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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)23-30
Number of pages8
JournalPattern Recognition Letters
Early online date12 Feb 2019
DateAccepted/In press - 1 Feb 2019
DateE-pub ahead of print - 12 Feb 2019
DatePublished (current) - 1 May 2019


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.

    Research areas

  • Biomedical signal, Deep learning, Multi-lead ECG, Myocardial infarction


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