ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

doi:10.1016/j.compbiomed.2022.105210

Computers in Biology and Medicine

Volume 142, March 2022, 105210

https://doi.org/10.1016/j.compbiomed.2022.105210 Get rights and content

Highlights

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The potential of using ECG data for COVID-19 diagnosis is investigated using a new pipeline called ECG-BiCoNet.
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ECG-BiCoNet extracts two levels of deep features from five convolutional neural networks of distinct structures.
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ECG-BiCoNet examines which level of features has a higher impact on its performance.
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The results of ECG-BiCoNet show that ECG data may be used for COVID-19 diagnosis.

Abstract

The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) applications, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

Keywords

COVID-19

ECG trace Image

Deep learning

Convolutional neural networks (CNN)

Discrete wavelet transform (DWT)

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Omneya Attallah received her B.Sc. and M.Sc. degrees in 2006 and 2009, respectively from the electronics and communications engineering department in the Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt. She received a full scholarship from the AASTMT for her B.Sc. and M.Sc. studies. She was the first in her class in the B.Sc degree. She received her Ph.D. degree in electrical end electronic engineering in 2016 from Aston University, Birmingham, UK. From 2008 to 2011 she was a teaching and research assistant with the department of electronics and communication engineering, AAST. From 2011 to 2015, she was a Ph.D. student at the school of engineering and applied sciences, Aston University, Birmingham, UK. From 2016 to 2020, she was working as an assistant professor teaching and researching at the electronics and communications engineering department in the AAST, Alexandria, Egypt. Since 2020, she has been an associate professor at the same department. She is a reviewer of IEEE access journal, Scientific Reports journals by Nature, and Journal of Ambient Intelligence and Humanized computing by springer and several other reputable journals in Springer, MDPI, and Wiley publishers. She is currently a mentor at Neuromatch Academy. Her current research interests include signal/image processing, biomedical engineering, biomedical informatics, neuroinformatics, pattern recognition, machine/deep learning, and data mining.

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