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首页> 外文期刊>BMC Medical Informatics and Decision Making >Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning
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Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

机译：使用Hexaxial特征映射和深度学习来分类Covid-19心电图

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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. 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. 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. 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.
机译：冠状病毒疾病2019（Covid-19）自2019年底首次出现以来已成为大流行。Covid-19造成的死亡日益日益增加，早期诊断已经至关重要。由于目前的诊断方法具有许多缺点，因此需要新的调查来提高诊断的性能。提出了一种新的方法，通过第一次使用深度学习的心电图（ECG）数据来自动诊断Covid-19。此外，提出了一种新的和有效的方法，称为Hexaxial特征映射，以将12个引导ECG代表到2D彩色图像。灰度级共出矩阵（GLCM）方法用于提取特征并生成Hexaxial映射图像。然后将这些生成的图像馈送到新的卷积神经网络（CNN）架构中以诊断Covid-19。两个不同的分类方案是在公开的基于纸张的ECG图像数据集上进行的，以揭示所提出的方法的诊断能力和性能。在第一场景中，标记为Covid-19和No-Findings（正常）的ECG数据被分类以评估Covid-19分类能力。根据结果​​，该方法提供了令人鼓舞的Covid-19检测性能，精度为96.20％，F1分数为96.30％。在第二种情况下，标记为阴性（正常，异常和心肌梗死）和阳性（Covid-19）的ECG数据被分类以评估Covid-19诊断能力。实验结果表明，该方法提供了令人满意的Covid-19预测性能，精度为93.00％和F1分，得分为93.20％。此外，进行了不同的实验研究以评估所提出的方法的鲁棒性。通过ECG数据的深度学习框架，可以实现由Covid-19引起的心血管变化的自动检测。这不仅证明了Covid-19引起的心血管变化的存在，而且揭示了ECG可能用于Covid-19的诊断。我们认为拟议的研究可以为医疗保健专业人员提供一个重要的决策制度。

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