RESEARCH ARTICLE
Automated Risk Identification of Myocardial Infarction Using Relative Frequency Band Coefficient (RFBC) Features from ECG
Gohel Bakul*, U.S Tiwary
Article Information
Identifiers and Pagination:
Year: 2010Volume: 4
First Page: 217
Last Page: 222
Publisher ID: TOBEJ-4-217
DOI: 10.2174/1874120701004010217
Article History:
Received Date: 14/3/2010Revision Received Date: 3/7/2010
Acceptance Date: 9/7/2010
Electronic publication date: 10/10/2010
Collection year: 2010
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
Various structural and functional changes associated with ischemic (myocardial infarcted) heart cause amplitude and spectral changes in signals obtained at different leads of ECG. In order to capture these changes, Relative Frequency Band Coefficient (RFBC) features from 12-lead ECG have been proposed and used for automated identification of myocardial infarction risk. RFBC features reduces the effect of subject variabilty in body composition on the amplitude dependent features. The proposed method is evaluated on ECG data from PTB diagnostic database using support vector machine as classifier. The promising result suggests that the proposed RFBC features may be used in the screening and clinical decision support system for myocardial infarction.