ArticleBiosensors2022
An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Light-XAI: a CADx for explainable cervical cancer detection via attention-based lightweight convolutional neural networks and layer-wise feature fusion.BioData mining · 2026Article
- Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration.Frontiers in artificial intelligence · 2026Article
- Enhancing Heart Disease Diagnosis Using ECG Signal Reconstruction and Deep Transfer Learning Classification with Optional SVM Integration.Diagnostics (Basel, Switzerland) · 2025Article
- A lightweight deep learning framework for transformer fault diagnosis in smart grids using multiple scale CNN features.Scientific reports · 2025Article
- Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.Scientific reports · 2024Article
- AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.Diagnostics (Basel, Switzerland) · 2024Review
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection.Biomimetics (Basel, Switzerland) · 2024Article
- A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI.PloS one · 2024Article
- RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection.Biomimetics (Basel, Switzerland) · 2023Article
- A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.Medical & biological engineering & computing · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection.Information sciences · 2023Article
- A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods.Diagnostics (Basel, Switzerland) · 2022Article
- A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.Applied soft computing · 2022Article
- Article
- A deep learning-based diagnostic tool for identifying various diseases via facial images.Digital healthArticle
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1 author.
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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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