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.
Highlights
The COVID-19 pandemic is continuing to the present time despite recent vaccination efforts
To evaluate the proposed model comprehensively, three distinct clinically relevant classification problems were defined based on the collected cough sound dataset: Case 1: COVID-19 vs. healthy binary classification. 906 + 696 = 1602 observations were analyzed, and iterative maximum relevance minimum redundancy (ImRMR) was implemented to select 198 features
We presented presented the results obtained using ImRMR, iterative neighborhood component analysis the results obtained using ImRMR, iterative neighborhood component analysis (INCA), (INCA), iterative ReliefF (IRF) and iterative Chi2 (IChi2) feature selectors
Summary
The COVID-19 pandemic is continuing to the present time despite recent vaccination efforts. Experts advise people to continue to wear masks, implement sanitization procedures, and avoid crowds [1,2]. COVID-19 has disrupted normal life and has strained national health resources, even more so at the beginning of the pandemic [3]. A new normal is necessary to limit its spread [4] and people are often living in isolation according to quarantine rules [5,6]. Many patients with pre-existing chronic illnesses such as heart failure (HF) suffer restricted access to routine medical care, and may risk acute clinical deterioration that requires hospitalization [7]
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