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

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 <i>8,380</i> <i>clinically validated samples with laboratory molecular-test</i> (<i>2,339</i> Covid-19 positive and <i>6,041</i> Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e., Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called <i>'DeepCough'</i>. 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 prototype web-app <i>'CoughDetect'</i>. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of [Formula: see text] <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>98</mml:mn> <mml:mo>.</mml:mo> <mml:mn>80</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>83</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> , sensitivity of [Formula: see text] <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>96</mml:mn> <mml:mo>.</mml:mo> <mml:mn>43</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>.</mml:mo> <mml:mn>85</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> , and specificity of [Formula: see text] <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>96</mml:mn> <mml:mo>.</mml:mo> <mml:mn>20</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>.</mml:mo> <mml:mn>74</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> and average AUC of [Formula: see text] <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>81</mml:mn> <mml:mo>.</mml:mo> <mml:mn>08</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>5</mml:mn> <mml:mo>.</mml:mo> <mml:mn>05</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> for the recognition of three severity levels. Our proposed web tool as a point-of-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.