Update README.md

README.md

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-# 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** 
+<br/>
+**Background** <p align="justify">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.</p>
+
+**Methods**
+<p align="justify">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.</p>
+
+**Results**
+<p align="justify">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.</p>
+
+**Conclusion**
+<p align="justify">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.</p>
+
+**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: [[email protected]](mailto:[email protected])