  In this report, I investigate the use of end-to-end deep residual learning
with dilated convolutions for myocardial infarction (MI) detection and
localization from electrocardiogram (ECG) signals. Although deep residual
learning has already been applied to MI detection and localization, I propose a
more accurate system that distinguishes among a higher number (i.e., six) of MI
locations. Inspired by speech waveform processing with neural networks, I found
a more robust front-end than directly arranging the multi-lead ECG signal into
an input matrix consisting of the use of a single one-dimensional convolutional
layer per ECG lead to extract a pseudo-time-frequency representation and create
a compact and discriminative input feature volume. As a result, I end up with a
system achieving an MI detection and localization accuracy of 99.99% on the
well-known Physikalisch-Technische Bundesanstalt (PTB) database.
