ECGConVT: A Hybrid CNN and Vision Transformer Model for Enhanced 12-Lead ECG Images Classification

Cardiovascular diseases, which are currently the major causes of death globally, can be largely ameliorated through early detection and categorization. Electrocardiogram (ECG) tests have emerged as widely employed, low-cost and non-invasive procedures for evaluating electrical activities of the heart and diagnosing cardiovascular ailments. In this research, by using deep learning techniques to detect specific cardiac disorders like cardiac myocardial infarction(MI), arrhythmia, past history of myocardial infarction(PMI) and normal ECG patterns on a dataset containing patients with heart disease. We propose ECGConVT framework that combines Convolutional Neural Network (CNN) module for extracting local features, and Vision Transformer (ViT) module for capturing global features. The final classification is achieved by combining the two using Multilayer Perceptron (MLP) module. The experimental results indicate promise of ECGConVT in ECG image classification where it outperforms other approaches showing an average accuracy of 98.5%, F1-score: 98.7%, Recall: 98.8% and Precision: 98.5%. In order to meet the practical needs of clinical applications, we implemented a lightweight post-processing step to reduce the size of the model.