Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Author
Keywords
Abstract
Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus.
Year of Publication
2020
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
6
Issue
Special Issue on Soft Computing
Number
1
Number of Pages
16-25
Date Published
03/2020
ISSN Number
1989-1660
Citation Key
URL
https://www.ijimai.org/journal/sites/default/files/files/2019/02/ijimai20206_1_2_pdf_13999.pdf
DOI
10.9781/ijimai.2019.02.001
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