Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks

• URL: http://arxiv.org/abs/2208.05433v1
• Date: Wed, 10 Aug 2022 16:44:28 GMT
• Title: Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks
• Authors: Thao Nguyen, Hieu H. Pham, Huy Khiem Le, Anh Tu Nguyen, Ngoc Tien Thanh, Cuong Do
• Abstract summary: Clinical reports indicated that electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. We propose a novel method to extract ECG signals from ECG paper records, which are fed into a one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately.
• Score: 7.10816643091298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into a one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42%, 95.63%, and 98.50% for classifying COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.

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