Effectively measuring respiratory flow with portable pressure data using back propagation neural network

Dayong Fan, Jiachen Yang, Junbao Zhang*, Zhihan Lv, Haojun Huang, Jun Qi, Po Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

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.

Original languageEnglish
Article number1600112
JournalIEEE Journal of Translational Engineering in Health and Medicine
Volume6
DOIs
Publication statusPublished - 25 Jan 2018
Externally publishedYes

Keywords

  • BP neural network
  • Respiratory monitoring
  • airway flow
  • mainstream
  • respiratory tube

Fingerprint

Dive into the research topics of 'Effectively measuring respiratory flow with portable pressure data using back propagation neural network'. Together they form a unique fingerprint.

Cite this