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Duca ȘT, Chetran A, Miftode RȘ, Mitu O, Costache AD, Nicolae A, Iliescu-Halițchi D, Halițchi-Iliescu CO, Mitu F, Costache II. Myocardial Ischemia in Patients with COVID-19 Infection: Between Pathophysiological Mechanisms and Electrocardiographic Findings. Life (Basel) 2022; 12:life12071015. [PMID: 35888103 PMCID: PMC9318430 DOI: 10.3390/life12071015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/26/2022] [Accepted: 06/29/2022] [Indexed: 12/29/2022] Open
Abstract
Given the possible pathophysiological links between myocardial ischemia and SARS-CoV-2 infection, several studies have focused attention on acute coronary syndromes in order to improve patients’ morbidity and mortality. Understanding the pathophysiological aspects of myocardial ischemia in patients infected with SARS-CoV-2 can open a broad perspective on the proper management for each patient. The electrocardiogram (ECG) remains the easiest assessment of cardiac involvement in COVID-19 patients, due to its non-invasive profile, accessibility, low cost, and lack of radiation. The ECG changes provide insight into the patient’s prognosis, indicating either the worsening of an underlying cardiac illnesses or the acute direct injury by the virus. This indicates that the ECG is an important prognostic tool that can affect the outcome of COVID-19 patients, which important to correlate its aspects with the clinical characteristics and patient’s medical history. The ECG changes in myocardial ischemia include a broad spectrum in patients with COVID-19 with different cases reported of ST-segment elevation, ST-segment depression, and T wave inversion, which are associated with severe COVID-19 disease.
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Affiliation(s)
- Ștefania Teodora Duca
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania
| | - Adriana Chetran
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania
- Correspondence: ; Tel.: +40-741089910
| | - Radu Ștefan Miftode
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania
| | - Ovidiu Mitu
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania
| | - Alexandru Dan Costache
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiovascular Rehabilitation, Clinical Rehabilitation Hospital, 700661 Iasi, Romania
| | - Ana Nicolae
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania
| | - Dan Iliescu-Halițchi
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, Arcadia Hospital, 700620 Iasi, Romania
| | - Codruța-Olimpiada Halițchi-Iliescu
- Department of Mother and Child Medicine-Pediatrics, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania;
- Department of Pedriatics, Arcadia Hospital, 700620 Iasi, Romania
| | - Florin Mitu
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiovascular Rehabilitation, Clinical Rehabilitation Hospital, 700661 Iasi, Romania
| | - Irina Iuliana Costache
- Department of Internal Medicine I, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700145 Iasi, Romania; (Ș.T.D.); (R.Ș.M.); (O.M.); (A.D.C.); (A.N.); (D.I.-H.); (F.M.); (I.I.C.)
- Department of Cardiology, “St. Spiridon” Emergency County Hospital, 700111 Iasi, Romania
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Attallah O. An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques. BIOSENSORS 2022; 12:bios12050299. [PMID: 35624600 PMCID: PMC9138764 DOI: 10.3390/bios12050299] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2022] [Accepted: 04/24/2022] [Indexed: 06/01/2023]
Abstract
Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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Sobahi N, Sengur A, Tan RS, Acharya UR. Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection. Comput Biol Med 2022; 143:105335. [PMID: 35219186 PMCID: PMC8858432 DOI: 10.1016/j.compbiomed.2022.105335] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 02/09/2023]
Abstract
BACKGROUND The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible. METHOD For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer. RESULTS A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database.
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Affiliation(s)
- Nebras Sobahi
- King Abdulaziz University, Department of Electrical and Computer Engineering, Jeddah, Saudi Arabia.
| | - Abdulkadir Sengur
- Firat University, Technology Faculty, Electrical and Electronics Engineering Department, Elazig, Turkey
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore and Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Attallah O. ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration. Comput Biol Med 2022; 142:105210. [PMID: 35026574 PMCID: PMC8730786 DOI: 10.1016/j.compbiomed.2022.105210] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 12/29/2022]
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.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 1029, Egypt.
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Ozdemir MA, Ozdemir GD, Guren O. Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning. BMC Med Inform Decis Mak 2021; 21:170. [PMID: 34034715 PMCID: PMC8146190 DOI: 10.1186/s12911-021-01521-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 05/05/2021] [Indexed: 12/17/2022] Open
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.
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Affiliation(s)
- Mehmet Akif Ozdemir
- 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
| | - Gizem Dilara Ozdemir
- 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
| | - Onan Guren
- Department of Biomedical Engineering, Faculty of Enigneering and Architecture, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
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