Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning.
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...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 21 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
5/25/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150494824&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150494824 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 5/25/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 150494824 150494824 NLM34034715 10.1186/s12911-021-01521-x NLM34034715 150494824 ppf: 1 ppct: 20 formats: tig: atl: Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning. aug: au: Ozdemir, Mehmet Akif Ozdemir, Gizem Dilara Guren, Onan affil: Department of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi University, 35620, Cigli, Izmir, Turkey sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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