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.
Source: Physiological Measurement - Category: Physiology Authors: Javad Hassannataj Joloudari, Sanaz Mojrian, Issa Nodehi, Amir Mashmool, Zeynab Kiani Zadegan, Sahar Khanjani Shirkharkolaie, Roohallah Alizadehsani, Tahereh Tamadon, Samiyeh Khosravi, Mitra Akbari Kohnehshari, Edris Hassannatajjeloudari, Danial Sharifrazi Source Type: research
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