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dc.contributor.authorIrmak, Emrah
dc.date.accessioned2024-10-24T11:18:29Z
dc.date.available2024-10-24T11:18:29Z
dc.date.issued2022en_US
dc.identifier.urihttps://pubmed.ncbi.nlm.nih.gov/35020175/
dc.identifier.urihttps://hdl.handle.net/20.500.12868/2512
dc.description.abstractClinical 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.en_US
dc.language.isoengen_US
dc.relation.isversionofhttps://doi.org/10.1007/s13246-022-01102-wen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCardiovascular diseases diagnosisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectCOVID-19 diagnosisen_US
dc.subjectDeep learningen_US
dc.subjectElectrocardiographyen_US
dc.subjectMachine learningen_US
dc.titleCOVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network modelen_US
dc.typearticleen_US
dc.contributor.departmentALKÜ, Fakülteler, Rafet Kayış Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.issue45en_US
dc.identifier.startpage167en_US
dc.identifier.endpage179en_US
dc.relation.journalPhysical and Engineering Sciences in Medicine (en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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