Enhancing myocardial infarction detection with vectorcardiography: fusion-based comparative analysis of machine learning methods.

ABSTRACT:

Background

Early detection and diagnosis of myocardial infarction (MI) help physicians save lives through timely treatment. Vectorcardiography (VCG) is an alternative to the 12-lead electrocardiography, providing not only characteristic changes in cardiac electrical activity in MI patients but also unique spatial information often overlooked by traditional methods. Despite its potential, comprehensive comparative studies applying machine learning (ML) techniques specifically to VCG data remain limited.

Methods

This study proposes a novel VCG processing methodology using a comparative analysis of machine learning-based algorithms for the automated detection of MI patients from VCG recordings, utilizing extracted domain knowledge VCG features that monitor morphological changes

SUBMITTER: Vondrak J 

PROVIDER: S-EPMC12813126 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature