COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model
| dc.contributor.author | Irmak, Emrah | |
| dc.date.accessioned | 2024-10-24T11:18:29Z | |
| dc.date.available | 2024-10-24T11:18:29Z | |
| dc.date.issued | 2022 | |
| dc.department | ALKÜ, Fakülteler, Rafet Kayış Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | |
| dc.description.abstract | 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. | |
| dc.identifier.doi | https://doi.org/10.1007/s13246-022-01102-w | |
| dc.identifier.endpage | 179 | en_US |
| dc.identifier.issue | 45 | en_US |
| dc.identifier.startpage | 167 | en_US |
| dc.identifier.uri | https://pubmed.ncbi.nlm.nih.gov/35020175/ | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12868/2512 | |
| dc.indekslendigikaynak | PubMed | |
| dc.language.iso | en | |
| dc.relation.ispartof | Physical and Engineering Sciences in Medicine ( | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Cardiovascular diseases diagnosis | |
| dc.subject | Convolutional neural networks | |
| dc.subject | COVID-19 diagnosis | |
| dc.subject | Deep learning | |
| dc.subject | Electrocardiography | |
| dc.subject | Machine learning | |
| dc.title | COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model | |
| dc.type | Article |
