Intelligent electrocardiogram data classification method based on voting ensemble learning

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. ...

AI Technical Summary

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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