An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring

Peter H Charlton; Timothy Bonnici; Lionel Tarassenko; David A Clifton; Richard Beale; Peter J Watkinson; Jordi Alastruey

doi:10.1016/j.bspc.2020.102339

Peter H Charlton, Timothy Bonnici, Lionel Tarassenko, David A Clifton, Richard Beale, Peter J Watkinson, Jordi Alastruey

Original language	English
Article number	102339
Pages (from-to)	102339
Journal	Biomedical Signal Processing and Control
Volume	65
DOIs	https://doi.org/10.1016/j.bspc.2020.102339
Accepted/In press	15 Nov 2020
Published	1 Mar 2021
Additional links	Link to publication in Scopus

Funding Information: This work was supported by a UK Engineering and Physical Sciences Research Council (EPSRC) Impact Acceleration Award awarded to PHC; the EPSRC [ EP/H019944/1 ]; the Wellcome EPSRC Centre for Medical Engineering at King’s College London [ WT 203148/Z/16/Z ]; the Oxford and King’s College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grants [ WT88877/Z/09/Z ] and [ WT088641/Z/09/Z ]; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s & St Thomas’ NHS Foundation Trust and King’s College London ; the NIHR Oxford Biomedical Research Centre Programme; a Royal Academy of Engineering Research Fellowship (RAEng) awarded to DAC ; and EPSRC grants EP/P009824/1 and EP/N020774/1 to DAC. The views expressed are those of the authors and not necessarily those of the EPSRC, Wellcome Trust, NIHR, NHS or RAEng. Funding Information: This work was supported by a UK Engineering and Physical Sciences Research Council (EPSRC) Impact Acceleration Award awarded to PHC; the EPSRC [EP/H019944/1]; the Wellcome EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z]; the Oxford and King's College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grants [WT88877/Z/09/Z] and [WT088641/Z/09/Z]; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's & St Thomas? NHS Foundation Trust and King's College London; the NIHR Oxford Biomedical Research Centre Programme; a Royal Academy of Engineering Research Fellowship (RAEng) awarded to DAC; and EPSRC grantsEP/P009824/1 and EP/N020774/1 to DAC. The views expressed are those of the authors and not necessarily those of the EPSRC, Wellcome Trust, NIHR, NHS or RAEng. Publisher Copyright: © 2020 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

Documents

Impedance SQI manuscript - Sept 2020 submission
Impedance_SQI_manuscript_Sept_2020_submission.pdf, 835 KB, application/pdf
Uploaded date:04 Dec 2020
Version:Submitted manuscript
Impedance SQI manuscript - Oct 2020 revision
Impedance_SQI_manuscript_Oct_2020_revision.pdf, 843 KB, application/pdf
Uploaded date:04 Dec 2020
Version:Accepted author manuscript
Licence:CC BY-NC-ND

Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data.

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