Development of an Efficient 1D-CNN Model for Myocardial Infarction Classification Using 12-Lead ECG Signals
DOI:
10.29303/jppipa.v11i3.10238Published:
2025-03-31Issue:
Vol. 11 No. 3 (2025): MarchKeywords:
12 lead, CNN, ECG, Myocardial infarction, PTB-XLResearch Articles
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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.
References
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
Banerjee, S., & Mitra, M. (2013). Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification. IEEE Transactions on Instrumentation and Measurement, 63(2), 326–333. https://doi.org/10.1109/TIM.2013.2279001
Brady, W. J., Hwang, V., Sullivan, R., Chang, N., Beagle, C., Carter, C. T., Martin, M. L., & Aufderheide, T. P. (2000). A Comparison of 12-and 15-Lead ECGs in ED Chest Pain Patients: Impact on Diagnosis, Therapy, and Disposition. The American Journal of Emergency Medicine, 18(3), 239–243. https://doi.org/10.1016/S0735-6757(00)90112-8
Chakraborty, A., Chatterjee, S., Majumder, K., Shaw, R. N., & Ghosh, A. (2022). A Comparative Study of Myocardial Infarction Detection from ECG Data Using Machine Learning. Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2021, 257–267. https://doi.org/10.1007/978-981-16-2164-2_21
Darmawahyuni, A., & Nurmaini, S. (2019). Deep Learning with Long Short-Term Memory for Enhancement Myocardial Infarction Classification. 2019 6th International Conference on Instrumentation, Control, and Automation (ICA), 19–23. https://doi.org/10.1109/ICA.2019.8916683
Fariha, M. A. Z., Ikeura, R., Hayakawa, S., & Tsutsumi, S. (2020). Analysis of Pan-Tompkins Algorithm Performance with Noisy ECG Signals. Journal of Physics: Conference Series, 1532(1), 12022. https://doi.org/10.1088/1742-6596/1532/1/012022
Hainaut, C., & Gade, W. (2003). The Emerging Roles of BNP and Accelerated Cardiac Protocols in Emergency Laboratory Medicine. American Society for Clinical Laboratory Science, 16(3), 166–179. https://doi.org/10.29074/ascls.16.3.166
Hasbullah, S., Zahid, M. S. M., & Mandala, S. (2023). Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network. BioMedInformatics, 3(2), 478–492. https://doi.org/10.3390/biomedinformatics3020033
Huanhuan, M., & Yue, Z. (2014). Classification of Electrocardiogram Signals with Deep Belief Networks. 2014 IEEE 17th International Conference on Computational Science and Engineering, 7–12. https://doi.org/10.1109/CSE.2014.36
Mathews, S. M., Kambhamettu, C., & Barner, K. E. (2018). A Novel Application of Deep Learning for Single-Lead ECG Classification. Computers in Biology and Medicine, 99, 53–62. https://doi.org/10.1016/j.compbiomed.2018.05.013
Meek, S., & Morris, F. (2002). Introduction. I—Leads, Rate, Rhythm, and Cardiac Axis. BMJ, 324(7334), 415–418. https://doi.org/10.1136/bmj.324.7334.415
Mezgec, S., & Seljak, B. K. (2017). NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment. Nutrients, 9(7), 657. https://doi.org/10.3390/nu9070657
Mirza, A. H., Nurmaini, S., & Partan, R. U. (2022). Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network. Applied Sciences, 12(11), 5603. https://doi.org/10.3390/app12115603
Mirza, A. H., Nurmaini, S., Partan, R. U., & Herdiansyah, M. I. (2023). Myorcadial Infarction Classification with 15 Lead Electrocardiogram (ECG) Signal using Hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BILSTM). 2023 Eighth International Conference on Informatics and Computing (ICIC), 1–6. https://doi.org/10.1109/ICIC60109.2023.10381905
Muraki, R., Teramoto, A., Sugimoto, K., Sugimoto, K., Yamada, A., & Watanabe, E. (2022). Automated Detection Scheme for Acute Myocardial Infarction Using Convolutional Neural Network and Long Short-Term Memory. PLoS One, 17(2), e0264002. https://doi.org/10.1371/journal.pone.0264002
National Institute of Health Research and Development. (2018). National Report on Basic Health Research, RISKESDAS 2018. Jakarta: Ministry of Health.
