Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network

TL;DR

This paper demonstrates that one-dimensional convolutional neural networks can effectively diagnose coronary artery disease from ECG signals, offering improved accuracy and reduced complexity without feature extraction.

Contribution

It introduces the application of 1D-CNNs for CAD detection directly from ECG data, exploring sample length effects and network simplification.

Findings

Highest accuracy achieved with 250 sample length

1D-CNNs can interpret ECG signals without feature extraction

Sample size significantly impacts model performance

Abstract

Coronary Artery Disease (CAD) diagnostic to be a major global cause of death, necessitating innovative solutions. Addressing the critical importance of early CAD detection and its impact on the mortality rate, we propose the potential of one-dimensional convolutional neural networks (1D-CNN) to enhance detection accuracy and reduce network complexity. This study goes beyond traditional diagnostic methodologies, leveraging the remarkable ability of 1D-CNN to interpret complex patterns within Electrocardiogram (ECG) signals without depending on feature extraction techniques. We explore the impact of varying sample lengths on model performance and conduct experiments involving layers reduction. The ECG data employed were obtained from the PhysioNet databases, namely the MIMIC III and Fantasia datasets, with respective sampling frequencies of 125 Hz and 250 Hz. The highest accuracy for unseen data obtained with a sample length of 250. These initial findings demonstrate the potential of 1D-CNNs in CAD diagnosis using ECG signals and highlight the sample size's role in achieving high accuracy.