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

Objective. 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 worldwide. 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. Approach. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some 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. Main results. 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. Significance. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.

Automated summary

This study conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and other biophysical signals, including machine learning (ML) and deep learning (DL) models. The review found that deep convolutional neural networks (DCNNs) showed excellent classification performance for MI diagnosis, explaining their prevalence in recent years.