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

TL;DR

This paper presents an end-to-end deep residual learning approach with dilated convolutions for highly accurate myocardial infarction detection and localization from ECG signals, distinguishing six MI locations with near-perfect accuracy.

Contribution

The study introduces a novel system that uses a pseudo-time-frequency representation from ECG signals and achieves state-of-the-art accuracy in MI detection and localization.

Findings

Achieved 99.99% accuracy on PTB database.

Distinguished six MI locations effectively.

Utilized a robust front-end inspired by speech processing.

Abstract

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.