End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization
This work addresses a critical medical diagnosis problem for healthcare, but it is incremental as it builds on existing deep residual learning methods for MI detection.
The paper tackles myocardial infarction detection and localization from ECG signals by proposing an end-to-end deep residual learning system with dilated convolutions, achieving 99.99% accuracy on the PTB database.
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.