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

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.

Citation

Saeed, U., Shah, S. Y., Zahid, A., Ahmad, J., Imran, M. A., Abbasi, Q. H., & 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), 20833-20840. https://doi.org/10.1109/jsen.2021.3096641

• 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