Cardiac disease risk pre-warning system and method based on deep learning algorithm

A deep learning, disease risk technology, applied in computing, informatics, medical informatics, etc., can solve problems such as poor stability, high false alarm rate, low efficiency, etc., to achieve high accuracy and improve accuracy.

Inactive Publication Date: 2017-08-29
NANJING MEDICAL UNIV
View PDF9 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Aiming at the defects of high false alarm rate, low efficiency and poor stability existing in the existing heart disease early warning methods, the present invention proposes a heart disease risk early warning system and method based on a deep learning algorithm

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
  • Cardiac disease risk pre-warning system and method based on deep learning algorithm
  • Cardiac disease risk pre-warning system and method based on deep learning algorithm
  • Cardiac disease risk pre-warning system and method based on deep learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0047] Such as figure 1 As shown, the present invention provides a heart disease risk early warning system based on a deep learning algorithm, including a clinic, a deep learning framework 1 to N, a deep learning model 1 to N, a server, and an ECG acquisition device.

[0048] The specific implementation steps are:

[0049] Step 1: Acquire normal ECG signals and ECG signals of various heart diseases clinically, divide the signals into ECG signals of 10 seconds each, and use wavelet analysis algorithm to extract signal frequency rhythm information, and convert the obtained The frequency rhythm information is classified and sent to the deep learning framework 1 to N for training, and correspondingly various trained deep learning models 1 to N are obtained and stored in the server, where N≥4;

[0050] Step 2: The user wears an ECG acquisition device capable of ...

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 relates to a cardiac disease risk pre-warning system and method based on a deep learning algorithm. According to early-stage preparation, a normal electrocardiogram signal and electrocardiogram signals of various cardiac diseases are obtained clinically, the signals are segmented, the frequency rhythm information of the signals is extracted through the wavelet analysis algorithm, the obtained frequency rhythm information is transmitted into a deep learning framework in a classified mode, training is conducted, and various trained deep learning models are obtained. According to later-stage application, a user wears an electrocardiogram data acquisition device capable of carrying out data transmission, the acquired electrocardiogram signals are uploaded to a server, the server extracts the frequency rhythm information of the signals, and transmits the frequency rhythm information into the trained deep learning model of the frequency rhythm of the normal electrocardiogram signal at first for discrimination, if cardiac disease symptoms exist, the frequency rhythm information of the electrocardiogram signals is transmitted into the trained deep learning models of the various cardiac diseases in sequence for screening and evaluating, and a report about the risk of the cardiac disease of a specific class is fed back to a user.

Description

technical field [0001] The present invention relates to a heart disease risk early warning system and method, in particular to a heart disease risk early warning system and method based on a deep learning algorithm. Background technique [0002] As an important organ for blood work throughout the body, the heart plays an important role in maintaining our physical condition. The activity process of the human heart can be reflected in the form of electric current on the body surface, and the electrocardiogram (ECG) is recorded by using a certain detection instrument to record this ECG signal. The ordinary ECG examination in the hospital can only record the static and very short ECG signal of the subject. This kind of examination is not easy to find hidden heart diseases. Therefore, long-term ECG monitoring becomes the key to discover hidden heart diseases important means of disease. [0003] Long-term ECG monitoring brings a huge amount of ECG data. Due to the limited resour...

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 Applications(China)
IPC IPC(8): G06F19/00
CPCG16H50/30
Inventor 吴小玲李修寒竺明月张可王伟杨悦韩佳薇
Owner NANJING MEDICAL UNIV
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