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.
KeywordsPartial Little Square Regression Action Potential Duration Action Potential Amplitude Principal Component Analysis Component Diastolic Interval
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