TY - JOUR
T1 - Propagation of Parametric Uncertainty in Aliev-Panfilov Model of Cardiac Excitation
AU - Son, Jeongeun
AU - Du, Yuncheng
AU - Du, Dongping
PY - 2018/7/1
Y1 - 2018/7/1
N2 - Models of cardiac electrophysiology are useful for studying heart functions and cardiac disease mechanisms. However, cardiac models often have a great level of complexity, and it is often computationally prohibitive to simulate tissue and organ activities in a real-time fashion. To address the challenge, simplified models such as Aliev-Panfilov model are developed to reduce model complexity, while providing necessary details of cardiac functions. Simplified models may induce uncertainty, which can deteriorate the accuracy and reliability of cardiac models. In addition, model parameters are calibrated with noisy data and cannot be known with certainty. It is important to assess the effect of parametric uncertainty on model predictions. For the probabilistic, time-invariant parametric uncertainty, a generalized polynomial chaos (gPC) expansion-based method is presented in this work to quantify and propagate uncertainty onto model predictions. Using gPC, a measure of confidence in model predictions can be quickly estimated. As compared with sampling-based uncertainty propagation techniques, e.g., Monte Carlo (MC) simulations, the gPC-based method in this work shows its advantages in terms of computational efficiency and accuracy, which has the potentials for dealing with complicated cardiac models, e.g., 2D tissue and 3D organ models.
AB - Models of cardiac electrophysiology are useful for studying heart functions and cardiac disease mechanisms. However, cardiac models often have a great level of complexity, and it is often computationally prohibitive to simulate tissue and organ activities in a real-time fashion. To address the challenge, simplified models such as Aliev-Panfilov model are developed to reduce model complexity, while providing necessary details of cardiac functions. Simplified models may induce uncertainty, which can deteriorate the accuracy and reliability of cardiac models. In addition, model parameters are calibrated with noisy data and cannot be known with certainty. It is important to assess the effect of parametric uncertainty on model predictions. For the probabilistic, time-invariant parametric uncertainty, a generalized polynomial chaos (gPC) expansion-based method is presented in this work to quantify and propagate uncertainty onto model predictions. Using gPC, a measure of confidence in model predictions can be quickly estimated. As compared with sampling-based uncertainty propagation techniques, e.g., Monte Carlo (MC) simulations, the gPC-based method in this work shows its advantages in terms of computational efficiency and accuracy, which has the potentials for dealing with complicated cardiac models, e.g., 2D tissue and 3D organ models.
UR - http://www.scopus.com/inward/record.url?scp=85056671063&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513608
DO - 10.1109/EMBC.2018.8513608
M3 - Article
C2 - 30441570
SN - 1557-170X
VL - 2018
SP - 5450
EP - 5453
JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
ER -