Intelligent electrocardiogram data classification method based on voting ensemble learning

A technology of ECG data and integrated learning, applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve problems restricting the application of ECG, and achieve the effects of early detection, low threat, and early treatment

Active Publication Date: 2020-04-14
SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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Problems solved by technology

With the popularity of wearable ECG equipment, the acquisition of ECG is becoming increasingly simple, but only professional doctors can interpret ECG, which seriously restricts the application of ECG

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  • Intelligent electrocardiogram data classification method based on voting ensemble learning
  • Intelligent electrocardiogram data classification method based on voting ensemble learning
  • Intelligent electrocardiogram data classification method based on voting ensemble learning

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Embodiment Construction

[0028] Below by embodiment the present invention will be further described.

[0029] The ECG data intelligent classification method based on voting integrated learning of the present invention is characterized in that it is realized by the following steps:

[0030] a). Data preprocessing, obtain a sufficient number of N pieces of data from the Chinese cardiovascular database ccdd, and perform feature extraction on each piece of data, so that each piece of data consists of 172 columns, the first column in each piece of data is the serial number, the first 2 columns are labels, and the remaining 169 columns are features; the N pieces of data are divided into training sets and test sets according to the ratio of 30% and 70%, and label columns and feature columns are extracted at the same time;

[0031] The acquired data shall not be less than 20,000, such as 23,535.

[0032] The labels include 7 categories, which are: normal, atrial fibrillation, atrial premature beats, occasion...

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Abstract

An intelligent electrocardiogram data classification method based on voting ensemble learning in the invention is characterized by being realized through the following steps: a) carrying out data preprocessing; b) establishing a logistic regression model; c) establishing a decision tree model; d) establishing a support vector machine; e) establishing a naive Bayesian model; f) establishing a neuron model; g) establishing a k proximity model; and h) carrying out model integration. Finally, a model with the accuracy of not less than 80% is obtained, and the effect of the model is better than theeffect of the single model established in the steps b) to g). According to the intelligent electrocardiogram data classification method, enough data are firstly acquired from ccdd and are divided into a training set and a test set, then various models are established, and the model with accuracy of not less than 80% is finally obtained, thereby realizing intelligent identification and classification of normal, atrial fibrillation, atrial premature beat, accidental atrial premature beat, frequent atrial premature beat, atrial tachycardia and atrial fibrillation accompanied with rapid ventricular rate, and realizing early discovery and early treatment of cardiovascular diseases.

Description

technical field [0001] The present invention relates to an intelligent classification method of electrocardiographic data, and more specifically, to an intelligent classification method of electrocardiographic data based on voting ensemble learning. Background technique [0002] With the increasing aging of the global population, the number of people suffering from heart disease is increasing. According to incomplete statistics, about one-third of the world's death population is caused by heart disease; in my country, about 540,000 people die of heart disease every year. Heart disease and other cardiovascular diseases caused by it are constantly threatening human health, and it is particularly important to prevent and diagnose cardiovascular diseases in various ways in advance. With the popularity of wearable ECG devices, the acquisition of ECG has become increasingly simple, but only professional doctors can interpret ECG, which seriously restricts the application of ECG. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
CPCA61B5/7267A61B5/318
Inventor 王迪武鲁葛菁赵志刚霍吉东李响李娜
Owner SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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