Automatic and Robust Identification of Spontaneous Coughs from COVID-19 Patients.

Abstract

Cough is one of the most common symptoms of COVID-19. It is easily recorded using a smartphone for further analysis. This makes it a great way to track and possibly identify patients with COVID. In this paper, we present a deep learning-based algorithm to identify whether a patient's audio recording contains a cough for subsequent COVID screening. More generally, cough identification is valuable for the remote monitoring and tracking of infections and chronic conditions. Our algorithm is validated on our novel dataset in which COVID-19 patients were instructed to volunteer natural coughs. The validation dataset consists of real patient cough and no cough audio. It was supplemented by files without cough from publicly available datasets that had cough-like sounds including: throat clearing, snoring, etc. Our algorithm had an area under receiver operating characteristic curve statistic of 0.977 on a validation set when making a cough/no cough determination. The specificity and sensitivity of the model on a reserved test set, at a threshold set by the validation set, was 0.845 and 0.976. This algorithm serves as a fundamental step in a larger cascading process to monitor, extract, and analyze COVID-19 patient coughs to detect the patient's health status, symptoms, and potential for deterioration.