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Pulse Interval Modulation-based Method to Extract the Respiratory Rate from Oscillometric Cuff Pressure Waveform During Blood Pressure Measurement

Lookup NU author(s): Professor Alan Murray, Dr Dingchang Zheng

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

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

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


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