Automated detection of COVID-19 cough. (January 2022) - Tena, Alberto; Clarià, Francesc; Solsona, Francesc
Highlights: A new time–frequency methodology for the automatic detection of COVID-19 cough. It can be applied in other respiratory diseases and for detecting Covid outbreaks. The method obtained an accuracy close to 90%. High performance was obtained in various sampling sources (UdL, UC, Virufy and Coswara). The experiments validated the method as a generic proposal. Abstract: Easy detection of COVID-19 is a challenge. Quick biological tests do not give enough accuracy. Success in the fight against new outbreaks depends not only on the efficiency of the tests used, but also on the cost, time elapsed and the number of tests that can be done massively. Our proposal provides a solution to this challenge. The main objective is to design a freely available, quick and efficient methodology for the automatic detection of COVID-19 in raw audio files. Our proposal is based on automated extraction of time–frequency cough features and selection of the more significant ones to be used to diagnose COVID-19 using a supervised machine-learning algorithm. Random Forest has performed better than the other models analysed in this study. An accuracy close to 90% was obtained. This study demonstrates the feasibility of the automatic diagnose of COVID-19 from coughs, and its applicability to detecting new outbreaks.