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

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.

Role of Artificial Intelligence in COVID-19 Detection

“…Nonetheless, this modality is not useful when the pleura is spared from the pneumonic pathology during the early course of the disease [ 297 ]. Recent developments in the diagnosis of COVID-19 using signals such as respiratory sounds, speech signals, and coughing sounds, have also attracted many researchers [ 298 , 299 ]. Furthermore, in the future, this can be combined with other imaging modalities and signals to enhance the performance of the system using various deep learning approaches.…”

COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans

“… [9] initiated an automated diagnosis system to differentiate between COVID-19 and heart failure depending on the pattern of cough sounds. They proposed a model to generate features deploying DNA pattern.…”

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

“…The authors employed a random forest classifier with nearly 90% accuracy. Kobat et al [13] used a graph-based local feature generator, an iterative maximum relevance minimal redundancy iterative feature selector, and the k-nearest neighbor classifier for cough sound-based COVID-19, heart failure, and healthy subject discrimination. For the COVID-19 vs. healthy, heart failure vs. healthy, and COVID-19 vs. heart failure vs. healthy classes, the authors reported accuracies of 100.0 %, 99.38 %, and 99.49 %, respectively.…”