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

Research output: Contribution to journalArticle

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Classification of myocardial infarction with multi-lead ECG signals and deep CNN. / Baloglu, Ulas Baran; Talo, Muhammed; Yildirim, Ozal; Tan, Ru San; Acharya, U. Rajendra.

In: Pattern Recognition Letters, Vol. 122, 01.05.2019, p. 23-30.

Research output: Contribution to journalArticle

Harvard

Baloglu, UB, Talo, M, Yildirim, O, Tan, RS & Acharya, UR 2019, 'Classification of myocardial infarction with multi-lead ECG signals and deep CNN', Pattern Recognition Letters, vol. 122, pp. 23-30. https://doi.org/10.1016/j.patrec.2019.02.016

APA

Baloglu, U. B., Talo, M., Yildirim, O., Tan, R. S., & Acharya, U. R. (2019). Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters, 122, 23-30. https://doi.org/10.1016/j.patrec.2019.02.016

Vancouver

Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters. 2019 May 1;122:23-30. https://doi.org/10.1016/j.patrec.2019.02.016

Author

Baloglu, Ulas Baran ; Talo, Muhammed ; Yildirim, Ozal ; Tan, Ru San ; Acharya, U. Rajendra. / Classification of myocardial infarction with multi-lead ECG signals and deep CNN. In: Pattern Recognition Letters. 2019 ; Vol. 122. pp. 23-30.

Bibtex

@article{54e33cef096d45d9b304cfbe04882bf4,
title = "Classification of myocardial infarction with multi-lead ECG signals and deep CNN",
abstract = "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.",
keywords = "Biomedical signal, Deep learning, Multi-lead ECG, Myocardial infarction",
author = "Baloglu, {Ulas Baran} and Muhammed Talo and Ozal Yildirim and Tan, {Ru San} and Acharya, {U. Rajendra}",
year = "2019",
month = "5",
day = "1",
doi = "10.1016/j.patrec.2019.02.016",
language = "English",
volume = "122",
pages = "23--30",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "North-Holland Publishing Company",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Classification of myocardial infarction with multi-lead ECG signals and deep CNN

AU - Baloglu, Ulas Baran

AU - Talo, Muhammed

AU - Yildirim, Ozal

AU - Tan, Ru San

AU - Acharya, U. Rajendra

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - Biomedical signal

KW - Deep learning

KW - Multi-lead ECG

KW - Myocardial infarction

UR - http://www.scopus.com/inward/record.url?scp=85061592365&partnerID=8YFLogxK

U2 - 10.1016/j.patrec.2019.02.016

DO - 10.1016/j.patrec.2019.02.016

M3 - Article

VL - 122

SP - 23

EP - 30

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

ER -