Imperial College London
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ProfessorSpencerSherwin

Faculty of Engineering, Department of Aeronautics

Head of the Department of Aeronautics
 
 
 
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Contact

 

+44 (0)20 7594 5052s.sherwin Website

 
 
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Location

 

318City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Reavette:2021:10.3389/fbioe.2021.737055,
author = {Reavette, RM and Sherwin, SJ and Tang, M-X and Weinberg, PD},
doi = {10.3389/fbioe.2021.737055},
journal = {Frontiers in Bioengineering and Biotechnology},
pages = {1--13},
title = {Wave intensity analysis combined with machine learning can detect impaired stroke volume in simulations of heart failure},
url = {http://dx.doi.org/10.3389/fbioe.2021.737055},
volume = {9},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Heart failure is treatable, but in the United Kingdom, the 1-, 5- and 10-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques; the disease is often advanced by the time a diagnosis is made. Previous studies have demonstrated that certain metrics derived from pressure-velocity-based wave intensity analysis are significantly altered in the presence of impaired heart performance when averaged over groups, but to date, no study has examined the diagnostic potential of wave intensity on an individual basis, and, additionally, the pressure waveform can only be obtained accurately using invasive methods, which has inhibited clinical adoption. Here, we investigate whether a new form of wave intensity based on noninvasive measurements of arterial diameter and velocity can detect impaired heart performance in an individual. To do so, we have generated a virtual population of two-thousand elderly subjects, modelling half as healthy controls and half with an impaired stroke volume. All metrics derived from the diameter-velocity-based wave intensity waveforms in the carotid, brachial and radial arteries showed significant crossover between groups-no one metric in any artery could reliably indicate whether a subject's stroke volume was normal or impaired. However, after applying machine learning to the metrics, we found that a support vector classifier could simultaneously achieve up to 99% recall and 95% precision. We conclude that noninvasive wave intensity analysis has significant potential to improve heart failure screening and diagnosis.
AU  - Reavette,RM
AU  - Sherwin,SJ
AU  - Tang,M-X
AU  - Weinberg,PD
DO  - 10.3389/fbioe.2021.737055
EP  - 13
PY  - 2021///
SN  - 2296-4185
SP  - 1
TI  - Wave intensity analysis combined with machine learning can detect impaired stroke volume in simulations of heart failure
T2  - Frontiers in Bioengineering and Biotechnology
UR  - http://dx.doi.org/10.3389/fbioe.2021.737055
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35004634
UR  - https://www.frontiersin.org/articles/10.3389/fbioe.2021.737055/full
UR  - http://hdl.handle.net/10044/1/93873
VL  - 9
ER  -
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