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

Abstract

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 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 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 '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 prototype web-app 'CoughDetect'. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of [Formula: see text] $98.80\%\pm 0.83\%$ , sensitivity of [Formula: see text] $96.43\%\pm 1.85\%$ , and specificity of [Formula: see text] $96.20\%\pm 1.74\%$ and average AUC of [Formula: see text] $81.08\%\pm 5.05\%$ 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.

Keywords: Deep Learning; audio systems; smart healthcare.

Fig. 1.

The CoughDetect app can be easily used with a) mobile phone, b) laptop or c) tablet connected with Internet.

Fig. 2.

A user can record his cough sample using the CoughDetect web or mobile app with complete anonymity. The user's cough sample is then analysed by DeepCough (the inference mechanism of CoughDetect) for primary screening of Covid-19. A user can receive one of the following two messages on successful analysis of his cough sample: ${}^{*}$Your cough sound shares similarities to those of Covid-19 patients, if you are a high-risk individual, please contact health services immediately, otherwise quarantine yourself. ${}^{†}$Our system does not recognise your pattern as similar to those with Covid-19 in our database, still if you feel the most likely symptoms, please contact health services.

Fig. 3.

The overall flow diagram delineating the steps involved in the DeepCough, 2D and 3D, inference mechanism.

Fig. 4.

a) Density distributions of cycle threshold (Ct), lymphocyte count, age, and days from first symptoms from the samples of Covid-19 positive patients. b) Percentage ratios of sex (Male, Female, and Not Specified) and level of positivity (Borderline Positive, Standard Positive, and High Positive) of samples from positive Covid-19 patients displayed in pie charts. c) Percentage ratios of sex (Male, Female, and Not Specified) of samples from negative Covid-19 displayed in a pie chart along with density distribution of age for Covid-19 negative patients.

Fig. 5.

A pictorial illustration of the steps involved in the detection algorithm.

Fig. 6.

Energy contours of the Auditory Processed Spectrum (APS) representation which is related to MFCCs (a-c) Covid-19 positive patients (d-f) Covid-19 negative persons. Darker colors represent lower energy in the spectrum, while lighter color means higher energy.

Fig. 7.

(a) An illustration of the architecture of the Convolutional Neural Network (CNN) with (b) dimensions of convolutional blocks (B1-B4), max-pooling layers, a global averaging (GA), and a dense (D) layer.

Fig. 8.

Flowchart outlining the model selection process with Auto-ML .

Fig. 9.

a) Statistical metrics comparison of DeepCough with the other best and worst methods tested. b) Receiver Operating Characteristic (ROC) for DeepCough and other methods to detect pulmonary infection (Covid-19) coughs versus other type of coughs using this study database.

Fig. 10.

Statistical performance results for the recognition of possible markers of disease severity.