A method for automatic classification of ECG signals

An ECG signal and classifier technology, applied in diagnostic signal processing, medical science, sensors, etc., can solve problems such as unstable ECG signal classification, improve the phenomenon of over-smoothness of the general threshold, improve stability, and improve accuracy Effect

Active Publication Date: 2017-04-05
HEBEI UNIVERSITY
View PDF9 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a kind of automatic classification method of electrocardiogram signal, to solve the unstable problem of classification algorithm of electrocardiogram signal under different human bodies and different environments

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
  • A method for automatic classification of ECG signals
  • A method for automatic classification of ECG signals
  • A method for automatic classification of ECG signals

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] This embodiment is implemented in a computer with Intel Xeon CPU E5-2697@2.70GHz, internal memory 128.00GB, Win7, 64-bit operating system, and the entire ECG signal automatic classification algorithm is implemented in Matlab language.

[0048] The implementation process of the present invention is as figure 1 Shown:

[0049] a) Obtain the original ECG signal of the human body, perform filtering processing, and detect the R wave of the filtered ECG signal, which specifically operates according to the following steps:

[0050] (1) ECG original signal collection: The present invention utilizes the MedSun 18-lead Holter of Beijing Pengyang Fengye to collect the ECG signal of the human body for a long time, and its sampling output frequency is 250 Hz, and the collected ECG data is stored in the form of TXT. It can be easily read into the Matlab environment for display, and its form is as follows figure 2 .

[0051] (2) Filtering the collected ECG raw signal data:

[0052]...

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 invention discloses a method for automatic classification of electrocardiographic signals, which is realized according to the following steps: a) acquiring the electrocardiographic signals of the human body, performing filtering processing, and detecting the R wave of the filtered electrocardiographic signals; b) detecting After the R wave, build a data set, the data set is composed of several sets of heart beat data, each set of heart beat data has a label; c) construct a sparse automatic encoding deep learning network; d) train the sparse Automatically encode the deep learning network; e) according to the network weights of the first hidden layer, the network weights of the second hidden layer and the network weights of the softmax classifier obtained in step d), input the heartbeat data to be tested into the Sparse auto-encoding deep learning network to obtain heartbeat data for classification output. The present invention applies the sparse auto-encoding deep learning network to the classification of heart beat data, utilizes its self-learning ability and the characteristics of deep feature mining, extracts deeper features of the signal, and classifies the heart beat data.

Description

technical field [0001] The invention relates to an automatic detection and analysis technology of electrocardiographic signals, in particular to a method for automatic classification of electrocardiographic signals. Background technique [0002] Heart disease is hidden and latent, and it is difficult to show it on the ECG when it does not occur, and it is short-lived when it occurs, and it is too late to observe the ECG. To this end, it is necessary to carry a 24-hour Holter to the patient to collect ECG signals for 24 hours, and then hand over the ECG data to the doctor, who will analyze the data. The amount of data generated at this time is huge, and doctors need a lot of time to find abnormal heart beats. Although Holter's built-in software system can automatically analyze heartbeats and give statistical information, due to the large differences in human body and complex ECG changes, some heartbeats still need to be manually recognized and corrected by doctors. Finding ...

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
Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0402A61B5/0456A61B5/352
CPCA61B5/72A61B5/316A61B5/352A61B5/318
Inventor 刘秀玲杨建利白洋杜海曼王洪瑞
Owner HEBEI UNIVERSITY
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