AttentionCovidNet: Efficient ECG-based diagnosis of COVID-19

The novel coronavirus caused a worldwide pandemic. Rapid detection of COVID-19 can help reduce the spread of the novel coronavirus as well as the burden on healthcare systems worldwide. The current method of detecting COVID-19 suffers from low sensitivity, with estimates of 50%–70% in clinical settings. Therefore, in this study, we propose AttentionCovidNet, an efficient model for the detection of COVID-19 based on a channel attention convolutional neural network for electrocardiograms. The electrocardiogram is a non-invasive test, and so can be more easily obtained from a patient. We show that the proposed model achieves state-of-the-art results compared to recent models in the field, achieving metrics of 0.993, 0.997, 0.993, and 0.995 for accuracy, precision, recall, and F1 score, respectively. These results indicate both the promise of the proposed model as an alternative test for COVID-19, as well as the potential of ECG data as a diagnostic tool for COVID-19.