Development of an Efficient 1D-CNN Model for Myocardial Infarction Classification Using 12-Lead ECG Signals

Authors

Ahmad Haidar Mirza , R.M. Nasrul Halim , Muhammad Jidan Hasbiallah

DOI:

10.29303/jppipa.v11i3.10238

Published:

2025-03-31

Issue:

Vol. 11 No. 3 (2025): March

Keywords:

12 lead, CNN, ECG, Myocardial infarction, PTB-XL

Research Articles

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Mirza, A. H., Halim, R. N., & Hasbiallah, M. J. (2025). Development of an Efficient 1D-CNN Model for Myocardial Infarction Classification Using 12-Lead ECG Signals. Jurnal Penelitian Pendidikan IPA, 11(3), 1167–1182. https://doi.org/10.29303/jppipa.v11i3.10238
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Abstract

Myocardial Infarction (MI) is a leading cause of global mortality, necessitating efficient diagnostic methods. This study develops a simplified one-dimensional Convolutional Neural Network (1D-CNN) model for classifying MI using 12-lead ECG signals from the PTB-XL dataset. The research focuses on reducing computational complexity by limiting convolutional layers while maintaining high accuracy. The proposed model processes ECG signals of varying lengths (600–1000 samples), identifying 700 samples as optimal, achieving an average accuracy of 96.18%, sensitivity of 82.84%, specificity of 97.63%, precision of 84.13%, and an F1-score of 82.68%. Leads V5 and V6 demonstrate superior performance in detecting MI, while other leads, such as I and AVL, require further optimization. By combining precise signal segmentation and an efficient CNN architecture, this model minimizes computational load without compromising performance, making it a strong candidate for real-time clinical applications. The findings highlight the importance of signal length optimization and simplified architecture in enhancing early MI detection.

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Author Biographies

Ahmad Haidar Mirza, Universitas Bina Darma

R.M. Nasrul Halim, Universitas Bina Darma

Muhammad Jidan Hasbiallah, Universitas Sriwijaya

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Copyright (c) 2025 Ahmad Haidar Mirza, R.M. Nasrul Halim, Muhammad Jidan Hasbiallah

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