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

Version 2 2024-06-02, 14:32
Version 1 2024-04-17, 06:54
journal contribution
posted on 2024-06-02, 14:32 authored by J Hassannataj Joloudari, S Mojrian, I Nodehi, A Mashmool, Z Kiani Zadegan, S Khanjani Shirkharkolaie, Roohallah AlizadehsaniRoohallah Alizadehsani, T Tamadon, S Khosravi, M Akbari Kohnehshari, E Hassannatajjeloudari, D Sharifrazi, A Mosavi, HW Loh, RS Tan, UR Acharya
Abstract 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.

History

Journal

Physiological Measurement

Volume

43

Article number

08TR01

Pagination

1-22

Location

Bristol, Eng.

ISSN

0967-3334

eISSN

1361-6579

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

8

Publisher

IOP Publishing