PubMed Health⌕ Search

PubMed · 10772782

Constructing a 3-D mesh model for electrical cardiac activity simulation.

Abstract

The 3-D ventricle model in this study was reconstructed from a series of MRI torso cross-section data. We used a 3-D voxel array to represent the ventricle. As in cardiac simulations proposed by previous studies, the activation sequence and body surface ECG were simulated in this model. But to reduce the amount of elements in the model, so that the amount of parameters in the model can be handled numerically, we propose another approach to simulate cardiac activity. A mesh model was constructed on the closed surface formed by epicardiac and endocardiac surfaces of the ventricle. We propose a method to simulate the activation sequence on the epicardiac and endocardiac surfaces of the mesh model. As with the uniform double layer theorem, body surface ECG can be estimated in terms of epicardiac and endocardiac surface current source. Consequently, we can also generate ECG waveforms corresponding to this mesh simulation. Both the depolarization sequence and ECG simulated by the mesh model resemble those generated by the 3-D voxel model. However, the mesh model greatly simplified the process of ECG simulation. Both the simulation of depolarization and ECG estimation were expressed in terms of clear and simple mathematical representations. Consequently, we can analytically investigate the effects of the mesh model's parameters on the cardiac activation sequence and ECG. It could be a useful tool to numerically study the relation of ECG waveforms and electrical activity of the heart.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C H Hsiao, T Kao. 2000. Constructing a 3-D mesh model for electrical cardiac activity simulation.. https://doi.org/10.1006/cbmr.1999.1528

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

Quantification of the hydrophobic interaction by simulations of the aggregation of small hydrophobic solutes in water.

The hydrophobic interaction, the tendency for nonpolar molecules to aggregate in solution, is a major driving force in biology. In a direct approach to the physical basis of the hydrophobic effect, nanosecond molecular dynamics simulations were performed on increasing numbers of hydrocarbon solute molecules in water-filled boxes of different sizes. The intermittent formation of solute clusters gives a free energy that is proportional to the loss in exposed molecular surface area with a constant of proportionality of 45 +/- 6 cal/mol A(2). The molecular surface area is the envelope of the solute cluster that is impenetrable by solvent and is somewhat smaller than the more traditional solvent-accessible surface area, which is the area transcribed by the radius of a solvent molecule rolled over the surface of the cluster. When we apply a factor relating molecular surface area to solvent-accessible surface area, we obtain 24 cal/mol A(2). Ours is the first direct calculation, to our knowledge, of the hydrophobic interaction from molecular dynamics simulations; the excellent qualitative and quantitative agreement with experiment proves that simple van der Waals interactions and atomic point-charge electrostatics account for the most important driving force in biology.

Computer Simulation↗