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Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning
Mehmet Akif Ozdemir1,2, Gizem Dilara Ozdemir3,4, Onan Guren3
1Department of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi University, 35620, Cigli, Izmir, Turkey. makif.ozdemir@ikcu.edu.tr.
Insights
This study introduces a novel deep learning method using Electrocardiogram (ECG) data for automated COVID-19 diagnosis. The approach achieves high accuracy, offering a potential new tool for early detection of the disease.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronavirus disease 2019 (COVID-19) is a global pandemic requiring improved diagnostic methods.
- Current diagnostic techniques for COVID-19 have limitations, necessitating novel approaches.
- Early diagnosis of COVID-19 is critical for patient outcomes and disease management.
Purpose of the Study:
- To propose a novel deep learning framework for automated COVID-19 diagnosis using Electrocardiogram (ECG) data.
- To develop and validate a new hexaxial feature mapping technique for ECG representation.
- To assess the diagnostic performance of the proposed method in classifying COVID-19 cases.
Main Methods:
- A novel hexaxial feature mapping method was developed to convert 12-lead ECG data into 2D colorful images.
- Gray-Level Co-Occurrence Matrix (GLCM) was employed for feature extraction from ECG data.
- A new Convolutional Neural Network (CNN) architecture was designed and utilized for COVID-19 classification based on the generated ECG images.
Main Results:
- The proposed approach achieved 96.20% accuracy and 96.30% F1-Score in classifying COVID-19 versus normal ECG data.
- In a broader classification scenario, the method demonstrated 93.00% accuracy and 93.20% F1-Score for COVID-19 prediction.
- Experimental results confirmed the robustness and satisfactory performance of the deep learning model for COVID-19 detection.
Conclusions:
- Deep learning analysis of ECG data can effectively detect cardiovascular changes associated with COVID-19.
- ECG data holds potential as a non-invasive tool for the diagnosis of COVID-19.
- The developed framework can serve as a valuable decision-making support system for healthcare professionals in diagnosing COVID-19.
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.
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