Automated detection of COVID-19 cough

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.

This work was supported by the Intelligent Energy Europe programme and the Ministerio de Economía y Competitividad under contract TIN2017-84553-C2-2-R and the Ministerio de Ciencia e Innovación under contract PID2020-113614RB-C22. We thank Alfredo Jover and Maria Ramirez from the Functional Unit of Nosocomial Infections, and Cristina Acosta from the Internal Medicine, of the Arnau de Vilanova University Hospital in Lleida, who recorded coughs using the website covid.udl.cat. We also thank the University of Cambridge (in particular, Cecilia Mascolo), the Coswara and Virufy projects, who voluntary share their cough datasets. Finally, we also thank everyone who volunteered their cough.