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

End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization