Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders. (1st May 2023)

• Record Type: Journal Article
• Authors: Chaitanya, M Krishna; Sharma, Lakhan Dev; Rahul, Jagdeep; Sharma, Diksha; Roy, Amarjit
• Is Part Of: Biomedical physics & engineering express. Volume 9:Number 3(2023)
• Publication Date: 2023-05-01
• Subjects: coronavirus disease (COVID-19) -- SARS-CoV-2 -- ECG image -- deep learning -- VGG -- AlexNet; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/acbd53
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.