Automated covid-19 and heart failure detection using dna pattern technique with cough sounds
Article
Kobat, Mehmet Ali, Kivrak, Tarik, Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Tan, Ru-San, Ciaccio, Edward J. and Acharya, U. Rajendra. 2021. "Automated covid-19 and heart failure detection using dna pattern technique with cough sounds." Diagnostics. 11 (11). https://doi.org/10.3390/diagnostics11111962
| Article Title | Automated covid-19 and heart failure detection using dna pattern technique with cough sounds |
|---|---|
| ERA Journal ID | 212275 |
| Article Category | Article |
| Authors | Kobat, Mehmet Ali, Kivrak, Tarik, Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Tan, Ru-San, Ciaccio, Edward J. and Acharya, U. Rajendra |
| Journal Title | Diagnostics |
| Journal Citation | 11 (11) |
| Article Number | 1962 |
| Number of Pages | 15 |
| Year | 2021 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 2075-4418 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/diagnostics11111962 |
| Web Address (URL) | https://www.mdpi.com/2075-4418/11/11/1962 |
| 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. |
| Keywords | Advanced sound processing; COVID-1; heart failure; DNA pattern; cough sounds |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Firat University, Turkey |
| School of Management and Enterprise | |
| University of Technology Sydney | |
| Cogninet Australia, Australia | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| Columbia University Irving Medical Center, United States | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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