TY - GEN
T1 - Physics-based deep spatio-temporal metamodeling for cardiac electrical conduction simulation
AU - Yan, Hao
AU - Zhao, Xinyu
AU - Hu, Zhiyong
AU - Du, Dongping
N1 - Funding Information:
This project is partially supported by NSF DMS-1830363, CMMI-1646664, and CMMI-1728338.
Funding Information:
*Research supported by National Science Foundation. Hao Yan is with the Arizona State University, Tempe, AZ 85281 USA (corresponding author) phone: (480) 727-0556; e-mail: haoyan@asu.edu. Xinyu Zhao is with the Arizona State University, Tempe, AZ 85281 USA e-mail: xzhao119@asu.edu. Zhiyong Hu, and Dongping Du are with Texas Tech University, Lubbock, TX 79409, USA, email: dongping.du@ttu.edu
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Modeling and simulation have been widely used in both cardiac research and clinical study to investigate cardiac disease mechanism and develop new treatment design. Electrical conduction among cardiac tissue is commonly modeled with a partial differential equation, i.e., reaction-diffusion equation, where the reaction term describes cellular excitation and diffusion term describes electrical propagation. Cellular excitation can be modeled by either detailed human cellular models or simplified models such as the FitzHugh-Nagumo model; electrical propagation can be simulated using either biodomain or mono-domain tissue model. However, existing cardiac models have a great level of complexity, and the simulation is often time-consuming. This paper develops a new spatiotemporal model as a surrogate model of the timeconsuming cardiac model. Specifically, we propose to investigate the auto-regressive convolutional neural network (AR-CNN) and convolutional long short-term memory (Conv-LSTM) to model the spatial and temporal structure for the metamodeling. Model predictions are compared to the one-dimensional simulation data to validate the prediction accuracy. The metamodel can accurately capture the properties of the individual cardiac cell, as well as the electrical wave morphology in cardiac fiber at different simulation scenarios, which demonstrates its superior performance in modeling and the long-term prediction.
AB - Modeling and simulation have been widely used in both cardiac research and clinical study to investigate cardiac disease mechanism and develop new treatment design. Electrical conduction among cardiac tissue is commonly modeled with a partial differential equation, i.e., reaction-diffusion equation, where the reaction term describes cellular excitation and diffusion term describes electrical propagation. Cellular excitation can be modeled by either detailed human cellular models or simplified models such as the FitzHugh-Nagumo model; electrical propagation can be simulated using either biodomain or mono-domain tissue model. However, existing cardiac models have a great level of complexity, and the simulation is often time-consuming. This paper develops a new spatiotemporal model as a surrogate model of the timeconsuming cardiac model. Specifically, we propose to investigate the auto-regressive convolutional neural network (AR-CNN) and convolutional long short-term memory (Conv-LSTM) to model the spatial and temporal structure for the metamodeling. Model predictions are compared to the one-dimensional simulation data to validate the prediction accuracy. The metamodel can accurately capture the properties of the individual cardiac cell, as well as the electrical wave morphology in cardiac fiber at different simulation scenarios, which demonstrates its superior performance in modeling and the long-term prediction.
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U2 - 10.1109/COASE.2019.8842902
DO - 10.1109/COASE.2019.8842902
M3 - Conference contribution
AN - SCOPUS:85072961701
T3 - IEEE International Conference on Automation Science and Engineering
SP - 152
EP - 157
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Y2 - 22 August 2019 through 26 August 2019
ER -