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Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Saeed, Umer; Shah, Syed Yaseen; Zahid, Adnan; Ahmad, Jawad; Imran, Muhammad Ali; Abbasi, Qammer H.; Shah, Syed Aziz

Authors

Umer Saeed

Syed Yaseen Shah

Adnan Zahid

Muhammad Ali Imran

Qammer H. Abbasi

Syed Aziz Shah



Abstract

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.

Journal Article Type Article
Acceptance Date Jul 7, 2021
Online Publication Date Jul 12, 2021
Publication Date Sep 15, 2021
Deposit Date Jan 31, 2022
Journal IEEE Sensors Journal
Print ISSN 1530-437X
Electronic ISSN 1558-1748
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 21
Issue 18
Pages 20833-20840
DOI https://doi.org/10.1109/jsen.2021.3096641
Keywords COVID-19, abnormal respiratory, non-invasive, USRP, CSI, software defined radio, neural network
Public URL http://researchrepository.napier.ac.uk/Output/2839898