QRS wave group classification method based on support vector machine

Abstract

The present invention provides a QRS wave group classification method based on a support vector machine. The method comprises the following steps: a) selecting an appropriate QRS wave group, and performing preprocessing of the data; using a support vector machine to perform training a classification model between each two classes of data; reading QRS wave group data required predicting the type of the waveform; c) performing preprocessing of the data, wherein the preprocessing comprises normalization processing and data dimension algorithm processing; d) performing prediction of the type of the waveform through adoption of the classification model; and e) using a voting method to realize identification and classification of a plurality of forms of QRS wave groups, and obtaining a prediction result. Through random selection of a training set, the QRS wave group classification method based on the support vector machine performs repeat training and test and finally obtain an optimal classification model so as to realize identification of different forms of the QRS wave groups.

Description

technical field [0001] The invention relates to a method for classifying QRS complexes based on a support vector machine, and belongs to the technical field of electrocardiogram (Electrocardiogram, ECG) automatic diagnosis. Background technique [0002] In the electrocardiogram, the QRS wave group represents the electrical activity of the ventricular septum and the left and right ventricles, reflecting the process of ventricular depolarization. Its shape has important diagnostic value for the analysis of heart diseases such as arrhythmia and bundle branch block. By identifying the shape of the QRS wave group, analyzing the abnormality of the QRS wave group, and improving the accuracy of the automatic diagnosis of the electrocardiogram. [0003] Commonly used methods for QRS complex shape recognition include Bayesian classification, fuzzy logic, K-nearest neighbor classification, neural network, multivariate statistics, decision tree and so on. These algorithms have their ow...

AI Technical Summary

Problems solved by technology

These algorithms have their own advantages, but they also have certain limitations. For example, the typical decision tree classification algorithm is efficient and easy to understand and implement, but it is prone to overfitting

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