  Myocardial Infarction is one of the leading causes of death worldwide. This
paper presents a Convolutional Neural Network (CNN) architecture which takes
raw Electrocardiography (ECG) signal from lead II, III and AVF and
differentiates between inferior myocardial infarction (IMI) and healthy
signals. The performance of the model is evaluated on IMI and healthy signals
obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A
subject-oriented approach is taken to comprehend the generalization capability
of the model and compared with the current state of the art. In a
subject-oriented approach, the network is tested on one patient and trained on
rest of the patients. Our model achieved a superior metrics scores (accuracy=
84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the
benchmark. We also analyzed the discriminating strength of the features
extracted by the convolutional layers by means of geometric separability index
and euclidean distance and compared it with the benchmark model.
