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

Journal: Physiological measurement
Published Date: Aug 3, 2022

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 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.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.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 some other biophysical signals.

Authors

  • Javad Hassannataj Joloudari
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Sanaz Mojrian
    Department of Information Technology Engineering, Mazandaran University of Science and Technology, Babol, Iran.
  • Issa Nodehi
    Department of Computer Engineering, University of Qom, Qom, Iran.
  • Amir Mashmool
    Dipartimento di Informatica, Bioingegneria, Robotica eIngegneria dei Sistemi (DIBRIS), Università di Genova, Genova, Italy.
  • Zeynab Kiani Zadegan
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Sahar Khanjani Shirkharkolaie
    Department of Information Technology Engineering, Mazandaran University of Science and Technology, Babol, Iran.
  • Roohallah Alizadehsani
    Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.
  • Tahereh Tamadon
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Samiyeh Khosravi
    Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
  • Mitra Akbari Kohnehshari
    Computer Engineering Department, Engineering Faculty, Bu-Ali Sina University, Hamedan, Iran.
  • Edris Hassannatajjeloudari
    Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran.
  • Danial Sharifrazi
    Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
  • Amir Mosavi
    Faculty of Informatics, Technische Universität Dresden, Dresden, Germany.
  • Hui Wen Loh
    School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.
  • Ru-San Tan
    National Heart Centre Singapore, Singapore, Singapore.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.

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