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Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods

Fatma Latifoğlu1✉,
Aigul Zhusupova2,
Merve İnce3,
Nermin Aybike Ertürk4,
Berat Özdet5,
Semra İçer6,
Ayşegül Güven7,
Ömer Levent Avşaroğulları8,
Şaban Keleşoğlu9,
Nihat Kalay10
1a:1:{s:5:"en_US";s:18:"Erciyes University";}
2Erciyes University
3Erciyes University
4Erciyes University
5Erciyes University
6Erciyes University
7Erciyes University
8Erciyes University
9Erciyes University
10Erciyes University
Published:March 31, 2024
DOI: 10.56038/ejrnd.v4i1.421
Vol. 4, No. 1

Abstract

In contemporary medicine, the development of computer-aided diagnostic systems using Electrocardiography (ECG) signals has gained significance for the diagnosis of heart diseases. Myocardial infarction (MI) is recognized as the condition where blood flow to the heart muscle is obstructed due to blockages in coronary vessels. In this study, four deep learning approaches were employed to automatically identify different MI conditions (STEMI, NSTEMI, USAP) using images generated from 12-lead ECG signals. The utilized architectures include deep neural networks such as Visual Geometry Group-16 (VGG-16), AlexNet, Residual Neural Network (ResNet), SqueezeNet and an ensemble model composed of these networks. With the proposed method, classification was performed based on 10-second grayscale images of 12-lead ECG signals for HC-STEMI, HC-NSTEMI, HC-USAP, and NSTEMI-STEMI conditions. According to the obtained results, the HC-STEMI group achieved the highest performance with a cross-validated 0.8237 F1 score using the AlexNet architecture.

Among the novel contributions of this study is the image-based ECG classification method that can be more easily adapted to clinical applications and the analysis of the potential use of detecting different MI conditions in clinical practices. In conclusion, this study sheds light on future research by demonstrating the significant potential of using multi-channel ECG signals in image format for MI diagnosis, paving the way for advancements in this field.

Keywords
Myocardial infarction (MI)Electrocardiography (ECG)AlexNetResNetSqueezeNetVGG16Majority voting

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Cite This Article
Latifoğlu, F., Zhusupova, A., İnce, M., Ertürk, N. A., Özdet, B., İçer, S., Güven, A., Avşaroğulları, Ö. L., Keleşoğlu, Ş., Kalay, N. (2024). Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods. *The European Journal of Research and Development*, 4(1), 42-54. https://doi.org/10.56038/ejrnd.v4i1.421

Bibliographic Info

JournalThe European Journal of Research and Development
Volume4
Issue1
Pages42–54
PublishedMarch 31, 2024
eISSN2822-2296