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 positive and 6,041 Covid-19 negative) Samples were clinically labelled 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 a algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and 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 IEEE
A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels