Szczegóły publikacji

Opis bibliograficzny

Autorzy (6)

• Sakr Ahmed S.
• Pławiak Paweł
• AGHTadeusiewicz Ryszard
• Pławiak Joanna
• Sakr Mohamed
• Hammad Mohamed

Słowa kluczowe

Dane bibliometryczne

ID BaDAP	143794
Data dodania do BaDAP	2022-11-22
Tekst źródłowy	URL
DOI	10.1016/j.ins.2022.11.069
Rok publikacji	2023
Typ publikacji	artykuł w czasopiśmie
Otwarty dostęp
Czasopismo/seria	Information Sciences

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.

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