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• W4309293781 startingPage "324" @default.
• W4309293781 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." @default.
• W4309293781 created "2022-11-25" @default.
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• W4309293781 date "2023-01-01" @default.
• W4309293781 modified "2023-10-17" @default.
• W4309293781 title "ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection" @default.
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• W4309293781 doi "https://doi.org/10.1016/j.ins.2022.11.069" @default.
• W4309293781 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36415325" @default.
• W4309293781 hasPublicationYear "2023" @default.
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