From 9015d17c076a3be3c12bae748031981f692b4e22 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mehmet=20Akif=20=C3=96zdemir?= Date: Sun, 30 May 2021 16:30:43 +0300 Subject: [PATCH] Update README.md --- README.md | 68 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 66 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 711a08a4..2f10e53a 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,67 @@ -# COVID-19-ECG-Classification +# Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning -Content and source codes will be added as soon as possible. +**Ozdemir, M. A. et al. (2021).** [Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning](https://rdcu.be/clAU0), BMC Medical Informatics and Decision Making. + + +![Figure 1](https://user-images.githubusercontent.com/15153217/120105662-983ca880-c162-11eb-9bd8-1389b8750dd8.png) + + +**Abstract** +
+**Background**

Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.

+ +**Methods** +

A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19.

+ +**Results** +

Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach.

+ +**Conclusion** +

Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals.

+ +**Keywords:** COVID-19, ECG, Paper-based ECG, GLCM, Hexaxial mapping, Deep learning, Convolutional neural network, Diagnosis + +## Content + + + +## DOI + +https://doi.org/10.1186/s12911-021-01521-x + +## Web Site + +https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01521-x + +## Citation + +Citation is now available. Please cite us by following; + +Ozdemir, M.A., Ozdemir, G.D. & Guren, O. Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning. BMC Med Inform Decis Mak 21, 170 (2021). https://doi.org/10.1186/s12911-021-01521-x + +``` +@article{ozdemir2021covidECG, + title={Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning}, + author={Ozdemir, Mehmet Akif and Ozdemir, Gizem Dilara and Guren, Onan}, + journal={BMC Medical Informatics and Decision Making}, + volume={21}, + number={1}, + pages={1--20}, + year={2021}, + doi={10.1186/s12911-021-01521-x}, + url={https://doi.org/10.1186/s12911-021-01521-x}, + publisher={BioMed Central} +} +``` +Cite from [Endnote](https://citation-needed.springer.com/v2/references/10.1186/s12911-021-01521-x?format=refman&flavour=citation) +Cite from [BiomedCentral](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01521-x#article-info) or Cite from [Google Scholar](https://scholar.google.com/scholar?hl=tr&as_sdt=0%2C5&q=Classification+of+COVID-19+electrocardiograms+by+using+hexaxial+feature+mapping+and+deep+learning&btnG=) + +## Additional Information +[Peer Review History](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01521-x/peer-review) + +[Altmetric](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01521-x/metrics) + +[PubMed ID:34034715](https://pubmed.ncbi.nlm.nih.gov/34034715/) + +## Contact +If you need any help, feel free to start an issue (preferred because other people can benefit ) or send me an email: [makif.ozdemir@ikcu.edu.tr](mailto:makif.ozdemir@ikcu.edu.tr)