A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Abstract

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin.