Ozdemir Mehmet Akif, Ozdemir Gizem Dilara, Guren Onan
Department of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi University, 35620, Cigli, Izmir, Turkey.
Department of Biomedical Technologies, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, 35620, Cigli, Izmir, Turkey.
BMC Med Inform Decis Mak. 2021 May 25;21(1):170. doi: 10.1186/s12911-021-01521-x.
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.
All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification.
自2019年末首次出现以来，2019冠状病毒病（COVID-19）已成为全球大流行疾病。由COVID-19导致的死亡人数仍在逐日增加，早期诊断变得至关重要。由于当前的诊断方法存在诸多缺点，需要开展新的研究以提高诊断性能。
首次提出一种利用心电图（ECG）数据通过深度学习自动诊断COVID-19的新方法。此外，还提出了一种名为六轴特征映射的新有效方法，将12导联心电图表示为二维彩色图像。使用灰度共生矩阵（GLCM）方法提取特征并生成六轴映射图像。然后将这些生成的图像输入到一种新的卷积神经网络（CNN）架构中以诊断COVID-19。
在一个公开可用的基于纸质心电图图像的数据集上进行了两种不同的分类场景，以揭示所提方法的诊断能力和性能。在第一种场景中，对标记为COVID-19和无异常发现（正常）的心电图数据进行分类，以评估COVID-19分类能力。根据结果，所提方法在COVID-19检测方面表现出色，准确率为96.20%，F1分数为96.30%。在第二种场景中，对标记为阴性（正常、异常和心肌梗死）和阳性（COVID-19）的心电图数据进行分类，以评估COVID-19诊断能力。实验结果表明，所提方法在COVID-19预测方面表现令人满意，准确率为93.00%，F1分数为93.20%。此外，还进行了不同的实验研究以评估所提方法的稳健性。
通过深度学习框架利用心电图数据可以实现对由COVID-19引起的心血管变化的自动检测。这不仅证明了由COVID-19引起的心血管变化的存在，还表明心电图有可能用于COVID-19的诊断。我们相信所提研究可能为医疗保健专业人员提供一个关键的决策系统。
所有源代码可在以下网址公开获取：https://github.com/mkfzdmr/COVID-19-ECG-Classification 。
