Automated detection of COVID-19 cough

Alberto Tena; Francesc Clarià; Francesc Solsona

doi:10.1016/j.bspc.2021.103175

Automated detection of COVID-19 cough

Biomed Signal Process Control. 2022 Jan:71:103175. doi: 10.1016/j.bspc.2021.103175. Epub 2021 Sep 13.

Authors

Alberto Tena¹, Francesc Clarià², Francesc Solsona²

Affiliations

¹ CIMNE, Building C1, North Campus, UPC. Gran Capità, 08034 Barcelona, Spain.
² Dept. of Computer Science & INSPIRES, University of Lleida. Jaume II 69, E-25001 Lleida, Spain.

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.

Keywords: Automated cough detection; Diagnosis; Keyword: COVID-19; Signal processing; Time–frequency.