Covid-19 Detection Using Variational Mode Decomposition of Cough Sounds

Abstract

According to the World Health Organization, cough is one of the most prominent symptoms of the COVID-19 disease declared as a global pandemic. The symptom is seen in 68% to 83% of people with COVID-19 who come to the clinic for medical examination. Therefore, during the pandemic, cough plays an important role in diagnosing of COVID-19 and distinguishing patients from healthy individuals. This study aims to distinguish the cough sounds of COVID-19 positive people from those of COVID-19 negative, thus providing automatic detection and support for the diagnosis of COVID-19. For this aim, “Virufy” dataset containing cough sounds labeled as COVID-19 and Non COVID-19 was included. After using the ADASYN technique to balance the data, independent modes were obtained for each sound by utilizing the Variational Mode Decomposition (VMD) method and various features were extracted from every mode. Afterward, the most effective features were selected by ReliefF algorithm. Following, ensemble machine learning methods, namely Random Forest, Gradient Boosting Machine and Adaboost were prepared to identify cough sounds as COVID-19 and Non COVID-19 through classification. As a result, the best performance was obtained with the Gradient Boosting Machine as 94.19% accuracy, 87.67% sensitivity, 100% specificity, 100% precision, 93.43% F-score, 0.88 kappa and 93.87% area under the ROC curve.