Classification of myocardial infarction with multi-lead ECG signals and deep CNN

被引:299
作者
Baloglu, Ulas Baran [1 ]
Talo, Muhammed [1 ]
Yildirim, Ozal [1 ]
Tan, Ru San [2 ,3 ]
Acharya, U. Rajendra [4 ,5 ,6 ]
机构
[1] Munzur Univ, Dept Comp Engn, TR-62000 Tunceli, Turkey
[2] Natl Heart Ctr Singapore, Dept Cardiol, Singapore, Singapore
[3] Duke NUS Med Sch, Singapore, Singapore
[4] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore, Singapore
[5] Singapore Sch Social Sci, Sch Sci & Technol, Dept Biomed Engn, Singapore, Singapore
[6] Taylors Univ, Fac Hlth & Med Sci, Sch Med, Subang Jaya 47500, Malaysia
关键词
Myocardial infarction; Deep learning; Multi-lead ECG; Biomedical signal;
D O I
10.1016/j.patrec.2019.02.016
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Myocardial infarction (MI), commonly known as heart attack, causes irreversible damage to heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to avoid death. Blood tests and electrocardiogram (ECG) signals are used to diagnose acute MI. However, for an increase in blood enzyme values, a certain time must pass after the attack. This time lag may delay MI diagnosis. Hence, ECG diagnosis is still very important. Manual ECG interpretation requires expertise and is prone to inter-observer variability. Therefore, computer aided diagnosis may be useful in automatic detection of MI on ECG. In this study, a deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI is proposed. For this purpose, the most commonly used technique, convolutional neural network (CNN) is used. Our trained CNN model with the proposed architecture yielded impressive accuracy and sensitivity performance over 99.00% for MI diagnosis on all ECG lead signals. Thus, the proposed model has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:23 / 30
页数:8
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