ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection

TytułECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection
Publication TypeJournal Article
Rok publikacji2023
AutorzySakr AS, Plawiak P, Tadeusiewicz R, Pławiak J, Sakr M, Hammad M
JournalInformation Sciences
Słowa kluczoweCNN, COVID-19, Deep learning, ECG, End-to-end

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.


Historia zmian

Data aktualizacji: 15/12/2023 - 15:26; autor zmian: Paweł Pławiak (