Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2252-2257. doi: 10.1109/EMBC46164.2021.9629830.
Cough is one of the most common symptoms of COVID-19. It is easily recorded using a smartphone for further analysis. This makes it a great way to track and possibly identify patients with COVID. In this paper, we present a deep learning-based algorithm to identify whether a patient's audio recording contains a cough for subsequent COVID screening. More generally, cough identification is valuable for the remote monitoring and tracking of infections and chronic conditions. Our algorithm is validated on our novel dataset in which COVID-19 patients were instructed to volunteer natural coughs. The validation dataset consists of real patient cough and no cough audio. It was supplemented by files without cough from publicly available datasets that had cough-like sounds including: throat clearing, snoring, etc. Our algorithm had an area under receiver operating characteristic curve statistic of 0.977 on a validation set when making a cough/no cough determination. The specificity and sensitivity of the model on a reserved test set, at a threshold set by the validation set, was 0.845 and 0.976. This algorithm serves as a fundamental step in a larger cascading process to monitor, extract, and analyze COVID-19 patient coughs to detect the patient's health status, symptoms, and potential for deterioration.
咳嗽是 COVID-19 的最常见症状之一。使用智能手机很容易记录下来，以便进一步分析。这使得它成为追踪和识别 COVID 患者的一种很好的方法。在本文中，我们提出了一种基于深度学习的算法，用于识别患者的音频记录中是否包含咳嗽，以便进行后续的 COVID 筛查。更一般地说，咳嗽识别对于感染和慢性疾病的远程监测和跟踪非常有价值。我们的算法在我们的新型数据集上进行了验证，该数据集要求 COVID-19 患者自愿录制自然咳嗽。验证数据集包含真实的患者咳嗽和无咳嗽音频。它还补充了来自公开数据集的没有咳嗽的文件，这些文件包含类似于咳嗽的声音，包括：清嗓子、打鼾等。当对验证集进行咳嗽/不咳嗽的判断时，我们的算法在接收者操作特征曲线统计量上的得分达到了 0.977。在验证集设置的阈值下，模型在保留测试集上的特异性和敏感性分别为 0.845 和 0.976。该算法是一个更大的级联过程的基本步骤，用于监测、提取和分析 COVID-19 患者的咳嗽，以检测患者的健康状况、症状和恶化的可能性。
