arritmic3d 3.0b7

Fast eikonal solver for electrophysiology simulation

Arritmic3D: A fast Eikonal computational model for electrophysiology

simulation CoMMLab 2026-01-01

• Installation
• Compilation from source
• Usage
• Citing Arritmic3D
• Related research

Arritmic3D is a fast Eikonal computational model for electrophysiology simulation.

The simulator has three versions in separate branches: branch main (this branch), with a development version, branch ventricle, with the ventricle version, and branch atria, with the atria version. Checkout the desired branch before proceeding.

[!WARNING]

This development branch is not fully functional and has some differences in the diffusion model with respect to the other two branches. Thus, this branch is not validated and should not be used for research purposes yet.

The original version of the solver was developed in the Java environment Processing. Now it is being migrated to C++ and provided with a Python interface.

Installation

Arritmic3D is available as a Python package in PyPI. From a python environment, you can install the package using pip:

pip install arritmic3d

This will also install some convenience scripts that can be used from outside the python environment, as command line tools (see the usage section).

Compilation from source

You can also compile the wheel yourself. To compile it, you need a standard C++17 compiler, Eigen and Pybind11 installed in your system.

As a reference, in an ubuntu machine it should be enough to run:

sudo apt-get install \
    build-essential \
    pkg-config \
    cmake \
    python3-dev \
    gcc \
    g++ \
    libeigen3-dev \
    python3-pybind11

Then, clone the repository from GitHub and compile the wheel by running:

python -m pip install --upgrade pip setuptools wheel
python -m pip install .

Usage

The solver is presented as a C++ library with is wrapped in a Python module. In addition, a Python script to run simulations is provided as an executable called arritmic3d. This script is meant to provide a reather flexible way to run simulations in many common scenarios.

However, for advanced usage, you can build your own Python programs that use the Arritmic3D module. You can find the Arritmic3D API documentation here.

A guide on the usage of the arritmic3d script and other related tools can be found in the web page.

Citing Arritmic3D

If you find Arritmic3D useful for your research, please cite the following paper:

Serra, Dolors, Pau Romero, Ignacio Garcia-Fernandez, Miguel Lozano, Alejandro Liberos, Miguel Rodrigo, Alfonso Bueno-Orovio, Antonio Berruezo, and Rafael Sebastian. 2022. “An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility.” Mathematics 10 (8): 1293. https://doi.org/10.3390/math10081293.

Related research

Romitti, Giada S., Alejandro Liberos, María Termenón-Rivas, Javier Barrios-Álvarez De Arcaya, Dolors Serra, Pau Romero, David Calvo, et al. 2025. “Implementation of a Cellular Automaton for Efficient Simulations of Atrial Arrhythmias.” Medical Image Analysis 101 (April): 103484. https://doi.org/10.1016/j.media.2025.103484.

Serra, D., P. Franco, P. Romero, G. Romitti, I. García-Fernández, M. Lozano, A. Liberos, et al. 2023. “Assessment of Risk for Ventricular Tachycardia Based on Extensive Electrophysiology Simulations.” In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1–4. Sydney, Australia: IEEE. https://doi.org/10.1109/EMBC40787.2023.10340169.

Serra, Dolors, Paula Franco, Pau Romero, Ignacio García-Fernández, Miguel Lozano, David Soto, Diego Penela, Antonio Berruezo, Oscar Camara, and Rafael Sebastian. 2022. “Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels.” In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, edited by Oscar Camara, Esther Puyol-Antón, Chen Qin, Maxime Sermesant, Avan Suinesiaputra, Shuo Wang, and Alistair Young, 13593:56–64. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-23443-9_6.

Serra, Dolors, Pau Romero, Paula Franco, Ignacio Bernat, Miguel Lozano, Ignacio Garcia-Fernandez, David Soto, Antonio Berruezo, Oscar Camara, and Rafael Sebastian. 2025. “Unsupervised Stratification of Patients With Myocardial Infarction Based on Imaging and In-Silico Biomarkers.” IEEE Transactions on Medical Imaging 44 (12): 4762–74. https://doi.org/10.1109/TMI.2025.3582383.

Serra, Dolors, Pau Romero, Miguel Lozano, Ignacio Garcia-Fernandez, Diego Penela, Antonio Berruezo, Oscar Camara, Miguel Rodrigo, Miriam Gil, and Rafael Sebastian. 2024. “Patient Stratification Based on Fast Simulation of Cardiac Electrophysiology on Digital Twins.” In Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers, edited by Oscar Camara, Esther Puyol-Antón, Maxime Sermesant, Avan Suinesiaputra, Qian Tao, Chengyan Wang, and Alistair Young, 14507:35–43. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-52448-6_4.