Log In Sign Up

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

by   Tawsifur Rahman, et al.

The 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 consists 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: two-class classification (Normal vs COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and finally, 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 five-class classification, InceptionV3 outperforms others with an accuracy of 97.83 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.


page 5

page 6

page 16

page 19


Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks

The COVID-19 pandemic has exposed the vulnerability of healthcare servic...

Can AI help in screening Viral and COVID-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already ...

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection

In this article, we propose a novel ECG classification framework for atr...

COVID-19 Electrocardiograms Classification using CNN Models

With the periodic rise and fall of COVID-19 and numerous countries being...

COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks

In the research, we developed a computer vision solution to support diag...

CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images

Early detection of COVID-19 is vital to control its spread. Deep learnin...

Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests

Rapid and accurate detection of community outbreaks is critical to addre...