Enhanced ECG Signal features transformation to RGB matrix imaging for advanced deep learning classification of myocardial infarction and cardiac arrhythmia

Identifying and accurately classifying cardiac abnormalities, including myocardial infarction (MI) and cardiac arrhythmia (CA), remains a significant challenge in the field of cardiology, largely due to the limitations inherent in traditional ECG signal analysis techniques. This paper presents an innovative method aimed at addressing this challenge. By implementing a novel transformation technique, we map temporal, frequency-based, statistical, and spatial features of ECG signals onto the R, G, and B channels of an RGB image. This conversion process results in a feature-rich representation of the ECG signal, significantly enhancing its clinical relevance and thus maximizing classification accuracy. Utilizing an adaptive RGB-ResNet inception architecture, our approach achieves remarkable average accuracies of 99.25% for myocardial infarction and 99.21% for cardiac arrhythmia. These figures underscore the robustness of our method and highlight its significant potential to advance cardiology diagnostics through the application of advanced image analysis techniques.