Exploiting optimum acoustic features in COVID-19 individual's breathing sounds

Abstract

The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors; thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes; statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals.