Matthijs J. M. Cluitmans

Orcid: 0000-0002-3456-7668

Affiliations:
  • Maastricht University, The Netherlands


According to our database1, Matthijs J. M. Cluitmans authored at least 24 papers between 2012 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2023
Isogeometric-Mechanics-Driven Electrophysiology Simulations of Ventricular Tachycardia.
Proceedings of the Functional Imaging and Modeling of the Heart, 2023

Sensitivity of Repolarization Gradients to Infarct Borderzone Properties Assessed with the Ten Tusscher and Modified Mitchell-Schaeffer Model.
Proceedings of the Functional Imaging and Modeling of the Heart, 2023

2022
Reducing Line-of-Block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods.
IEEE Trans. Biomed. Eng., 2022

2021
Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology.
Comput. Biol. Medicine, 2021

Dynamics of Ventricular Electrophysiology Are Unmasked Through Noninvasive Electrocardiographic Imaging.
Proceedings of the Computing in Cardiology, CinC 2021, Brno, 2021

Electrocardiographic Imaging of Sinus Rhythm in Pig Hearts Using Bayesian Maximum A Posteriori Estimation.
Proceedings of the Computing in Cardiology, CinC 2021, Brno, 2021

2020
Variability of Electrocardiographic Imaging Within and Between Leadsets.
Proceedings of the Computing in Cardiology, 2020

An Open-Source Algorithm for Standardized Bullseye Visualization of High-Resolution Cardiac Ventricular Data: UNISYS.
Proceedings of the Computing in Cardiology, 2020

Relation of Surface T-wave to Vulnerability to Ventricular Fibrillation in Explanted Structurally Normal Hearts.
Proceedings of the Computing in Cardiology, 2020

CT-Scan Free Neural Network-Based Reconstruction of Heart Surface Potentials From ECG Recordings.
Proceedings of the Computing in Cardiology, 2020

2019
The Influence of Using a Static Diastolic Geometry in ECG Imaging.
Proceedings of the 46th Computing in Cardiology, 2019

Comparison of Activation Times Estimation for Potential-Based ECG Imaging.
Proceedings of the 46th Computing in Cardiology, 2019

Personalized Ventricular Arrhythmia Simulation Framework to Study Vulnerable Trigger Locations on Top of Scar Substrate.
Proceedings of the 46th Computing in Cardiology, 2019

2018
Wavelet-promoted sparsity for non-invasive reconstruction of electrical activity of the heart.
Medical Biol. Eng. Comput., 2018

Personalized Computational Framework to Study Arrhythmia Mechanisms on Top of ECG Image-Detected Substrate.
Proceedings of the Computing in Cardiology, 2018

2017
Physiology-based regularization of the electrocardiographic inverse problem.
Medical Biol. Eng. Comput., 2017

Integration of Electrical, Structural, and Anatomical Imaging for the Guidance of Cardiac Resynchronization Therapy.
Proceedings of the Computing in Cardiology, 2017

Influence of Body-Surface Geometry Accuracy on Noninvasive Reconstruction of Electrical Activation and Recovery in Electrocardiographic Imaging.
Proceedings of the Computing in Cardiology, 2017

2016
Spatiotemporal Activation Time Estimation Improves Noninvasive Localization of Cardiac Electrical Activity.
Proceedings of the Computing in Cardiology, CinC 2016, Vancouver, 2016

2015
In-vivo Evaluation of Reduced-Lead-Systems in Noninvasive Reconstruction and Localization of Cardiac Electrical Activity.
Proceedings of the Computing in Cardiology, 2015

2014
Physiology-based Regularization Improves Noninvasive Reconstruction and Localization of Cardiac Electrical Activity.
Proceedings of the Computing in Cardiology, CinC 2014, 2014

2013
Wavelet-sparsity based regularization over time in the inverse problem of electrocardiography.
Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

Inverse Reconstruction of Epicardial Potentials Improves by Vectorcardiography and Realistic Potentials.
Proceedings of the Computing in Cardiology, 2013

2012
Realistic training data improve noninvasive reconstruction of heart-surface potentials.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012


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