Iranian Journal of Electrical and Electronic Engineering
Iran University of Science and Technology
Sun, Nov 3, 2024
[
Archive
]
Remember me
Create Account
Reset Password
Home
Journal Information
About the Journal
Aims & Scopes
Editorial Board
Journal News
For Authors
Guide for Authors
How to Submit Your Articles
Copyright and Originality
Registration Form
Submit Your Article
Review Process
For Reviewers
Guide for Reviewers
Profession Form
Outstanding Reviewers
Policies and Ethics
Ethical Policies
Ethical Oversight
Basic Research Misconduct
Copyright and Originality
Conflict of Interest/Competing Interests
Authorship, Contributorship & Credit Author Statement
Change of Authorship
Research Data
Article Withdrawal
Article Retraction
Post-Publication Discussions and Corrections
Complaints and Appeals
Article- Archive
Current Issue
All Issues
Site Facilities
Download Forms
Search Contents
Tops
Contact Us
Contact Information
Contact Form
Volume 20, Issue 2 (June 2024)
IJEEE 2024, 20(2): 32-41
|
Back to browse issues page
10.22068/IJEEE.20.2.3018
Download citation:
BibTeX
|
RIS
|
EndNote
|
Medlars
|
ProCite
|
Reference Manager
|
RefWorks
Send citation to:
Mendeley
Zotero
RefWorks
Gorji Kandi N, Behnam H, Hosseinsabet A. Left Ventricular Entropy as a Measure of Wall Motion Abnormalities in Echocardiography Images. IJEEE 2024; 20 (2) :32-41
URL:
http://ijeee.iust.ac.ir/article-1-3018-en.html
Left Ventricular Entropy as a Measure of Wall Motion Abnormalities in Echocardiography Images
Neda Gorji Kandi
,
Hamid Behnam
,
Ali Hosseinsabet
Abstract:
(778 Views)
Cardiovascular diseases (CVD)
are today a major cause of death globally
that is diagnosed by measurement and quantification of left ventricle (LV) wall motion (WM) abnormality of the heart. The aim of this study was to assess the utility of left ventricular (LV) entropy, a novel measure of disease derived from two-dimensional (2D) echocardiography images that assesses the probability distribution of pixel intensities in the LV. The purpose of this research is to develop the method of LV entropy to predict heart diseases. In this algorithm, a frame is usually chosen as the reference frame to extract the region of interest (ROI) around LV and then it is mapped to all images in a cardiac cycle. Then Shannon Entropy transform was applied to calculate the distribution of pixel intensities across the LV so we obtained entropy curves and compared them. The main idea is to find a motion estimation accuracy.
The results obtained by our method are quantitatively evaluated to those obtained by an experienced echocardiographer visually on 22 normal cases and 19 myocardial infarction (MI) cases in apical four-chamber (A4C) view
. The entropy of diastole in MI cases was 0.50 (0.29-0.58) while in normal cases was 0.75 (0.64-1.13). The entropy of systole in MI cases was 0.64 (0.26-1.04) while in normal cases was 0.81 (0.63-1.26). The percent change of entropy for diastole and systole between normal and MI cases are 33.3% and 20.2%. The results indicate that the LV entropy curves of MI cases have less changes than normal cases
.
Keywords:
Echocardiography
,
Cardiac cycle
,
Entropy
,
Left ventricle
,
Wall motion
Full-Text
[PDF 617 kb]
(249 Downloads)
Type of Study:
Research Paper
| Subject:
Biomedical Signal & Image Processing
Received: 2023/08/13 | Revised: 2024/08/31 | Accepted: 2024/05/27
Rights and permissions
This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License
.