Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

TL;DR

This review surveys AI techniques, especially deep learning, for automated myocardial infarction detection using ECG and biophysical signals, highlighting the superior performance of deep convolutional neural networks.

Contribution

It provides the first comprehensive survey of AI methods for MI diagnosis, comparing traditional machine learning and deep learning approaches.

Findings

Deep convolutional neural networks achieve high classification accuracy.

Deep learning models automate feature extraction and signal analysis.

AI techniques improve MI detection speed and accuracy.

Abstract

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.