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 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

COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model

“…There are only two quite similar previous studies considering the latest literature. In the first study, COVID-19 disease has been performed from ECG data using well-known pre-trained DL models such as InceptionV3, MobileNetv2, DenseNet201, ResNet18, ResNet50 and ResNet101 [ 26 ]. The experimental results show that the pre-trained DenseNet201 outperforms other CNN models for COVID-19 vs. Normal binary classification with an accuracy of 99.1%.…”

“…There are also important publications that investigated the usage of ECG data to detect COVID-19 disease. For example, Ozdemir et al have proposed a DL based method to detect COVID-19 disease using ECG data [ 26 ]. They have obtained detection accuracy of 96.20%.…”

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

“…Du et al in [ 30 ], used the approach of deep learning on ECG trace images using a Fine-grained Multi-label ECG (FM-ECG) framework to effectively detect the abnormalities using recurrent neural network (RNN) from the real clinical ECG was proposed. Ozdemir et al [ 31 ] proposed hexaxial feature mapping for detecting COVID-19 to represent 12-lead ECG to 2D colourful images, also the Gray-Level Co-Occurrence Matrix was applied in feature extraction. In this examination, they obtained 93.20% of testing accuracy.…”

J-waves in acute COVID-19: A novel disease characteristic and predictor of mortality?

“…Although ECG analysis can be realistically utilized even in overstrained medical systems, ECG patterns offering a potential prognostic value have yet to be identified. Ozdemir et al have proposed a novel approach to the classification of COVID-19 ECG by using a hexaxial feature mapping along with deep learning [ 16 ]. In their study, they were able to achieve COVID-19 outcome prediction with an accuracy of 93.0% with emphasis on the impact of COVID-19 on ECG changes [ 16 ].…”

“…Ozdemir et al have proposed a novel approach to the classification of COVID-19 ECG by using a hexaxial feature mapping along with deep learning [ 16 ]. In their study, they were able to achieve COVID-19 outcome prediction with an accuracy of 93.0% with emphasis on the impact of COVID-19 on ECG changes [ 16 ]. In addition, the analysis of J-waves might represent a promising approach in the context of COVID-19-induced ECG changes.…”