Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

Mehmet Ali Kobat; Tarik Kivrak; Prabal Datta Barua; Turker Tuncer; Sengul Dogan; Ru-San Tan; Edward J Ciaccio; U Rajendra Acharya

doi:10.3390/diagnostics11111962

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

Diagnostics (Basel). 2021 Oct 22;11(11):1962. doi: 10.3390/diagnostics11111962.

Authors

Mehmet Ali Kobat¹, Tarik Kivrak¹, Prabal Datta Barua^{2

3

4}, Turker Tuncer⁵, Sengul Dogan⁵, Ru-San Tan^{6

7}, Edward J Ciaccio⁸, U Rajendra Acharya^{9

10

11}

Affiliations

¹ Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey.
² School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
³ Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
⁴ Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia.
⁵ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey.
⁶ Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore.
⁷ Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore.
⁸ Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, New York, NY 10032, USA.
⁹ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
¹⁰ Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599494, Singapore.
¹¹ Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.

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: COVID-19; DNA pattern; advanced sound processing; cough sounds; heart failure.