Audio-Based Cough Detection in Clinic Waiting Rooms
Audio-Based Cough Detection in Clinic Waiting Rooms
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DOI：
10.1109/ichi54592.2022.00037
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Yumna Anwar;S. Mullan;O. Chipara;Alberto Maria Segre;P. Polgreen
中科院分区：
文献类型：
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作者：
Yumna Anwar;S. Mullan;O. Chipara;Alberto Maria Segre;P. Polgreen
自动咳嗽检测在疾病监测和支持医疗决策方面具有重要应用，因为咳嗽声可以是有用的生物标志物。然而，由于缺乏真实世界的数据，鲁棒的咳嗽检测模型的实现和评估可能具有挑战性。本文介绍并提供了在诊所候诊室记录的2，883次咳嗽和3，074次非咳嗽声音的集合，我们希望这将成为这项任务的基线。使用这个数据集，我们评估了不同的卷积网络架构，用于将短音频片段分类为咳嗽或非咳嗽。卷积神经网络的集成模型提供了最强大的性能，ROC AUC为98.1%。同样重要的是，我们构建了一个咳嗽计数器，该计数器结合了集合模型来计算每天的咳嗽次数。然后，一个简单的线性模型估计患者报告咳嗽症状的就诊次数。这个简单的回归模型可以预测门诊的咳嗽次数，绝对平均误差为每天4.26次咳嗽。使用关于患者何时在诊所的额外信息有助于类似的回归模型达到每天3.65次咳嗽就诊的平均绝对误差。这些结果证明了使用咳嗽检测作为呼吸道病毒在社区内传播的生物标志物的可行性。
Automated cough detection has significant applications for the surveillance of diseases and supports medical decisions, as cough sounds can be a useful biomarker. However, the implementation and evaluation of robust cough detection models can be challenging due to the lack of real-world data. This paper introduces and makes available a collection of 2,883 coughs and 3,074 non-cough sounds recorded in clinic waiting rooms that we hope will become a baseline for this task. Using this dataset, we evaluate different convolutional network architectures for classifying short audio segments as cough or non-cough. An ensemble model of convolutional neuronal networks provides the most robust performance and has a ROC AUC of 98.1%. Equally important, we construct a cough counter that incorporates the ensemble model to compute the number of coughs per day. Then, a simple linear model estimates the number of visits in which the patients report cough symptoms from the cough counts. This simple regression model can predict the number of cough visits in the clinic with an absolute mean error of 4.26 cough visits per day. Using additional information about when patients are in the clinic helps a similar regression model reach a mean absolute error of 3.65 cough visits per day. These results demonstrate the feasibility of using cough detection as a biomarker for the spread of respiratory viruses within the community.