Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning
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 ne...
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| Format: | Article |
| Language: | English |
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BMC
2021-05-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01521-x |
| _version_ | 1819092130602156032 |
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| author | Mehmet Akif Ozdemir Gizem Dilara Ozdemir Onan Guren |
| author_facet | Mehmet Akif Ozdemir Gizem Dilara Ozdemir Onan Guren |
| author_sort | Mehmet Akif Ozdemir |
| collection | DOAJ |
| description | 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. Source code All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification |
| first_indexed | 2024-12-21T22:50:44Z |
| format | Article |
| id | doaj.art-542c785a87e2458eb52e16231e03963a |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-21T22:50:44Z |
| publishDate | 2021-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-542c785a87e2458eb52e16231e03963a2022-12-21T18:47:36ZengBMCBMC Medical Informatics and Decision Making1472-69472021-05-0121112010.1186/s12911-021-01521-xClassification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learningMehmet Akif Ozdemir0Gizem Dilara Ozdemir1Onan Guren2Department of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi UniversityDepartment of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi UniversityDepartment of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi UniversityAbstract 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. Source code All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classificationhttps://doi.org/10.1186/s12911-021-01521-xCOVID-19ECGPaper-based ECGGLCMHexaxial mappingDeep learning |
| spellingShingle | Mehmet Akif Ozdemir Gizem Dilara Ozdemir Onan Guren Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning BMC Medical Informatics and Decision Making COVID-19 ECG Paper-based ECG GLCM Hexaxial mapping Deep learning |
| title | Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning |
| title_full | Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning |
| title_fullStr | Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning |
| title_full_unstemmed | Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning |
| title_short | Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning |
| title_sort | classification of covid 19 electrocardiograms by using hexaxial feature mapping and deep learning |
| topic | COVID-19 ECG Paper-based ECG GLCM Hexaxial mapping Deep learning |
| url | https://doi.org/10.1186/s12911-021-01521-x |
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