COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model
Özet
Clinical reports show that COVID-19 disease has impacts on the cardiovascular system in addition to the respiratory system.
Available COVID-19 diagnostic methods have been shown to have limitations. In addition to current diagnostic methods such
as low-sensitivity standard RT-PCR tests and expensive medical imaging devices, the development of alternative methods
for the diagnosis of COVID-19 disease would be benefcial for control of the COVID-19 pandemic. Further, it is important
to quickly and accurately detect abnormalities caused by COVID-19 on the cardiovascular system via ECG. In this study,
the diagnosis of COVID-19 disease is proposed using a novel deep Convolutional Neural Network model by using only ECG
trace images created from ECG signals of COVID-19 infected patients based on the abnormalities caused by the COVID-19
virus on the cardiovascular system. An overall classifcation accuracy of 98.57%, 93.20%, 96.74% and AUC value of 0.9966,
0.9771, 0.9905 is achieved for COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, COVID-19 vs. Myocardial
Infarction binary classifcation tasks, respectively. In addition, an overall classifcation accuracy of 86.55% and 83.05%
is achieved for COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction and Normal vs. COVID-19 vs. Abnormal
Heartbeats vs. Myocardial Infarction multi-classifcation tasks. This study is believed to have great potential to speed up the
diagnosis and treatment of COVID-19 patients, saving clinicians time and facilitating the control of the pandemic.
