Fan, D., Yang, J., Zhang, J. et al. (4 more authors) (2018) Effectively measuring respiratory flow with portable pressure data using back propagation neural network. IEEE Journal of Translational Engineering in Health and Medicine, 6. ISSN 2168-2372
Abstract
Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in uncontrolled environments which is also a problem. For portable breathing equipment, due to the volume limit, the pressure signals acquired by using the airway tube may be too weak and contain some noise, leading to huge errors in respiratory flow measures. In this paper, a cost-effective portable pressure sensor-based respiratory measure device is designed. This device has a new airway tube design, which enables the pressure drop efficiently after the air flowing through the airway tube. Also, a new back propagation (BP) neural network-based algorithm is proposed to stabilize the device calibration and remove pressure signal noise. For improving the reliability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of the proposed BP neural network algorithm have been carried out. The results show that giving proper parameters setting, the proposed BP neural network algorithm is capable of efficiently improving the reliability of newly designed respiratory device.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. |
Keywords: | Respiratory monitoring; mainstream; airway flow; respiratory tube; BP neural network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Sep 2019 13:06 |
Last Modified: | 12 Sep 2019 13:06 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/jtehm.2017.2688458 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150776 |