Wireless channel modelling for identifying six types of respiratory patterns with SDR sensing and deep multilayer perceptron

Saeed, U., Shah, S. Y., Zahid, A., Anjum, N., Ahmad, J., Imran, M. A. , Abbasi, Q. H. and Shah, S. A. (2021) Wireless channel modelling for identifying six types of respiratory patterns with SDR sensing and deep multilayer perceptron. IEEE Sensors Journal, 21(18), pp. 20833-20840. (doi: 10.1109/JSEN.2021.3096641)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zahid, Mr Adnan and Abbasi, Professor Qammer and Imran, Professor Muhammad and Shah, Mr Syed
• Authors: Saeed, U., Shah, S. Y., Zahid, A., Anjum, N., Ahmad, J., Imran, M. A., Abbasi, Q. H., and Shah, S. A.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 12 July 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Sensors Journal 21(18):20833-20840
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher