Kobat Mehmet Ali, Kivrak Tarik, Barua Prabal Datta, Tuncer Turker, Dogan Sengul, Tan Ru-San, Ciaccio Edward J, Acharya U Rajendra
Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey.
School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Diagnostics (Basel). 2021 Oct 22;11(11):1962. doi: 10.3390/diagnostics11111962.
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
新型冠状病毒肺炎（COVID-19）和心力衰竭（HF）是常见疾病，尽管它们有一些相似症状，但需要不同的治疗方法。准确诊断这些疾病对于疾病管理至关重要，包括对患者进行隔离以遏制COVID-19的感染传播。在这项工作中，我们旨在开发一种计算机辅助诊断系统，该系统可以使用咳嗽声音准确区分这三类（正常、COVID-19和HF）。一种新颖的手工制作模型被用于利用脱氧核糖核酸（DNA）模式自动对COVID-19与健康（病例1）、HFHF与健康（病例2）以及COVID-19与HF与健康（病例3）进行分类。该模型是使用通过手机从241名COVID-19患者、244名HF患者和247名健康受试者收集的咳嗽声音开发的。据我们所知，这是第一项使用咳嗽声音信号对健康受试者、HF和COVID-19患者进行自动分类的工作。我们提出的模型包括一个基于图的局部特征生成器（DNA模式）、一个迭代最大相关最小冗余（ImRMR）迭代特征选择器，并使用k近邻分类器进行分类。我们提出的模型在病例1、病例2和病例3中的准确率分别达到了100.0%、99.38%和99.49%。所开发的系统完全自动化且经济实惠，可用于使用咳嗽声音准确检测COVID-19与HF。
