LAUSANNE, April 19 (WNM/EPFL) - Researchers with the Embedded Systems Laboratory at the Swiss Federal Institute of Technology Lausanne (EPFL) are developing a diagnostic test on a smartphone app that works by "listening" to the sound of the user's cough (https://coughvid.epfl.ch/).

In the wake of the COVID-19 pandemic, mass coronavirus testing has proven essential to governments in monitoring the spread of the disease, isolating infected individuals, and effectively “flattening the curve” of infections over time. However, this oropharyngeal swab test is physically invasive and must be performed by a trained clinician. Ideally, testing would be performed noninvasively at no cost and administered at the homes of potential patients to minimize contamination risk.

The World Health Organization (WHO) has reported that 67.7% of COVID-19 patients exhibit a “dry cough”, meaning that no mucus is produced, unlike the typical “wet cough” that occurs during a cold or allergies (https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf). Dry coughs can be distinguished from wet coughs by the sound they produce, which raises the question of whether the analysis of the cough sounds can give some insights about COVID-19. Such cough sounds analysis has proven successful in diagnosing respiratory conditions like pertussis, asthma, and pneumonia.

The Embedded Systems Laboratory (ESL) at EPFL proposes to leverage signal processing, pervasive computing, and machine learning to develop an Android application and website to automatically screen COVID-19 from the comfort of people’s homes. Test subjects will be able to simply download a mobile application, enter their symptoms, record an audio clip of their cough, and upload the data anonymously to our servers. ESL will then use audio signal processing and machine learning techniques to evaluate if there is some room for automatic or assisted COVID-19 screening.