Atrial fibrillation event detection method based on deep learning

A deep learning and event detection technology, applied in the field of atrial fibrillation detection, can solve the problems of thromboembolism, affecting the quality of life of patients, and difficult to break through detection accuracy.

Pending Publication Date: 2020-10-23
江苏正心智能科技有限公司
View PDF8 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Atrial fibrillation is a common arrhythmia problem, which is a serious disorder of atrial electrical activity. With age, the incidence of atrial fibrillation is also increasing. Atrial fibrillation not only affects the quality of life of patients, but also may cause thromboembolism, Heart failure and stroke, and with the development of long-t

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
  • Atrial fibrillation event detection method based on deep learning
  • Atrial fibrillation event detection method based on deep learning
  • Atrial fibrillation event detection method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0025] Such as Figure 1 ~ Figure 3 As shown, a deep learning-based atrial fibrillation event detection method of the present invention includes the following steps:

[0026] S1. Obtain the ECG signal (that is, the ECG signal) used to train the deep learning model for atrial fibrillation event detection, and then preprocess the ECG signal to remove interference and invalid data to prevent these interference signals from subsequent data processing Cause adverse effects in;

[0027] Preprocessing operations include: removing high-frequency glitch noise signals through a low-pass filter, removing baseline drift interference signals through a high-pass filter, and removing 50Hz power frequency interference signals through a notch filter;

[0028] S2. Perform QRS detection processing on the preprocessed ECG signal to extract the heartbeat information in the ECG signal;

[0029] QRS detection processing specifically includes the following steps:

[0030] S2.1. QRS heartbeat positioning. Sin...

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 provides an atrial fibrillation event detection method based on deep learning. The method can break through the constraint of insufficient features of a traditional atrial fibrillation detection algorithm and further improve the accuracy. The method comprises the following steps: S1, acquiring an electrocardiosignal for training an atrial fibrillation event detection deep learning model, and then performing preprocessing operation on the electrocardiosignal to remove interference and invalid data; S2, performing QRS detection processing on the preprocessed electrocardiosignal toextract heart beat information in the electrocardiosignal; S3, according to the QRS detection processing result, performing electrocardio dimension-increasing transformation processing; S4, building adeep learning model according to the data subjected to the dimension increasing transformation processing; and S5, carrying out training set and test set data division on the data set subjected to the dimension-increasing transformation processing through a five-fold cross validation method, and then carrying out training of the deep learning model through the training set data to finally obtainan atrial fibrillation detection model.

Description

technical field [0001] The invention relates to the technical field of atrial fibrillation detection, in particular to a method for detecting atrial fibrillation events based on deep learning. Background technique [0002] Atrial fibrillation is a common arrhythmia problem, which is a serious disorder of atrial electrical activity. With age, the incidence of atrial fibrillation is also increasing. Atrial fibrillation not only affects the quality of life of patients, but also may cause thromboembolism, Heart failure and stroke, and with the development of long-term ECG monitoring, the amount of ECG signal data that can be obtained is increasing, and the automatic detection algorithm for atrial fibrillation is also developed. However, the traditional automatic detection algorithm for atrial fibrillation Often limited by the feature acquisition process, it is difficult to break through the detection accuracy. Contents of the invention [0003] In view of the above problems, ...

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): A61B5/046A61B5/0472A61B5/00
CPCA61B5/7203A61B5/7225A61B5/7267A61B2576/023Y02A90/10
Inventor 赵卫周成龙
Owner 江苏正心智能科技有限公司
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