Ge, Y., Husmeier, D. , Lazarus, A., Rabbani, A. and Gao, H. (2023) Bayesian inference of cardiac models emulated with a time series Gaussian process. In: Proceedings of the 5th International Conference on Statistics: Theory and Applications. International Aset Inc., p. 149. ISBN 9781990800252 (doi: 10.11159/icsta23.149)
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Abstract
The objective of this research is to estimate the specific biophysical parameters of a cardiac mechanics model using a time series of variables that can be acquired in the clinic. This method is driven by the need to infer the passive stiffness of the myocardium to diagnose cardiophysiological diseases, which requires the measurement of the volume of the left ventricle (LV) of the heart at different time points. Although there have been many advancements in cardiac models, their computation is complex and costly. To overcome this challenge, we propose a method that utilizes a Gaussian process to construct a statistical model for emulation. Since the LV volumes are acquired in a time series, we employ the Kronecker product to compute two covariance matrices separately for time and biophysical parameters. Once we construct an accurate emulator to represent the passive filling process of the cardiac mechanics model during diastole, we can estimate the biophysical parameters inversely. We also aim to evaluate the impact of increasing the number of time points on reducing the uncertainty of the inverse estimation in this study.
| Item Type: | Book Sections |
|---|---|
| Additional Information: | This work has been supported by EPSRC (grant reference numbers EPT017899/1, EP/S020950/1, EP/R511705/1) and the British Heart Foundation (PG/22/10930). |
| Keywords: | Gaussian process, time series kernel, inverse estimate, cardiac model. |
| Status: | Published |
| Glasgow Author(s) Enlighten ID: | Gao, Dr Hao and Husmeier, Professor Dirk |
| Authors: | Ge, Y., Husmeier, D., Lazarus, A., Rabbani, A., and Gao, H. |
| Subjects: | Q Science > QA Mathematics |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics College of Science and Engineering > School of Mathematics and Statistics > Mathematics |
| Publisher: | International Aset Inc. |
| ISSN: | 2562-7767 |
| ISBN: | 9781990800252 |
| Copyright Holders: | Copyright © 2023 International ASET Inc. |
| First Published: | First published in Proceedings of the Proceedings of the 5th International Conference on Statistics: Theory and Applications (ICSTA'24): 149 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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