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dc.contributor.authorStewart, Jill
dc.contributor.authorStewart, Paul
dc.contributor.authorWalker, Tom
dc.contributor.authorViramontes-Hörner, Daniela
dc.contributor.authorLucas, Bethany
dc.contributor.authorWhite, Kelly
dc.contributor.authorTaal, Maarten W.
dc.contributor.authorSelby, Nicholas M.
dc.contributor.authorMorris, Mel
dc.date.accessioned2020-12-04T12:31:53Z
dc.date.available2020-12-04T12:31:53Z
dc.date.issued2020-11-28
dc.identifier.citationStewart, J., Stewart, P., Walker, T., Viramontes-Hörner, D., Lucas, B., White, K., Taal, M.W., Selby, N.M. and Morris, M., (2020). 'An iterative run-to-run learning model to derive continuous brachial pressure estimates from arterial and venous lines during dialysis treatment'. Biomedical Signal Processing and Control, 65, pp. 1-8.en_US
dc.identifier.issn1746-8094
dc.identifier.doi10.1016/j.bspc.2020.102346
dc.identifier.urihttp://hdl.handle.net/10545/625443
dc.description.abstractObjective: Non-invasive continuous blood pressure monitoring is not yet part of routine practice in renal dialysis units but could be a valuable tool in the detection and prevention of significant variations in patient blood pressure during treatment. Feasibility studies have delivered an initial validation of a method which utilises pressure sensors in the extra-corporeal dialysis circuit, without any direct contact with the person receiving treatment. Our main objective is to further develop this novel methodology from its current early development status to a continuous-time brachial artery pressure estimator. Methods: During an in vivo patient feasibility study with concurrent measurement validation by Finapres Nova experimental physiological measurement device, real-time continuous dialysis line pressures, and intermittent occluding arm cuff pressure data were collected over the entire period of (typically 4-hour) dialysis treatments. There was found to be an underlying quasi-linear relationship between arterial line and brachial pressure measurements which supported the development of a mathematical function to describe the relationship between arterial dialysis line pressure and brachial artery BP. However, unmodelled non-linearities, dynamics and time-varying parameters present challenges to the development of an accurate BP estimation system. In this paper, we start to address the problem of physiological parameter time variance by novel application of an iterative learning run-to-run modelling methodology originally developed for process control engineering applications to a parameterised BP model. Results: The iterative run-to-run learning methodology was applied to the real-time data measured during an observational study in 9 patients, supporting subsequent development of an adaptive real-time BP estimator. Tracking of patient BP is analysed for all the subjects in our patient study, supported only by intermittent updates from BP cuff measurements. Conclusion: The methodology and associated technology is shown to be capable of tracking patient BP noninvasively via arterial line pressure measurement during complete 4-hour treatment sessions. A robust and tractable method is demonstrated, and future refinements to the approach are defined.en_US
dc.description.sponsorshipMel Morris, iTrend Medical Research Ltd.en_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/abs/pii/S1746809420304584en_US
dc.rights© 2020 Elsevier Ltd. All rights reserved.
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttps://www.elsevier.com/tdm/userlicense/1.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectSignal Processingen_US
dc.subjectHealth Informaticsen_US
dc.subjectDialysis, Blood pressure Monitoring, Iterative learning, Run-to-run controlen_US
dc.titleAn iterative run-to-run learning model to derive continuous brachial pressure estimates from arterial and venous lines during dialysis treatmenten_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Nottinghamen_US
dc.contributor.departmentRenal unit, Royal Derby Hospital, Derby,en_US
dc.contributor.departmentITREND Medical Research Ltd., UKen_US
dc.identifier.journalBiomedical Signal Processing and Controlen_US
dc.identifier.piiS1746809420304584
dc.source.journaltitleBiomedical Signal Processing and Control
dc.source.volume65
dc.source.beginpage102346
dcterms.dateAccepted2020-11-15
dc.author.detail784376en_US


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