Multi-Modal Deep Learning for Myocardial Infarction Detection Using ECG Signals and Images

Abstract

Heart attacks, formally referred to as myocardial infarction (MI), are a major cause of morbidity and death globally. Timely intervention and better patient outcomes depend on early and precise MI identification. Traditional methods for diagnosing MI primarily rely on clinical examinations, Electrocardiogram (ECG), and imaging techniques. However, these methods often face challenges in terms of accuracy, sensitivity, and timely interpretation. This study explores a multi-modal Deep Learning (DL) model for detecting MI using both ECG signal and image data. The model integrates a Convolutional Neural Network (CNN) for processing ECG images, Long Short-Term Memory (LSTM) networks for analyzing ECG signals, and an Attention-based feature fusion mechanism to combine features from both modalities. The model was evaluated in two configurations: training on the PTB-XL dataset with testing on the Mendeley ECG image dataset, and training on the Mendeley ECG image dataset with testing on the PTB-XL dataset. The results show that the hypertuned multi-modal model consistently outperforms the baseline, with improvements in F1-score, recall, precision, and accuracy. In the PTB-XL dataset training and Mendeley ECG image dataset testing setup, the hypertuned model achieved an accuracy of 0.982, while in the Mendeley image dataset training and PTB-XL dataset testing setup, it reached 0.9638. These findings demonstrate promising avenues for advancing automated cardiovascular diagnostics combining the strengths of both image and signal-based analysis.