Model Based Feature Extraction Method for Myocardial Infarction Detection

Abstract

The electrical activity of the heart represented by the electrocardiogram (ECG) has been widely used for the detection of heart diseases. Long-term records require the automatic detection of cardiac events. In this work, the detection of myocardial infarction (MI) is performed by means of novel ECG features based on a synthesis ECG model previously described in the literature. The model consists of a sum of five Gaussians centered on each wave of the ECG (P, Q, R, S and T). Each Gaussian is fully specified by three parameters; location in time, amplitude and broadness. By fitting this set of Gaussians, and performing numerical and nonlinear optimization procedures in the resulting 15-dimensional space, we get this set of 15 parameters as features for classification.