Application of Artificial Intelligence Techniques for Automated Detection of Myocardial Infarction: a Review

EasyChair Preprint 6048, version 2

Abstract

Myocardial infarction disease (MID) is caused to the rapid progress of undiagnosed coronary artery disease (CAD) that indicates the injury of a heart cell by decreasing the blood flow to the cardiac muscles. MID is the leading cause of death in middle-aged and elderly subjects all over the world. In general, raw Electrocardiogram (ECG) signals are tested for MID identification by clinicians that is exhausting, time-consuming, and expensive. Artificial intelligence-based methods are proposed to handle the problems to diagnose MID on the ECG signals automatically.  Hence, in this survey paper, artificial intelligence-based methods, including machine learning and deep learning, are review for MID diagnosis on the ECG signals. Using the methods demonstrate that the feature extraction and selection of ECG signals required to be handcrafted in the ML methods. In contrast, these tasks are explored automatically in the DL methods. Based on our best knowledge, Deep Convolutional Neural Network (DCNN) methods are highly required methods developed for the early diagnosis of MID on the ECG signals. Most researchers have tended to use DCNN methods, and no studies have surveyed using artificial intelligence methods for MID diagnosis on the ECG signals.

Keyphrases: Deep Convolutional Neural Network, Electrocardiogram, Myocardial Infarction Disease, deep learning, diagnosis, machine learning

@booklet{EasyChair:6048,
  author    = {Javad Hassannataj Joloudari and Sanaz Mojrian and Issa Nodehi and Amir Mashmool and Zeynab Kiani Zadegan and Sahar Khanjani Shirkharkolaie and Tahereh Tamadon and Samiyeh Khosravi and Mitra Akbari and Edris Hassannataj and Roohallah Alizadehsani and Danial Sharifrazi and Amir Mosavi},
  title     = {Application of Artificial Intelligence Techniques for Automated Detection of Myocardial Infarction: a Review},
  howpublished = {EasyChair Preprint 6048},
  year      = {EasyChair, 2022}}