抄録
In this paper, we propose a method for reducing the computational cost of strong coupling for multiscale cardiovascular simulation models. In such a model, individual model modules of myocardial cell, left ventricular structural dynamics, and circulatory hemodynamics are coupled. The strong coupling method enables stable and accurate calculation, but requires iterative calculations which are computationally expensive. The iterative calculations can be reduced, if accurate initial approximations are made available by predictors. The proposed method uses the Kalman filter to estimate accurate predictions by filtering out noise included in past values. The performance of the proposed method was assessed with an application to a previously published multiscale cardiovascular model. The proposed method reduced the number of iterations by 90% and 62% compared with no prediction and Lagrange extrapolation, respectively. Even when the parameters were varied and number of elements of the left ventricular finite-element model increased, the number of iterations required by the proposed method was significantly lower than that without prediction. These results indicate the robustness, scalability, and validity of the proposed method.
| 元の言語 | 英語 |
|---|---|
| 記事番号 | 7111221 |
| ページ（範囲） | 1100-1106 |
| ページ数 | 7 |
| ジャーナル | IEEE Journal of Biomedical and Health Informatics |
| 巻 | 20 |
| 発行部数 | 4 |
| DOI | |
| 出版物ステータス | 出版済み - 7 1 2016 |
| 外部発表 | Yes |
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All Science Journal Classification (ASJC) codes
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management
これを引用
Application of the Kalman Filter for Faster Strong Coupling of Cardiovascular Simulations. / Hasegawa, Yuki; Shimayoshi, Takao; Amano, Akira; Matsuda, Tetsuya.
：: IEEE Journal of Biomedical and Health Informatics, 巻 20, 番号 4, 7111221, 01.07.2016, p. 1100-1106.研究成果: ジャーナルへの寄稿 › 記事
}
TY - JOUR
T1 - Application of the Kalman Filter for Faster Strong Coupling of Cardiovascular Simulations
AU - Hasegawa, Yuki
AU - Shimayoshi, Takao
AU - Amano, Akira
AU - Matsuda, Tetsuya
PY - 2016/7/1
Y1 - 2016/7/1
N2 - In this paper, we propose a method for reducing the computational cost of strong coupling for multiscale cardiovascular simulation models. In such a model, individual model modules of myocardial cell, left ventricular structural dynamics, and circulatory hemodynamics are coupled. The strong coupling method enables stable and accurate calculation, but requires iterative calculations which are computationally expensive. The iterative calculations can be reduced, if accurate initial approximations are made available by predictors. The proposed method uses the Kalman filter to estimate accurate predictions by filtering out noise included in past values. The performance of the proposed method was assessed with an application to a previously published multiscale cardiovascular model. The proposed method reduced the number of iterations by 90% and 62% compared with no prediction and Lagrange extrapolation, respectively. Even when the parameters were varied and number of elements of the left ventricular finite-element model increased, the number of iterations required by the proposed method was significantly lower than that without prediction. These results indicate the robustness, scalability, and validity of the proposed method.
AB - In this paper, we propose a method for reducing the computational cost of strong coupling for multiscale cardiovascular simulation models. In such a model, individual model modules of myocardial cell, left ventricular structural dynamics, and circulatory hemodynamics are coupled. The strong coupling method enables stable and accurate calculation, but requires iterative calculations which are computationally expensive. The iterative calculations can be reduced, if accurate initial approximations are made available by predictors. The proposed method uses the Kalman filter to estimate accurate predictions by filtering out noise included in past values. The performance of the proposed method was assessed with an application to a previously published multiscale cardiovascular model. The proposed method reduced the number of iterations by 90% and 62% compared with no prediction and Lagrange extrapolation, respectively. Even when the parameters were varied and number of elements of the left ventricular finite-element model increased, the number of iterations required by the proposed method was significantly lower than that without prediction. These results indicate the robustness, scalability, and validity of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84978208492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978208492&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2015.2436212
DO - 10.1109/JBHI.2015.2436212
M3 - Article
C2 - 26011898
AN - SCOPUS:84978208492
VL - 20
SP - 1100
EP - 1106
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 4
M1 - 7111221
ER -