QRS wave group classification method based on support vector machine

A technology of QRS wave group and support vector machine, which is applied to computer components, diagnostic recording/measurement, pattern recognition in signals, etc., can solve problems such as easy over-fitting, improve accuracy and reduce calculation amount , the effect of improving the running speed

Inactive Publication Date: 2017-10-24
ZHEJIANG HELOWIN MEDICAL TECH
View PDF3 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These algorithms have their own advantages, but they also have certain limitations. For example, the typical decis

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • QRS wave group classification method based on support vector machine
  • QRS wave group classification method based on support vector machine
  • QRS wave group classification method based on support vector machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0035] Embodiment: a kind of QRS wave group classification method based on support vector machine according to the present invention, this method classifies QRS wave group according to the difference of form, carries out [-1,1] normalization processing and reduction to training set Dimensional processing; using radial basis function as the kernel function; optimizing the regularization parameters; training the support vector machine classification model, and finally using the voting method to achieve multi-category decision-making.

[0036] The specific implementation of the QRS wave group classification method based on support vector machine is as follows: figure 1 shown, including the following steps:

[0037] 1. Support vector machine

[0038] For a linearly separable binary classification problem, assume a positive sample x i satisfy

[0039] w T x i +b≥1. ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ ﹒ (1)

[0040] Negative sample x j satisfy

[0041] w T x i +b≤-...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62A61B5/00
CPCA61B5/7264G06V2201/03G06F2218/02G06F2218/12G06F18/214G06F18/2411
Inventor 孙斌朱玉奎何晓彤符灵建
Owner ZHEJIANG HELOWIN MEDICAL TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap