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Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability

Sanjay Pant Orcid Logo, Chiara Corsini, Catriona Baker, Tain-Yen Hsia, Giancarlo Pennati, Irene E. Vignon-Clementel

Journal of The Royal Society Interface, Volume: 14, Issue: 126

Swansea University Author: Sanjay Pant Orcid Logo

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DOI (Published version): 10.1098/rsif.2016.0513

Abstract

Inverse problems in cardiovascular modelling have become increasingly important to assess each patient individually. These problems entail estimation of patient-specific model parameters from uncertain measurements acquired in the clinic. In recent years, the method of data assimilation, especially...

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Published in: Journal of The Royal Society Interface
ISSN: 1742-5689 1742-5662
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa34499
first_indexed 2017-06-27T20:09:30Z
last_indexed 2021-01-08T03:54:19Z
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spelling 2021-01-07T14:47:05.9531050 v2 34499 2017-06-27 Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2017-06-27 ACEM Inverse problems in cardiovascular modelling have become increasingly important to assess each patient individually. These problems entail estimation of patient-specific model parameters from uncertain measurements acquired in the clinic. In recent years, the method of data assimilation, especially the unscented Kalman filter, has gained popularity to address computational efficiency and uncertainty consideration in such problems. This work highlights and presents solutions to several challenges of this method pertinent to models of cardiovascular haemodynamics. These include methods to (i) avoid ill-conditioning of the covariance matrix, (ii) handle a variety of measurement types, (iii) include a variety of prior knowledge in the method, and (iv) incorporate measurements acquired at different heart rates, a common situation in the clinic where the patient state differs according to the clinical situation. Results are presented for two patient-specific cases of congenital heart disease. To illustrate and validate data assimilation with measurements at different heart rates, the results are presented on a synthetic dataset and on a patient-specific case with heart valve regurgitation. It is shown that the new method significantly improves the agreement between model predictions and measurements. The developed methods can be readily applied to other pathophysiologies and extended to dynamical systems which exhibit different responses under different sets of known parameters or different sets of inputs (such as forcing/excitation frequencies). Journal Article Journal of The Royal Society Interface 14 126 1742-5689 1742-5662 data assimilation, unscented Kalman filter, parameter estimation, heart rate, singleventricle physiology, haemodynamics 31 1 2017 2017-01-31 10.1098/rsif.2016.0513 https://hal.inria.fr/hal-01413446 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2021-01-07T14:47:05.9531050 2017-06-27T16:21:05.7395791 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Sanjay Pant 0000-0002-2081-308X 1 Chiara Corsini 2 Catriona Baker 3 Tain-Yen Hsia 4 Giancarlo Pennati 5 Irene E. Vignon-Clementel 6 0034499-05072017124838.pdf pant2016.pdf 2017-07-05T12:48:38.5070000 Output 771437 application/pdf Accepted Manuscript true 2018-01-11T00:00:00.0000000 true eng
title Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
spellingShingle Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
Sanjay Pant
title_short Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
title_full Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
title_fullStr Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
title_full_unstemmed Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
title_sort Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability
author_id_str_mv 43b388e955511a9d1b86b863c2018a9f
author_id_fullname_str_mv 43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant
author Sanjay Pant
author2 Sanjay Pant
Chiara Corsini
Catriona Baker
Tain-Yen Hsia
Giancarlo Pennati
Irene E. Vignon-Clementel
format Journal article
container_title Journal of The Royal Society Interface
container_volume 14
container_issue 126
publishDate 2017
institution Swansea University
issn 1742-5689
1742-5662
doi_str_mv 10.1098/rsif.2016.0513
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
url https://hal.inria.fr/hal-01413446
document_store_str 1
active_str 0
description Inverse problems in cardiovascular modelling have become increasingly important to assess each patient individually. These problems entail estimation of patient-specific model parameters from uncertain measurements acquired in the clinic. In recent years, the method of data assimilation, especially the unscented Kalman filter, has gained popularity to address computational efficiency and uncertainty consideration in such problems. This work highlights and presents solutions to several challenges of this method pertinent to models of cardiovascular haemodynamics. These include methods to (i) avoid ill-conditioning of the covariance matrix, (ii) handle a variety of measurement types, (iii) include a variety of prior knowledge in the method, and (iv) incorporate measurements acquired at different heart rates, a common situation in the clinic where the patient state differs according to the clinical situation. Results are presented for two patient-specific cases of congenital heart disease. To illustrate and validate data assimilation with measurements at different heart rates, the results are presented on a synthetic dataset and on a patient-specific case with heart valve regurgitation. It is shown that the new method significantly improves the agreement between model predictions and measurements. The developed methods can be readily applied to other pathophysiologies and extended to dynamical systems which exhibit different responses under different sets of known parameters or different sets of inputs (such as forcing/excitation frequencies).
published_date 2017-01-31T19:10:09Z
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score 11.04748

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