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Classifying method and system, electronic equipment and storage medium

A classification method and technology of electronic equipment, applied in the field of machine learning, can solve problems such as low diagnostic accuracy of electrocardiogram, and achieve the effect of solving very low diagnostic accuracy and improving the accuracy rate

Active Publication Date: 2019-02-19
WUHAN ZHONGQI BIOLOGICAL MEDICAL ELECTRONICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the present application provides a classification method, system, electronic equipment and storage medium, which are used to solve the problem of very low accuracy of electrocardiogram diagnosis in the prior art in electrocardiographic examination work

Method used

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  • Classifying method and system, electronic equipment and storage medium
  • Classifying method and system, electronic equipment and storage medium
  • Classifying method and system, electronic equipment and storage medium

Examples

Experimental program
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no. 1 example

[0037] See figure 1 , figure 1 A schematic flowchart of the classification method provided by the embodiment of the present application is shown. This application provides a classification method applied to electronic equipment, including:

[0038] Step S500: Input the samples to be tested into the first classification algorithm, calculate and obtain the first probability list, and input the samples to be tested into the second classification algorithm, and obtain the second probability list through calculation. The first probability list and the second probability list include probabilities that the samples to be tested belong to each target category.

[0039] It should be noted that the first classification algorithm and the second classification algorithm here are different learning algorithms, and the first classification algorithm and the second classification algorithm include: machine learning algorithm, deep learning algorithm, enhanced learning model algorithm or re...

no. 2 example

[0110] See Figure 8 , Figure 8 A schematic structural diagram of the classification system provided by the embodiment of the present application is shown. This application provides a classification system 101, the classification system 101 includes:

[0111] The lead signal obtaining module 100 is configured to calculate multiple training samples through an automatic diagnosis algorithm to obtain multiple lead signals.

[0112] The filtered eigenvalue obtaining module 200 is configured to perform time-domain characteristic algorithm calculation and sorting and filtering on multiple lead signals to obtain multiple filtered eigenvalues.

[0113] The first classification algorithm obtaining module 300 is configured to train the first learning model according to a plurality of training samples and a plurality of filtered feature values ​​to obtain a trained first classification algorithm.

[0114] The second classification algorithm obtaining module 400 is configured to train...

no. 3 example

[0122] See Figure 9 , Figure 9 A schematic structural diagram of the electronic device provided by the embodiment of the present application is shown. An electronic device 104 provided in the present application includes: a processor 102 and a memory 103 , the memory 103 stores machine-readable instructions executable by the processor 102 , and the machine-readable instructions are executed by the processor 102 to perform the above method.

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Abstract

The invention provides a classifying method and system, electronic equipment and a storage medium. The classifying method and system, the electronic equipment and the storage medium are used for solving the problem in the prior art that accuracy of electrocardiogram diagnosis in electrocardio examination work is very low. The method comprises the steps of inputting a to-be-tested sample to a firstclassifying algorithm, conducting calculating to obtain a first probability list, inputting the to-be-tested sample to a second classifying algorithm, and conducting calculating to obtain a second probability list, wherein the first probability list and the second probability list comprise probabilities that the to-be-tested sample belongs to all target classifications; sifting out the first classification with the highest probability from the first probability list, and sifting out the second classification with the highest probability from the second probability list; judging whether or notthe first classification and the second classification are the same; if not, adding the probabilities of all the target classifications in the first probability list and the probabilities of all thetarget classifications in the second probability list to obtain the probabilities and values of all the target classifications to form a third probability list; sifting out a third classification withthe highest probability from the third probability list, and regarding the third classification as the final classification.

Description

technical field [0001] The present application relates to the technical field of machine learning, and in particular to a classification method, system, electronic equipment and storage medium. Background technique [0002] In the electrocardiographic examination work, the electrocardiogram mainly reflects the electrical excitation process of the heart, and is an important clinical means for doctors to perform cardiac examination and diagnosis. The traditional ECG signal diagnosis is based on the time-series ECG signals collected by the ECG machine, combined with the automatic diagnosis parameters and conclusions of the ECG machine, to give the diagnosis result. The ECG signal itself is very complex, and people of different races, genders, and ages have great differences in various pathological conditions. The diagnostic criteria are usually a summary of the doctor's years of clinical experience. The clinical experience accumulated by each expert is different, and the exist...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/04A61B5/0402G06N3/04
CPCA61B5/7267A61B5/316A61B5/318G06N3/045
Inventor 张玮朱涛李毅罗伟朱佳兵康成
Owner WUHAN ZHONGQI BIOLOGICAL MEDICAL ELECTRONICS