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AuthorRahman, Tawsifur
AuthorAkinbi, Alex
AuthorChowdhury, Muhammad E. H.
AuthorRashid, Tarik A.
AuthorŞengür, Abdulkadir
AuthorKhandakar, Amith
AuthorIslam, Khandaker Reajul
AuthorIsmael, Aras M.
Available date2023-04-17T06:57:41Z
Publication Date2022
Publication NameHealth Information Science and Systems
ResourceScopus
URIhttp://dx.doi.org/10.1007/s13755-021-00169-1
URIhttp://hdl.handle.net/10576/41935
AbstractThe reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities. 2022, The Author(s).
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectCardiovascular diseases (CVDs)
Convolutional neural networks
COVID-19
Deep learning
Electrocardiogram (ECG)
TitleCOV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
TypeArticle
Issue Number1
Volume Number10
dc.accessType Open Access


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