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. Although the model was widely used previously with different purposes, its parameters had never been used as features for heartbeat classification even though they reflect the morphology of the ECG in an accurate manner. Physikalisch-Technische-Bundesanstalt (PTB) database was used to validate training and testing algorithms. Data was obtained from 48 healthy subjects (HS) and 95 patients with MI and was split into two datasets. The first dataset contains 190 beats from 26 HS, and 140 beats from 60 patients with MI and was used to train a support vector machine (SVM) classifier with linear kernel. The second dataset contains 88 beats from 22 HS, and 70 beats from 35 subjects with MI and was used to provide a detection performance assessment of the previously trained SVM. This assessment yielded an overall accuracy above 93%. The results show the feasibility of performing the separation between infarcted beats and physiological beats based on the new model-based features proposed. The simplicity of the linear kernel used in the SVM classifier shows the power of the proposed features for classification tasks.