Pulse interval modulation-based method to extract the respiratory rate from oscillometric cuff pressure waveform during blood pressure measurement

Yihan Gui, Fei Chen, Alan Murray, Dingchang Zheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Respiratory frequency has been extensively used to assess 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 20 healthy 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 the OscP 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.
Original languageEnglish
Title of host publicationComputing in Cardiology
PublisherIEEE
Volume44
ISBN (Electronic)978-1-5386-6630-2
ISBN (Print)978-1-5386-4555-0
DOIs
Publication statusPublished - 5 Apr 2018
Externally publishedYes
EventComputing in Cardiology 2017 - Rennes, France
Duration: 24 Sep 201727 Sep 2017
Conference number: 44
https://dblp.org/db/conf/cinc/cinc2017

Publication series

Name
ISSN (Electronic)2325-887X

Conference

ConferenceComputing in Cardiology 2017
Abbreviated titleCINC 2017
CountryFrance
CityRennes
Period24/09/1727/09/17
Internet address

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