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Yang, P, Fan, D, Yang, J, Zhang, J, Lv, Z, Huang, H and Qi, J (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
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Respiraton Devices-PY-F4.pdf - Accepted Version Download (1MB) | Preview |
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 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 noisy, 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 stablise the device calibration and remove pressure signal nosie. For improving the reability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of 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 new designed respiratory device.
| Item Type: | Article |
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| Additional Information: | (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE |
| Date Deposited: | 20 Mar 2017 11:50 |
| Last Modified: | 04 Sep 2021 11:48 |
| DOI or ID number: | 10.1109/JTEHM.2017.2688458 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/6042 |
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