BPX preconditioners for the Bidomain model of electrocardiology

Ottino, D.; Scacchi, Simone

doi:10.1016/j.cam.2015.02.011

Cited by 2 publications

(1 citation statement)

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“…In such cases full solutions remain the method of choice and a relatively long solution time will have to be accepted. Although new developments in scalable preconditioners may improve the situation somewhat (Munteanu et al, 2009 ; Ottino and Scacchi, 2015 ), it is unlikely that full solvers will ever scale as well as an ECG computation based on lead fields.…”

Section: Discussionmentioning

confidence: 99%

Scalable and Accurate ECG Simulation for Reaction-Diffusion Models of the Human Heart

Potse

2018

Front. Physiol.

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Realistic electrocardiogram (ECG) simulation with numerical models is important for research linking cellular and molecular physiology to clinically observable signals, and crucial for patient tailoring of numerical heart models. However, ECG simulation with a realistic torso model is computationally much harder than simulation of cardiac activity itself, so that many studies with sophisticated heart models have resorted to crude approximations of the ECG. This paper shows how the classical concept of electrocardiographic lead fields can be used for an ECG simulation method that matches the realism of modern heart models. The accuracy and resource requirements were compared to those of a full-torso solution for the potential and scaling was tested up to 14,336 cores with a heart model consisting of 11 million nodes. Reference ECGs were computed on a 3.3 billion-node heart-torso mesh at 0.2 mm resolution. The results show that the lead-field method is more efficient than a full-torso solution when the number of simulated samples is larger than the number of computed ECG leads. While the initial computation of the lead fields remains a hard and poorly scalable problem, the ECG computation itself scales almost perfectly and, even for several hundreds of ECG leads, takes much less time than the underlying simulation of cardiac activity.

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Section: Discussionmentioning

confidence: 99%

Scalable and Accurate ECG Simulation for Reaction-Diffusion Models of the Human Heart

Potse

2018

Front. Physiol.

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Efficient Numerical Solution of the EMI Model Representing the Extracellular Space (E), Cell Membrane (M) and Intracellular Space (I) of a Collection of Cardiac Cells

et al. 2021

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The EMI model represents excitable cells in a more accurate manner than traditional homogenized models at the price of increased computational complexity. The increased complexity of solving the EMI model stems from a significant increase in the number of computational nodes and from the form of the linear systems that need to be solved. Here, we will show that the latter problem can be solved by careful use of operator splitting of the spatially coupled equations. By using this method, the linear systems can be broken into sub-problems that are of the classical type of linear, elliptic boundary value problems. Therefore, the vast collection of methods for solving linear, elliptic partial differential equations can be used. We demonstrate that this enables us to solve the systems using shared-memory parallel computers. The computing time scales perfectly with the number of physical cells. For a collection of 512 × 256 cells, we solved linear systems with about 2.5×108 unknows. Since the computational effort scales linearly with the number of physical cells, we believe that larger computers can be used to simulate millions of excitable cells and thus allow careful analysis of physiological systems of great importance.

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BPX preconditioners for the Bidomain model of electrocardiology

Cited by 2 publications

References 64 publications

Scalable and Accurate ECG Simulation for Reaction-Diffusion Models of the Human Heart

Scalable and Accurate ECG Simulation for Reaction-Diffusion Models of the Human Heart

Efficient Numerical Solution of the EMI Model Representing the Extracellular Space (E), Cell Membrane (M) and Intracellular Space (I) of a Collection of Cardiac Cells

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