Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
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发表时间：
2020-02
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通讯作者：
Yunlu Wang;Menghan Hu;Qingli Li;Xiao-Ping Zhang;Guangtao Zhai;Nan Yao
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作者：
Yunlu Wang;Menghan Hu;Qingli Li;Xiao-Ping Zhang;Guangtao Zhai;Nan Yao
研究意义：在疫情防控期间，我们的研究有助于根据呼吸特征对COVID-19（新型冠状病毒）感染者进行预后、诊断和筛查。根据最新的临床研究，COVID-19的呼吸模式与流感和普通感冒的呼吸模式不同。 COVID-19 中出现的一项重要症状是呼吸急促。感染了 COVID-19 的人呼吸更加急促。我们的研究可以用来区分各种呼吸模式，并且我们的设备可以初步投入实际使用。该方法在一个主题和两个主题的情况下工作的演示视频可以在线下载。研究详情：以远程且不引人注目的方式准确检测人们意外的异常呼吸模式具有重要意义。在这项工作中，我们创新地利用深度相机和深度学习来实现这一目标。这项任务面临双重挑战：现实世界的数据量不足以训练得到深度模型；不同类型呼吸模式的类内变异较大，类外变异较小。本文考虑实际呼吸信号的特点，首次提出一种新颖高效的呼吸模拟模型（RSM），以填补大量训练数据与稀缺的现实数据之间的空白。随后，我们首先应用具有双向和注意力机制的 GRU 神经网络 (BI-AT-GRU) 对 6 种有临床意义的呼吸模式（平喘、呼吸急促、呼吸缓慢、Biots、Cheyne-Stokes 和中枢性呼吸暂停）进行分类。所提出的深度模型和建模思想具有扩展到公共场所、睡眠场景和办公环境等大规模应用的巨大潜力。
Research significance: During the epidemic prevention and control period, our study can be helpful in prognosis, diagnosis and screening for the patients infected with COVID-19 (the novel coronavirus) based on breathing characteristics. According to the latest clinical research, the respiratory pattern of COVID-19 is different from the respiratory patterns of flu and the common cold. One significant symptom that occurs in the COVID-19 is Tachypnea. People infected with COVID-19 have more rapid respiration. Our study can be utilized to distinguish various respiratory patterns and our device can be preliminarily put to practical use. Demo videos of this method working in situations of one subject and two subjects can be downloaded online. Research details: Accurate detection of the unexpected abnormal respiratory pattern of people in a remote and unobtrusive manner has great significance. In this work, we innovatively capitalize on depth camera and deep learning to achieve this goal. The challenges in this task are twofold: the amount of real-world data is not enough for training to get the deep model; and the intra-class variation of different types of respiratory patterns is large and the outer-class variation is small. In this paper, considering the characteristics of actual respiratory signals, a novel and efficient Respiratory Simulation Model (RSM) is first proposed to fill the gap between the large amount of training data and scarce real-world data. Subsequently, we first apply a GRU neural network with bidirectional and attentional mechanisms (BI-AT-GRU) to classify 6 clinically significant respiratory patterns (Eupnea, Tachypnea, Bradypnea, Biots, Cheyne-Stokes and Central-Apnea). The proposed deep model and the modeling ideas have the great potential to be extended to large scale applications such as public places, sleep scenario, and office environment.