Neural Network Algorithm Based on LVQ for Myocardial Infarction Detection and Localization Using Multi-Lead ECG Data

• Published in: Computers, materials & continua Vol. 82; no. 3; pp. 5257 - 5284
• Main Authors: Ozhikenov, Kassymbek, Alimbayeva, Zhadyra, Alimbayev, Chingiz, Ozhikenova, Aiman, Altay, Yeldos
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2025
• Subjects: Accuracy; Algorithms; Damage; Electrocardiography; Heart attacks; Learning vector quantization networks; Localization; Machine learning; Myocardial infarction; Myocardium; Neural networks
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2025.061508

Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution of each ECG lead in the 12-channel system, which obtained an accuracy of 87% in localizing damaged myocardium. The developed model was tested on verified data from the PTB database, including 445 ECG recordings from both healthy individuals and MI-diagnosed patients. The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients, serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage. A comprehensive comparison was performed, including CNN, SVM, and Logistic Regression, to evaluate the proposed LVQ model. The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency, making it suitable for resource-constrained environments. This study also applies a carefully designed data pre-processing flow, including class balancing and noise removal, which improves the reliability and reproducibility of the results. These aspects highlight the potential application of the LVQ model in cardiac diagnostics, opening up prospects for its use along with more complex neural network architectures.