Publications associées à Exploration électrophysiologique

An integrated heart-torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation

Alfio Quarteroni, Francesco Regazzoni

When generating in-silico clinical electrophysiological outputs, such as electrocardiograms (ECGs) and body surface potential maps (BSPMs), mathematical models have relied on single physics, i.e. of the cardiac electrophysiology (EP), neglecting the role o ...

Elsevier Science Sa2024

A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins

Jean-Marc Vesin, Adrian Luca, Vincent Schlageter

Background Superimposition of farfield (FF) and nearfield (NF) bipolar voltage electrograms (BVE) complicates the confirmation of pulmonary vein (PV) isolation after catheter ablation of atrial fibrillation. Our aim was to develop an automatic algorithm ba ...

SPRINGER2023

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Alfio Quarteroni, Simone Deparis, Riccardo Tenderini, Stefano Pagani

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by ...

2022

Ultra-high voltage, low power and energy recovering electronics for dielectric elastomer actuators

Raphaël Johannes Charles Mottet

Heart failure is a cardiovascular disease affecting between 1 and 2% of the population of developed countries alone - more than 10 million people - with this number bound to increase due to the ageing of the population. This condition starts to develop its ...

EPFL2022

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

Alfio Quarteroni, Andrea Manzoni

The numerical simulation of multiple scenarios easily becomes computationally prohibitive for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models (FOMs). Likewise, the use of traditional reduced order models (ROMs) ...

FRONTIERS MEDIA SA2021

Data integration for the numerical simulation of cardiac electrophysiology

Alfio Quarteroni, Andrea Manzoni, Luca Dede', Stefano Pagani

The increasing availability of extensive and accurate clinical data is rapidly shaping cardiovascular care by improving the understanding of physiological and pathological mechanisms of the cardiovascular system and opening new frontiers in designing thera ...

WILEY2021

A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation

Alfio Quarteroni, Andrea Manzoni

In the context of cardiac electrophysiology, we propose a novel computational approach to highlight and explain the long-debated mechanisms behind atrial fibrillation (AF) and to reliably numerically predict its induction and sustainment. A key role is pla ...

FRONTIERS MEDIA SA2021

Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

Alexandre Schmid, Lizeth Gonzalez Carabarin

Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Th ...

IEEE2021

Deep learning-based reduced order models in cardiac electrophysiology

Alfio Quarteroni, Andrea Manzoni

Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depola ...

PUBLIC LIBRARY SCIENCE2020

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Exploration électrophysiologique

An integrated heart-torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation

A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins

Finding information-rich electrocardiographic biomarkers to characterize atrial fibrillation.

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Ultra-high voltage, low power and energy recovering electronics for dielectric elastomer actuators

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

Data integration for the numerical simulation of cardiac electrophysiology

A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation

Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

Deep learning-based reduced order models in cardiac electrophysiology

Graph Chatbot

Chattez avec Graph Search

Data integration for the numerical simulation of cardiac electrophysiology

Deep learning-based reduced order models in cardiac electrophysiology

An integrated heart-torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation

Ultra-high voltage, low power and energy recovering electronics for dielectric elastomer actuators

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins

A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation

Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Finding information-rich electrocardiographic biomarkers to characterize atrial fibrillation.