论文标题
注意咳嗽：使用可解释的症状嵌入带有咳嗽声信号处理
Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing
论文作者
论文摘要
由SARS-COV-2引起的Covid-19（2019年冠状病毒病）大流行导致了危险的灾难性灾难。在撰写本文时，不建议使用特定的防病毒药物或疫苗来控制感染的传播和扩散。 COVID-19的当前诊断是通过反转录聚合物链反应（RT-PCR）测试完成的。但是，这种方法是昂贵的，耗时的，并且在海峡地区不容易获得。根据咳嗽的声音特征和症状元数据，设计和开发了一种可解释的诊断AI框架，以克服这些局限性。使用包含30000个音频段的症状和人口统计数据的医学数据集评估了拟议的框架的性能，来自150例咳嗽患者的328次咳嗽声音（COVID-19，哮喘，支气管炎和健康）。实验的结果表明，该模型捕获了更好，强大的特征嵌入，以区分Covid-19患者咳嗽和几种类型的非旋转19咳嗽，其特异性和准确性较高，为95.04 $ \ pm $ 0.18％和96.83 $ \ pm PM $ 0.18％，同时维持可解释的能力。
COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led to a treacherous and devastating catastrophe for humanity. At the time of writing, no specific antivirus drugs or vaccines are recommended to control infection transmission and spread. The current diagnosis of COVID-19 is done by Reverse-Transcription Polymer Chain Reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and not easily available in straitened regions. An interpretable and COVID-19 diagnosis AI framework is devised and developed based on the cough sounds features and symptoms metadata to overcome these limitations. The proposed framework's performance was evaluated using a medical dataset containing Symptoms and Demographic data of 30000 audio segments, 328 cough sounds from 150 patients with four cough classes ( COVID-19, Asthma, Bronchitis, and Healthy). Experiments' results show that the model captures the better and robust feature embedding to distinguish between COVID-19 patient coughs and several types of non-COVID-19 coughs with higher specificity and accuracy of 95.04 $\pm$ 0.18% and 96.83$\pm$ 0.18% respectively, all the while maintaining interpretability.
