Saeed Umer, Shah Syed Yaseen, Zahid Adnan, Ahmad Jawad, Imran Muhammad Ali, Abbasi Qammer H, Shah Syed Aziz
Research Centre for Intelligent HealthcareCoventry University Coventry CV1 5FB U.K.
School of Computing, Engineering and Built EnvironmentGlasgow Caledonian University Glasgow G4 0BA U.K.
IEEE Sens J. 2021 Jul 12;21(18):20833-20840. doi: 10.1109/JSEN.2021.3096641. eCollection 2021 Sep 15.
Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.
非接触式或非侵入性技术对诸如新冠病毒症状预测等医疗保健应用具有重大影响。非侵入性方法至关重要，尤其是在新冠疫情期间，因为它们可将医护人员的负担降至最低。新冠病毒感染的一个显著症状是呼吸频率加快，这需要对呼吸模式进行持续实时监测。在本文中，基于软件定义无线电（SDR）的射频传感技术和监督式机器学习算法被用于提供一个检测和监测各种呼吸模式的平台，这些呼吸模式包括：平静呼吸、比奥呼吸、呼吸过缓、叹息样呼吸、呼吸急促和库斯莫尔呼吸。利用人类呼吸产生的信道状态信息变化，通过细粒度正交频分复用信号来识别不同的呼吸模式。基于SDR和深度多层感知器分类器的所提出平台展现出能够有效检测和分类上述不同呼吸模式的能力，准确率高达99%。此外，通过与一种先进的机器学习分类器——随机森林进行比较，证明了所提方案在诊断准确率、精确率、召回率、F1分数和混淆矩阵方面的有效性。
