Andreu-Perez Javier, Perez-Espinosa Humberto, Timonet Eva, Kiani Mehrin, Giron-Perez Manuel I, Benitez-Trinidad Alma B, Jarchi Delaram, Rosales-Perez Alejandro, Gatzoulis Nick, Reyes-Galaviz Orion F, Torres-Garcia Alejandro, Reyes-Garcia Carlos A, Ali Zulfiqar, Rivas Francisco
School of Computer Science and Electronic Engineering, Faculty of Science and HealthUniversity of Essex Colchester CO4 3SQ U.K.
Department of Computer ScienceUniversity of Jaén 16747 Jaén Spain.
IEEE Trans Serv Comput. 2021 Feb 23;15(3):1220-1232. doi: 10.1109/TSC.2021.3061402. eCollection 2022 May.
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 ( Covid-19 positive and 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 . 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 . Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of [Formula: see text] , sensitivity of [Formula: see text] , and specificity of [Formula: see text] and average AUC of [Formula: see text] 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.
为降低新型冠状病毒肺炎（Covid-19）的感染率，世界各国纷纷响应，迫切需要一种经济、可及、即时检测的诊断测试来识别Covid-19携带者，以便建议他们（检测呈阳性的个体）自我隔离，而不是让整个社区隔离。快速周转时间诊断测试的可用性基本上意味着总体生活可以恢复正常。在这方面，与我们同时进行的研究调查了包括咳嗽在内的不同呼吸声音，以识别潜在的Covid-19携带者。然而，这些研究缺乏临床对照，并且依赖互联网用户在网络问卷（众包）中确认他们的测试结果，因此其分析并不充分。我们旨在仅基于来自经认证实验室定量逆转录聚合酶链反应（qRT-PCR）检测的（Covid-19阳性和Covid-19阴性）咳嗽声音，评估一种Covid-19初筛工具的检测性能。除了根据患者的qRT-PCR阈值循环（Ct）和淋巴细胞计数得出的疾病严重程度外，所有收集的样本均根据结果进行临床标记，即Covid-19阳性或阴性。我们提出的通用方法是一种基于经验模态分解（EMD）的算法，用于咳嗽声音检测，随后基于音频声谱图张量和具有卷积层的深度人工神经网络分类器（称为 ）进行分类。基于张量维度数量，研究了两种不同版本的DeepCough，即DeepCough2D和DeepCough3D。这些方法已部署在多平台原型网络应用程序 中。对于三种严重程度的识别，Covid-19识别结果率达到了有前景的曲线下面积（AUC）为[公式：见正文] ，灵敏度为[公式：见正文] ，特异性为[公式：见正文] ，平均AUC为[公式：见正文] 。我们提出的网络工具作为一种即时的Covid-19初筛诊断测试，有助于快速检测感染情况。我们相信它有潜力在全球范围内显著遏制Covid-19大流行。
