ECG-COVID: an end-to-end deep model based on electrocardiogram for COVID-19 detection
Authors:
- Ahmed S. Sakr,
- Paweł Pławiak,
- Ryszard Tadeusiewicz,
- Joanna Pławiak,
- Mohamed Sakr,
- Mohamed Hammad
Abstract
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.
- Record ID
- CUT77eb5f126c3847ebbfeafa566224e89f
- Publication categories
- ;
- Author
- Journal series
- Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291
- Issue year
- 2023
- Vol
- 619
- Pages
- 324-339
- Other elements of collation
- rys.; schem.; tab.; wykr.; Bibliografia (na s.) - 338-339; Bibliografia (liczba pozycji) - 36; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 619
- Keywords in English
- COVID-19, ECG, CNN, end-to-end, deep learning
- ASJC Classification
- ; ; ; ; ;
- DOI
- DOI:10.1016/j.ins.2022.11.069 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0020025522013585 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 12
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT77eb5f126c3847ebbfeafa566224e89f/
- URN
urn:pkr-prod:CUT77eb5f126c3847ebbfeafa566224e89f
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.