Dynamic electrocardiogram heart beat classification method based on gradient boosting decision tree

Abstract

The invention relates to a dynamic electrocardiogram heart beat classification method based on a gradient boosting decision tree. The method comprises the steps that in actual dynamic electrocardiogram, classification is conducted on single heart beats in an electrocardiogram signal according to whether or not arrhythmia exists and the types of arrhythmia, specific classification categories comprise normal heart beats, supraventricular ectopic beat heart beats, ventricular ectopic beats, ventricular beat and normal beat fusion heart beats and pacemaker heart beats; the method comprises the following steps that 1, training data is obtained; 2, heart beat interception and feature extraction are conducted; 3, feature selection and classification model training are conducted; 4, classificationmodel application is conducted, wherein a tree-model-based feature selection method is adopted in step 3 to select features, and the classification model is trained through a gradient boosting decision tree classification method. The method is suitable for arrhythmia classification training of dynamic electrocardiogram and classification identification of different types of heart beats, and a doctor can be assisted in accurately reading and analyzing the electrocardiogram.

Description

technical field [0001] The invention relates to the technical field of automatic auxiliary detection of a dynamic electrocardiogram, in particular to a method for classifying heartbeats of a dynamic electrocardiogram based on a gradient lifting decision tree. Background technique [0002] With the continuous acceleration of the pace of human life, heart disease has become an important disease that threatens human health, and most heart patients are accompanied by arrhythmia. Therefore, accurate detection and diagnosis of arrhythmia is very important for the prevention, monitoring, treatment and assistance It is of great significance for doctors to diagnose and improve the efficiency of doctors to read ECG. [0003] There are many kinds of arrhythmia, supraventricular ectopic beat, ventricular ectopic beat, fusion of ventricular beat and normal heartbeat, and pacemaker heartbeat are not only common in heart disease patients, but also supraventricular ectopic beat, ventricular...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing arrhythmia classification methods that cannot adapt to the diversity of the actual ECG signal form, the present invention provides a dynamic ECG beat classification method based on a gradient lifting decision tree, which can effectively avoid other problems in the dynamic ECG. Affected by a variety of abnormal heartbeats, accurately identify the type of heartbeat

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