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.
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