Publicaciones en las que colabora con M. Rodrigo (13)

2023

  1. Assessment of Risk for Ventricular Tachycardia based on Extensive Electrophysiology Simulations

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, Vol. 2023, pp. 1-4

  2. Cellular Automata for Fast Simulations of Arrhythmogenic Atrial Substrate

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Characterization of the Electrophysiological Characteristics of Chronic Atrial Fibrillation for Efficient Simulations

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, Vol. 2023, pp. 1-4

  4. Evaluation of Automata-Based Simulations for Atrial Fibrillation in 2D/3D Geometries Reproducing Disease Progression

    Computing in Cardiology

  5. Role of Fiber Direction and Ionic Heterogeneities in Atrial Arrhythmia Simulations

    Computing in Cardiology

  6. Role of Heterogeneous Ionic Profiles in Atrial Fibrillation Propagation. A Population of Models Study

    Computing in Cardiology

  7. Substrate-Specific Simulations of Atrial Fibrillation Reproducing Electrophysiological Clinical Markers

    Computing in Cardiology

2022

  1. An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility

    Mathematics, Vol. 10, Núm. 8

  2. Machine Learning Based Cell Model for Fast Approximation of Cellular Action Potential to Enable Clinical Translation

    Computing in Cardiology

  3. The Effects of Long- and Short-term Memory on Action Potential Duration for Atrial Cellular Automata

    Computing in Cardiology

2021

  1. Arrhythmic3D: A fast automata-based tool to simulate and assess arrhythmia risk in 3D ventricular models

    Computing in Cardiology

  2. Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Simplified Electrophysiology Modeling Framework to Assess Ventricular Arrhythmia Risk in Infarcted Patients

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)