ArticleDiagnostics (Basel, Switzerland)2021
Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Advancing cardiovascular screening: deep learning-based heart-sound classification using SMOTE and temporal modeling.Scientific reports · 2026Article
- Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases.Anatolian journal of cardiology · 2024Article
- Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.Informatics in medicine unlocked · 2023Article
- An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences.Diagnostics (Basel, Switzerland) · 2022Article
- L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets.Diagnostics (Basel, Switzerland) · 2022Article
- Audio texture analysis of COVID-19 cough, breath, and speech sounds.Biomedical signal processing and control · 2022Article
- Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.Sensors (Basel, Switzerland) · 2022Review
- COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans.Computer methods and programs in biomedicine update · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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