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Lookup NU author(s): Emeritus Professor Alan MurrayORCiD, Dr Dingchang Zheng
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Respiratory frequency has been extensively used toassess health status. This study aimed to evaluate two methods of extracting the respiratory rate from oscillometric cuff pressure pulses (OscP) during blood pressure (BP) measurement, which was compared with reference respiration signal (Resp). OscP and Resp were simultaneously recorded on 20healthy subjects during the linear cuff deflation period of BP measurement. Reference Resp was obtained from a chest magnetometer and OscP from an electronic pressure sensor connected to the cuff. Two de-modulation methods were developed by using the peak or valley positions of theOscP waveform to measure pulse intervals, from which the respiration modulation signal was derived. Statistical analysis showed that, in comparison with the Resp, there was no significant difference (-0.001 Hz for the peak-based method, and 0.001 Hz for valley-based method), and their corresponding limits of agreement were -0.08 Hz to 0.08 Hz and -0.10 Hz to 0.11 Hz, respectively. There was also a high correlation between Resp and respiratory frequencies extracted from OscP waveform, with the correlation coefficients of 0.7 for both methods.In conclusion, the present work demonstrated that, during BP measurement, respiratory frequency can be accurately derived from using either peak or valley point to characterize pulse intervals.
Author(s): Gui Y, Chen F, Murray A, Zheng D
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Computing in Cardiology 2017
Year of Conference: 2017
Pages: 1-4
Online publication date: 05/04/2018
Acceptance date: 01/09/2017
Date deposited: 25/04/2018
ISSN: 2325-887X
Publisher: IEEE Computer Society
URL: https://doi.org/10.22489/CinC.2017.326-252
DOI: 10.22489/CinC.2017.326-252
Notes: Conference paper has been made open access on the conference webpage at http://www.cinc.org/archives/2017/pdf/326-252.pdf
