Cheyne-Stokes respiration perception via machine learning algorithms

Yuan, C., Khan, M. B., Yang, X., Shah, F. H. and Abbasi, Q. H. (2022) Cheyne-Stokes respiration perception via machine learning algorithms. Electronics, 11(6), 958. (doi: 10.3390/electronics11060958)

Abstract

With the development of science and technology, transparent, non-invasive general computing is gradually applied to disease diagnosis and medical detection. Universal software radio peripherals (USRP) enable non-contact awareness based on radio frequency signals. Cheyne-Stokes respiration has been reported as a common symptom in patients with heart failure. Compared with the disadvantages of traditional detection equipment, a microwave sensing method based on channel state information (CSI) is proposed to qualitatively detect the normal breathing and Cheyne-Stokes breathing of patients with heart failure in a non-contact manner. Firstly, USRP is used to collect subjects’ respiratory signals in real time. Then the CSI waveform is filtered, smoothed and normalized, and the relevant features are defined and extracted from the signal. Finally, the machine learning classification algorithm is used to establish a recognition model to detect the Cheyne-Stokes respiration of patients with heart failure. The results show that the system accuracy of support vector machine (SVM) is 97%, which can assist medical workers to identify Cheyne-Stokes respiration symptoms of patients with heart failure.

• Item Type: Articles
• Additional Information: This research was funded by Fundamental Research Funds for the Central Universities (JB180205).
• Keywords: CSI, non-invasive detection, Cheyne-Stokes respiration, USRP.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer

Creator Roles

Abbasi, Q. H.: Writing – review and editing

• Authors: Yuan, C., Khan, M. B., Yang, X., Shah, F. H., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Electronics
• Publisher: MDPI
• ISSN: 2079-9292
• ISSN (Online): 2079-9292
• Published Online: 20 March 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Electronics 11(6): 958
• Publisher Policy: Reproduced under a Creative Commons License