A Classification Method of Vector Cardiogram Based on Nonlinear Dynamic Features

A technology of nonlinear dynamics and ECG vector diagram, applied in the field of pattern recognition, can solve the problem of not extracting the electrodynamic characteristics of the electrocardiogram, and achieve good classification effect and high accuracy

Active Publication Date: 2022-02-01
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

None of these methods extracted the electrodynamic properties of the heart

Method used

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  • A Classification Method of Vector Cardiogram Based on Nonlinear Dynamic Features
  • A Classification Method of Vector Cardiogram Based on Nonlinear Dynamic Features
  • A Classification Method of Vector Cardiogram Based on Nonlinear Dynamic Features

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Experimental program
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Embodiment

[0116] Such as figure 1 As shown, it is a flow chart of the vector cardiogram classification method based on nonlinear dynamic characteristics in an embodiment of the present invention, including the following steps:

[0117] Step 1 Obtaining the ECG vector diagram: In order to make the research results true and reliable and to verify its better effect, the data set is PTB provided by (https: / / www.physionet.org / cgi-bin / atm / ATM) Diagnostic ECG Database database. The ECG is obtained through the Frank three-lead system, such as Figure 2a Shown is a Frank three-lead electrocardiogram of a patient with myocardial infarction, such as Figure 2b is the patient's ECG vector Figure three Dimensional visualization display schematic diagram, such as Figure 2c Shown is the Frank three-lead electrocardiogram of a normal person, such as Figure 2d is the person's ECG vector Figure three Dimensional visualization shows schematic diagrams.

[0118] Step 2 Data preprocessing: Media...

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Abstract

The invention discloses a method for classifying ECG vector diagrams based on nonlinear dynamic characteristics, belonging to the technical field of ECG detection; the method comprises the following steps: collecting Frank three-lead ECG vector diagram signals; using median filtering to remove noise ; extract 10 nonlinear dynamic features of each lead respectively; carry out normalization processing on each feature, carry out feature fusion, and use the training vector diagram mode and the test electrocardiogram mode in the nonlinear dynamics The differences in indicators can realize the classification of normal ECG vector diagram and abnormal ECG vector diagram. The above method applies the nonlinear dynamic analysis method to the classification of ECG vector diagram for the first time. The extracted nonlinear dynamic features can represent the dynamic properties of ECG vector diagram, and better excavate the intrinsic characteristics of ECG vector diagram. Abnormal and normal vector electrocardiograms are distinguished and suitable for use in routine electrocardiographic examinations.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a method for classifying ECG vector diagrams based on nonlinear dynamic characteristics. Background technique [0002] Cardiovascular disease is a common human disease that seriously threatens the safety of human life and property. In 2012, when the World Health Organization counted the top ten causes of death in the world, it pointed out that cardiovascular disease has become the first cause of death in the world, and its death toll has significantly exceeded the death toll of tumors and other diseases. As a bioelectrical signal that was studied and analyzed earlier and used to diagnose cardiovascular diseases, ECG is more periodic and easier to extract and analyze than other biological information, and it contains rich physiological information , so it is used as an important basis for evaluating the health of the human heart. [0003] ECG is an import...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/346
CPCA61B5/7267A61B5/318
Inventor 邓木清王丹俐范慧婕李枚格
Owner HANGZHOU DIANZI UNIV
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