Acta Scientific Computer Sciences

Research Article Volume 4 Issue 6

Covid-19 and Cardiac Disease Classification using ECG Images

Anthome Aalwan Vaz1, Mohammed Imran1, Muruganantham Sadasivam1, Syed Farhan Hassan1, Vigneshwaran Mahendiran1 and Parisa Naraei2*

1AIMT, Lambton College, Toronto, Canada
2Principal Investigator Applied Research Center, Lambton College, Canada

*Corresponding Author: Parisa Naraei, Principal Investigator Applied Research Center, Lambton College, Canada.

Received: April 26, 2022; Published: May 18, 2022


The initial phase of the project has been started with the data collection, where we have collected ECG images and 12 lead ECG waveform values of patients affected with covid 19 with five classifications as Covid 19, abnormal heart rate, myocardial infarction (MI), history of MI and normal ECG. We have done gamma correction for the image dataset in data preprocessing and cleaning, and waveforms have been created from 12 lead ECG values. To increase the available data, we have augmented the created images. We have created test and validation classes for the image dataset to train the model. A sequential convolutional neural network has been built for image classification. We used the batch normalization method from Keras; in the sequential model layer, we used relu and softmax activation layers—Adam optimizer and sparse categorical cross-entropy as the loss function in the sequential model. Training and validation accuracy have been used as metrics to assess the model's performance. Vgg16 model has been trained to compare the results. On completion of model creation, the trained model has been exported, and mobile application integration has been made for the end-user.

Keywords: Covid-19; ECG Image; Arrhythmia; Data Collection; Data Preprocessing; Data Cleaning; Data Augmentation; Model Building; Training; Model Evaluation; Android Application Development; CNN; Custom CNN; Vgg16


  1. S Christodoulidis., et al. "Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis”. in IEEE Journal of Biomedical and Health Informatics 21.1 (2017): 76-84.
  2. Degerli A., et al. “COVID-19 infection map generation and detection from chest X-ray images”. Health Information Science and Systems 1 (2021): 15.
  3. E Kesim., et al. "X-Ray Chest Image Classification by A Small-Sized Convolutional Neural Network”. 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) (2019): 1-5.
  4. Rahman Tawsifur., et al. "Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization”. IEEE Access 8 (2020): 191586-191601.
  5. Altay Guvenir H., et al. UCI Machine Learning Repository Bilkent University, Department of Computer Engineering and Information Science (1998).
  6. Khan Ali Haider., et al. “ECG Images dataset of Cardiac and COVID-19 Patients”. Mendeley Data 1 (2020).
  7. N Tajbakhsh., et al. “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?”. IEEE Transactions on Medical Imaging (2016).
  8. Y Qiblawey., et al. “Detection and severity classification of COVID-19 in CT images using deep learning”. ArXiv Prepr. ArXiv2102.07726 (2021).
  9. C Liu., et al. “TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network”. in Proc. Int. Conf. Image Process. ICIP (2018).
  10. Rahman Tawsifur., et al. “COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network”. Health Information Science and Systems 10 (2022).
  11. MEH Chowdhury., et al. “Can AI Help in Screening Viral and COVID-19 Pneumonia?”. IEEE Access (2020).
  12. Chouhan SK., et al. “A novel transfer learning-based approach for pneumonia detection in chest X-ray images”. Applied Science (2020).
  13. AM Ismael and A Şengür. “Deep learning approaches for COVID-19 detection based on chest X-ray images”. Expert Systems with Applications (2021).
  14. MG Muhammad Uzair Zahid., et al. “Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network”. ArXiv.Org. (2020).
  15. T Rahman., et al. “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images”. Computers in Biology and Medicine 132 (2021): 104319.
  16. Moody B., et al. “MIMIC-III Waveform Database Matched Subset (version 1.0)”. PhysioNet (2020).
  17. Moody B., et al. “MIMIC-III Waveform Database (version 1.0)”. PhysioNet (2020).
  18. Johnson AEW., et al. “MIMIC-III, a freely accessible critical care database”. Scientific Data 3 (2016): 160035.
  19. Goldberger A., et al. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals”. Circulation Online 23 (2000): e215-e220.


Citation: Parisa Naraei., et al. “Covid-19 and Cardiac Disease Classification using ECG Images". Acta Scientific Computer Sciences 4.6 (2022): 35-45.


Copyright: © 2022 Parisa Naraei., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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