no code implementations • 10 Aug 2022 • Thao Nguyen, Hieu H. Pham, Huy Khiem Le, Anh Tu Nguyen, Ngoc Tien Thanh, Cuong Do
Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28. 11 ms. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately, with classification accuracies of 98. 42%, 95. 63%, and 98. 50% for classifying COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, and COVID-19 vs. other classes, respectively.
