  We seek to evaluate the detection performance of a rapid primary screening
tool of Covid-19 solely based on the cough sound from 8,380 clinically
validated samples with laboratory molecular-test (2,339 Covid-19 positives and
6,041 Covid-19 negatives). Samples were clinically labeled according to the
results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle
threshold, and lymphocytes count from the patients. Our proposed generic method
is an algorithm based on Empirical Mode Decomposition (EMD) with subsequent
classification based on a tensor of audio features and a deep artificial neural
network classifier with convolutional layers called DeepCough'. Two different
versions of DeepCough based on the number of tensor dimensions, i.e.
DeepCough2D and DeepCough3D, have been investigated. These methods have been
deployed in a multi-platform proof-of-concept Web App CoughDetect to administer
this test anonymously. Covid-19 recognition results rates achieved a promising
AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and
specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three
severity levels. Our proposed web tool and underpinning algorithm for the
robust, fast, point-of-need identification of Covid-19 facilitates the rapid
detection of the infection. We believe that it has the potential to
significantly hamper the Covid-19 pandemic across the world.
