PDF(Pubmed)
Abstract：
Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.
摘要：
咳嗽是一种非常常见的症状，也是寻求医疗建议的最常见原因。优化护理不可避免地要通过对这种症状的适应性记录和自动处理。这项研究提供了咳嗽声音采集领域的最新详尽的定量审查，自动检测较长的音频序列和自动分类的性质或疾病。分析了相关研究，提取并处理了指标，以创建最新技术和趋势的定量表征。建立了客观标准列表，以从临床实践的角度选择最完整的检测研究的子集。有144项研究入围，并绘制了最先进的技术图。趋势表明分类研究越来越多，数据集大小的增加，部分来自众包，COVID-19研究的迅速增加，智能手机和可穿戴传感器的普及，和深度学习的快速扩展。最后,12项检测研究的一个子集被确定为最完整的。给出了无与伦比的定量概述。该领域显示出非凡的动态，在对COVID-19诊断的研究的推动下，以及对移动医疗的完美适应。
