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A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

2021; Institute of Electrical and Electronics Engineers; Volume: 15; Issue: 3 Linguagem: Inglês

10.1109/tsc.2021.3061402

2372-0204

Javier Andreu-Pérez, Humberto Pérez-Espinosa, Eva Timonet‐Andreu, Mehrin Kiani, Manuel Iván Girón‐Pérez, Alma Betsaida Benítez-Trinidad, Delaram Jarchi, Alejandro Rosales-Pérez, Nick Gatzoulis, Orion F. Reyes-Galaviz, Alejandro A. Torres-García, Carlos A. Reyes-García, Zulfiqar Ali, Francisco Rivas-Ruíz,

Respiratory viral infections research

In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from