Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Arterial fibrillation detecting method based on single lead electrocardiosignal time-frequency characteristic

A technology of time-frequency characteristics and ECG signals, applied in diagnostic recording/measurement, medical science, sensors, etc., can solve the problems of discrimination and low detection accuracy of atrial fibrillation, and achieve improved accuracy, high accuracy, and good robustness Effect

Active Publication Date: 2018-01-19
夏勇
View PDF2 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problems of low detection accuracy of atrial fibrillation and difficulty in distinguishing atrial fibrillation from other arrhythmic conditions in the prior art, and provide a method for detecting atrial fibrillation based on the time-frequency characteristics of single-lead ECG signals. The method integrates RR interval features and heartbeat waveform features, and has the advantages of two methods based on atrial activity and ventricular response

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
  • Arterial fibrillation detecting method based on single lead electrocardiosignal time-frequency characteristic
  • Arterial fibrillation detecting method based on single lead electrocardiosignal time-frequency characteristic
  • Arterial fibrillation detecting method based on single lead electrocardiosignal time-frequency characteristic

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0026] Embodiment 1: This embodiment describes an atrial fibrillation detection method based on the time-frequency characteristics of a single-lead ECG signal, which divides the original ECG signal into normal sinus rhythm, atrial fibrillation, other arrhythmias or excessive noise. Four categories, the method includes the following steps:

[0027] Step 1: For the single-lead surface ECG signal (the signal length is in the range of 10-60 seconds), first perform a preprocessing operation to remove the baseline drift and some noise interference in the ECG signal;

[0028] Step 2: Calculate the change of signal energy in the time domain based on the sliding window method, and find the first window whose energy is not higher than the median value of the energy of each subsequent window. The corresponding part is removed from the signal, and the third step is performed; if the window does not exist, the signal is considered to be too noisy, and the detection process ends;

[0029] ...

specific Embodiment approach 2

[0035] Embodiment 2: In the method for detecting atrial fibrillation based on the time-frequency characteristics of a single-lead ECG signal according to Embodiment 1, the specific steps of the second step are as follows:

[0036] (1) Divide the ECG signal into a series of windows, each window has a length of 1.8 seconds, and there is an overlapping area of ​​0.9 seconds between adjacent windows;

[0037] (2) Calculate the energy of the signal in each window, the formula is as follows:

[0038]

[0039] Among them, x is the discrete signal sequence in the window, x[n] represents the value of the nth sampling point in the window, T is the window duration, and N represents the number of sampling points in the window;

[0040] (3) Starting from the first window, compare its energy with the median value of the energy of each window in the signal, until a window is found so that its energy is not higher than the median energy of each subsequent window; set the window at The ser...

specific Embodiment approach 3

[0044] Embodiment 3: In the method for detecting atrial fibrillation based on the time-frequency characteristics of a single-lead ECG signal according to Embodiment 1, the specific steps of step 5 are as follows:

[0045] (1) Define the lower limit of the similarity within the group, that is, the minimum Pearson correlation coefficient of the heartbeat waveform within the group, and the value is about 0.8 (adjusted according to the signal noise intensity);

[0046] (2) Calculate the Pearson correlation coefficient between the heartbeat waveforms in the same signal, the formula is as follows

[0047]

[0048] Among them, X and Y respectively represent the sampling point sequence corresponding to the two heartbeat waveforms, E represents the mathematical expectation, μ X represents the average value of each sampling point in X, μ Y Represents the average value of each sampling point in Y, σ X represents the standard deviation of each sampling point in X, σ Y Represents the...

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 arterial fibrillation detecting method based on the single lead electrocardiosignal time-frequency characteristic, and belongs to the crossing field of information processingtechnologies and medical treatment and health. A distinctive characteristic of the arterial fibrillation detecting method is that for a high-noise singe lead electrocardiosignal, the Pearson correlation coefficient is utilized to carry out simple clustering on all heartbeat waveforms in the same signal, and then noise disturbance of the heartbeat waveforms is eliminated by utilizing the average method; and the representative heartbeat waveform of the signal is found out, and then the time-frequency characteristic of the representative heartbeat waveform is extracted based on the Matching Pursuits algorithm and then is used for classifying the whole electrocardiosignal. In addition, the characteristic of the RR interval is further integrated (the heart rate variability characteristic and the time-frequency characteristic of the heartbeat waveform are combined), and thus the features of a method based on atrial activities and a method based on ventricular response are both achieved, andthe detecting method has high accuracy and good robustness.

Description

technical field [0001] The invention belongs to the cross field of information processing technology and medical health, and relates to a method for classifying single-lead electrocardiographic signals, in particular to a method for detecting atrial fibrillation based on time-frequency characteristics of single-lead electrocardiographic signals. Background technique [0002] Atrial fibrillation (abbreviated as atrial fibrillation) is the most common persistent arrhythmia, with an incidence of 1 to 2% in the general population. The incidence of atrial fibrillation also increases with age, reaching 10% of people over the age of 75. Atrial fibrillation increases the risk of various diseases including sudden death, stroke, heart failure and coronary heart disease, and poses a serious threat to people's life and health. [0003] Because some atrial fibrillation is paroxysmal and may not have any obvious symptoms in the early stage, it is easy to be overlooked in the early stage,...

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): A61B5/0402A61B5/046A61B5/361
Inventor 刘阳张恒贵夏勇
Owner 夏勇
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com