COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model
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Tarih
2022
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Erişim Hakkı
info:eu-repo/semantics/closedAccess
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.
Açıklama
Anahtar Kelimeler
Cardiovascular diseases diagnosis, Convolutional neural networks, COVID-19 diagnosis, Deep learning, Electrocardiography, Machine learning
Kaynak
Physical and Engineering Sciences in Medicine (
WoS Q DeÄŸeri
Scopus Q DeÄŸeri
Cilt
Sayı
45
