Application of artificial intelligence techniques for automated detection of myocardial infarction: a review
Article
Joloudari, Javad Hassannataj, Mojrian, Sanaz, Nodehi, Issa, Mashmool, Amir, Zadegan, Zeynab Kiani, Shirkharkolaie, Sahar Khanjani, Alizadehsani, Roohallah, Tamadon, Tahereh, Khosravi, Samiyeh, Kohnehshari, Mitra Akbari, Hassannatajjeloudari, Edris, Sharifrazi, Danial, Mosavi, Amir, Loh, Hui Wen, Tan, Ru-San and Acharya, U Rajendra. 2022. "Application of artificial intelligence techniques for automated detection of myocardial infarction: a review." Physiological Measurement. 43 (8). https://doi.org/10.1088/1361-6579/ac7fd9
Article Title | Application of artificial intelligence techniques for automated detection of myocardial infarction: a review |
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Article Category | Article |
Authors | Joloudari, Javad Hassannataj, Mojrian, Sanaz, Nodehi, Issa, Mashmool, Amir, Zadegan, Zeynab Kiani, Shirkharkolaie, Sahar Khanjani, Alizadehsani, Roohallah, Tamadon, Tahereh, Khosravi, Samiyeh, Kohnehshari, Mitra Akbari, Hassannatajjeloudari, Edris, Sharifrazi, Danial, Mosavi, Amir, Loh, Hui Wen, Tan, Ru-San and Acharya, U Rajendra |
Journal Title | Physiological Measurement |
Journal Citation | 43 (8) |
Article Number | 08TR01 |
Number of Pages | 22 |
Year | 2022 |
Digital Object Identifier (DOI) | https://doi.org/10.1088/1361-6579/ac7fd9 |
Web Address (URL) | https://iopscience.iop.org/article/10.1088/1361-6579/ac7fd9 |
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. |
Keywords | artificial intelligence; deep learning; machine learning |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Birjand, Iran |
Amol Institute of Higher Education, Iran | |
Mazandaran University of Science and Technology, Iran | |
University of Qom, Iran | |
University of Genoa, Italy | |
Deakin University | |
Bu-Ali Sina University, Iran | |
Maragheh University of Medical Science, Iran | |
Islamic Azad University, Iran | |
Obuda University, Hungary | |
Slovak University of Technology | |
Singapore University of Social Sciences (SUSS), Singapore | |
National Heart Centre, Singapore | |
Duke-NUS Medical Centre, Singapore | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v9v/application-of-artificial-intelligence-techniques-for-automated-detection-of-myocardial-infarction-a-review
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