Automatic diagnosis method for electrocardiographic abnormality

An automatic diagnosis and electrocardiogram technology, applied in the directions of diagnosis, diagnosis recording/measurement, medical science, etc., can solve the problem of reducing the feature resolution, and achieve the effect of improving the effect and improving the accuracy.

Inactive Publication Date: 2017-06-30
JINAN INSPUR HIGH TECH TECH DEV CO LTD
View PDF0 Cites 79 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Each convolutional layer in the convolutional neural network is followed by a calculation layer for local a

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
  • Automatic diagnosis method for electrocardiographic abnormality
  • Automatic diagnosis method for electrocardiographic abnormality

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0020] The method for automatically diagnosing ECG abnormalities described in this embodiment combines the learning ability of the RNN neural network for temporal features and the learning ability of the CNN neural network for spatial features to perform feature learning on the biological signal of the ECG; automatically characterize different types of abnormal ECGs, A neural network classifier based on a deep neural network was constructed; the classifier was trained using type-labeled electrocardiograms to improve classification accuracy, make it automatically diagnose abnormal electrocardiograms, and realize automatic classification of different arrhythmia types.

[0021] The main implementation process of the abnormal electrocardiogram automatic diagnosis method is as follows: firstly, multiple RNNs are combined to learn the ECG timing features of each lead, and hierarchically stacked CNNs are used to learn the spatial features of multi-lead ECG; then the above two features ...

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 an automatic diagnosis method for electrocardiographic abnormality and relates to the technical field of automatic diagnosis of electrocardiographic abnormality. The learning capacity of an RNN neural network to a time sequence and the learning capability of CNN to spatial features are combined to learn features of the electrocardiographic biological signal, and abnormal electrocardiographs of different types are automatically characterized. Then, a deep neural network based classifier is constructed, which is trained using an electrocardiograph with type annotations to improve the accuracy of classification, and automatic classification of different arrhythmia types is achieved. According to the invention, the process of manually extracting features is avoided; then, time sequence features and spatial features of the electrocardiographs are learned using RNN and CNN to form the classifier, and the classifier is trained through supervised learning so that the classifier can automatically diagnose abnormal electrocardiographs. As a result, the accuracy of automatic diagnosis and classification of electrocardiographic abnormality is improved.

Description

technical field [0001] The invention relates to the technical field of automatic diagnosis of abnormal electrocardiogram, in particular to an automatic diagnosis method for abnormal electrocardiogram. Background technique [0002] Electrocardiography is the most direct method and basis for diagnosing many diseases related to the heart. It has been widely used in clinical practice for many years due to its economical, reliable, fast, and non-invasive measurement methods. The electrophysiological activity of the heart can be directly reflected on the electrocardiogram, and the waveform, cycle and other information contained in the electrocardiogram are powerful evidence for doctors to make a diagnosis. [0003] The automatic diagnosis of ECG abnormality is an important medical auxiliary function. It can automatically diagnose the human ECG signal directly through the computer, detect abnormal ECG bands, and classify the abnormal types at the same time. In addition to being re...

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): A61B5/0402
CPCA61B5/7264A61B5/316A61B5/318
Inventor 高岩于治楼
Owner JINAN INSPUR HIGH TECH TECH DEV CO LTD
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