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

TitleAutomated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsRoopa, C. K., and B. S. Harish
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueIn Press
VolumeIn Press
NumberIn Press
Date Published02/2019
Pagination1-10
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.

KeywordsClassification, ECG, Fuzzy
DOI10.9781/ijimai.2019.02.001
URLhttp://www.ijimai.org/journal/sites/default/files/files/2019/02/ip_2019_02_01_pdf_66432.pdf
AttachmentSize
ip_2019_02_01.pdf1.37 MB