Nurmaini, S., Darmawahyuni, A., Mukti, A. N. S., Rachmatullah, M. N., Firdaus, F., & Tutuko, B. (2020a). Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification. Electronics, 9(1), 135. https://doi.org/10.3390/electronics9010135
Nurmaini, S., Partan, R. U., & Rachmatullah, M. N. (2019). Deep Classifier on the Electrocardiogram Interpretation System. Journal of Physics: Conference Series, 1246(1), 12030. https://doi.org/10.1088/1742-6596/1246/1/012030
Nurmaini, S., Tondas, A. E., Darmawahyuni, A., Rachmatullah, M. N., Partan, R. U., Firdaus, F., Tutuko, B., Pratiwi, F., Juliano, A. H., & Khoirani, R. (2020b). Robust Detection of Atrial Fibrillation from Short-Term Electrocardiogram Using Convolutional Neural Networks. Future Generation Computer Systems, 113, 304–317. https://doi.org/10.1016/j.future.2020.07.021
Partan, R. (2018). Cardiac Arrhythmias Classification Using Deep Neural Networks and Principle Component Analysis Algorithm. Int. J. Advance Soft Compu. Appl., 10(2).
Rawi, A. A., Albashir, M. K., & Ahmed, A. M. (2022). Classification and Detection of ECG Arrhythmia and Myocardial Infarction Using Deep Learning: A Review. Webology, 19(1), 1151–1170. https://doi.org/10.14704/WEB/V19I1/WEB19078
Ricardo, R. A., Bassani, R. A., & Bassani, J. W. M. (2009). A Simple Laboratory Method for Teaching How Electrocardiogram is Generated. World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany: Vol. 25/12 General Subjects, 385–387. https://doi.org/10.1007/978-3-642-03893-8_111
Sokhanvar, S. (2006). Evaluation of ST Segment Deviation on 15-Lead ECG in Patients with Acute Coronary Syndrome (ACS). Journal of Advances in Medical and Biomedical Research, 14(56), 32–39.
Strong, A., Sharma, G., Tu, C., Aminian, A., Rodriguez, J., & Kroh, M. D. (2017). A Population-Based Study of Early Postoperative Outcomes in Patients Undergoing Bariatric Surgery with Congestive Heart Failure. Journal of the American College of Surgeons, 225(4), e55. https://doi.org/10.1016/j.jamcollsurg.2017.07.666
Übeyli, E. D. (2010). Recurrent Neural Networks Employing Lyapunov Exponents for Analysis of ECG Signals. Expert Systems with Applications, 37(2), 1192–1199. https://doi.org/10.1016/j.eswa.2009.06.022
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and Composing Robust Features with Denoising Autoencoders. Proceedings of the 25th International Conference on Machine Learning, 1096–1103. https://doi.org/10.1145/1390156.1390294
Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., & Schaeffter, T. (2020). PTB-XL, a Large Publicly Available Electrocardiography Dataset. Scientific Data, 7(1), 1–15. https://doi.org/10.1038/s41597-020-0495-6
Wang, J., Ye, Y., Pan, X., & Gao, X. (2015). Parallel-Type Fractional Zero-Phase Filtering for ECG Signal Denoising. Biomedical Signal Processing and Control, 18, 36–41. https://doi.org/10.1016/j.bspc.2014.10.012
Wang, R., Xu, J., & Han, T. X. (2019). Object Instance Detection with Pruned Alexnet and Extended Training Data. Signal Processing: Image Communication, 70, 145–156. https://doi.org/10.1016/j.image.2018.09.013
Xiong, P., Lee, S. M.-Y., & Chan, G. (2022). Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Frontiers in Cardiovascular Medicine, 9, 860032. https://doi.org/10.3389/fcvm.2022.860032
Zhang, J., Lin, F., Xiong, P., Du, H., Zhang, H., Liu, M., Hou, Z., & Liu, X. (2019). Automated Detection and Localization of Myocardial Infarction with Staked Sparse Autoencoder and Treebagger. IEEE Access, 7, 70634–70642. https://doi.org/10.1109/ACCESS.2019.2919068
Zimetbaum, P. J., & Josephson, M. E. (2003). Use of the Electrocardiogram in Acute Myocardial Infarction. New England Journal of Medicine, 348(10), 933–940. https://doi.org/10.1056/NEJMra022700
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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