Results. ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F1-score of 100%.
In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model.
Apr 30, 2022 · In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed ...
ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects ... Authors: Amulya Agrawal; Aniket Chauhan; Manu Kumar Shetty ...
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Jun 10, 2024 · ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Computers in Biology and Medicine 146 ...
Jun 14, 2024 · ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Article. Full-text available. Apr 2022 ...
ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Comput. Biol. Med. 2022, 146, 105540. [Google Scholar] ...
ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects · A Novel Deep Learning based Stress Analysis and Detection ...
Nov 18, 2022 · ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Comput. Biol. Med. 2022;146 [PMC free ...
May 18, 2022 · A new paper named " ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects" by Amulya Agrawal, ...