A Gradient Boosting Decision Tree Based Heart Beat Classification Method for Ambulatory ECG

Abstract

A dynamic electrocardiogram heart beat classification method based on a gradient lifting decision tree. This method can classify a single heart beat in the ECG signal in the actual dynamic electrocardiogram according to whether there is arrhythmia and the type of arrhythmia. The specific classification categories include Normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat of ventricular beat and normal beat, and pacemaker beat. The method includes the following steps: (1) acquiring training data; (2) Heartbeat interception and feature extraction; (3) Feature selection and classification model training; (4) Classification model application, in which step (3) adopts the feature selection method based on the tree model to select features, and adopts gradient lifting to make decisions The tree classification method trains a classification model. The invention is suitable for arrhythmia classification training of dynamic electrocardiograms and classification and identification of different types of cardiac beats, and can assist doctors to accurately read and analyze electrocardiograms.

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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