Data-Driven Model Reduction for Fast, High Fidelity Atrial Electrophysiology Computations
Understanding and predicting atrial electrophysiology, for diagnosis and therapy planning purposes, calls for methods able to accurately represent the complex patterns of atrial electrical activity, and to produce very fast predictions to be suitable for use in the clinical practice. We apply a data-driven approach for the model reduction of an atrial cellular model. The reduced model predicts cellular action potentials (AP) in a simple form but is effective in capturing the physiological complexity of the original model. The model construction starts from an AP manifold learning which reduces the AP manifold dimension to 15, and continues with a regression model learning to predict the 15 components in the reduced AP manifold. The regression model has the potential to drastically improve the performance of atrial tissue-level electrophysiology (EP) modeling, enabling a 75 % reduction of the computational cost with the same time step and up to two order of magnitudes smaller computational time with larger time steps. The model is also capable of describing the restitution properties of the AP, as demonstrated in tests with varying diastolic intervals. This model has great potential use for real-time personalized atrial EP modeling, and the same modeling technique can be extended to the study of other excitable myocardial tissues.
- 2.Atienza, F., Almendral, J., Moreno, J., Vaidyanathan, R., Talkachou, A., Kalifa, J., Arenal, A., Villacastin, J., Torrecilla, E., Sanchez, A., Ploutz-Snyder, R., Jalife, J., Berenfeld, O.: Activation of inward rectifier potassium channels accelerates atrial fibrillation in Humans: evidence for a reentrant mechanism. Circulation 114, 2434–2442 (2006)CrossRefGoogle Scholar
- 4.Courtemanche, M., Ramirez, R., Nattel, S.: Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. Am. J. Physiol. 275, H301–H321 (1998)Google Scholar
- 7.Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009)Google Scholar
- 8.Krummen, D., Bayer, J., Ho, J., Ho, G., Smetak, M., Clopton, P., Trayanova, N., Narayan, S.: Mechanisms of human atrial fibrillation initiation: clinical and computational studies of repolarization restitution and activation latency. Circ. Arrhythm. Electrophysiol. 5(6), 1149–1159 (2012)CrossRefGoogle Scholar
- 11.Rapaka, S., Mansi, T., Georgescu, B., Pop, M., Wright, G., Kamen, A., Comaniciu, D.: Lbm-ep: Lattice-Boltzmann method for fast cardiac electrophysiology simulation from 3D images. Med. Image Comput. Comput Assist. Interv. 15(2), 33–40 (2012)Google Scholar