  The COVID-19 pandemic has exposed the vulnerability of healthcare services
worldwide, raising the need to develop novel tools to provide rapid and
cost-effective screening and diagnosis. Clinical reports indicated that
COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may
serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG
signals to detect COVID-19 automatically. We propose a novel method to extract
ECG signals from ECG paper records, which are then fed into a one-dimensional
convolution neural network (1D-CNN) to learn and diagnose the disease. To
evaluate the quality of digitized signals, R peaks in the paper-based ECG
images are labeled. Afterward, RR intervals calculated from each image are
compared to RR intervals of the corresponding digitized signal. 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. Furthermore, the proposed method also achieves
a high-level of performance for the multi-classification task. Our findings
indicate that a deep learning system trained on digitized ECG signals can serve
as a potential tool for diagnosing COVID-19.
