHO) report. According to WHO research, more than 17.7 million people die from cardiovascular diseases every year, 80% of which are caused by heart disease. Myocardial infarction, which belongs to coronary heart disease, is the result of insufficient blood flow to the heart due to partial or complete blockage of coronary arteries. Patients with myocardial infarction can be diagnosed by methods such as electrocardiography, echocardiography, magnetic resonance imaging (MRI), changes in cardiac biomarkers such as creatine kinase MB (CK-MB), troponin, and myoglobin. In pr...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/243A61B5/00A61B5/318A61B5/346A61B5/366
CPCA61B5/7235A61B5/7267A61B5/7203A61B5/725A61B5/7253
Inventor 马辛付幸文曹一荻
Owner BEIHANG UNIV