ERA-CVD Verbund Medic V-Tach: Mobile electrocardiographic imaging for the prediction of ventricular tachycardia

Project: Projects with Federal FundingProjects with Federal Ministry Funding: BMBF

Project Details

Description

Patients with heart failure are at risk of developing ventricular tachycardia (VT). VT is responsible for about 80% of sudden cardiac deaths. There is therefore a great need for research to develop better stratification of patients at risk of VT. The possibility of fusing imaging and electrophysiological data, electrocardiographic imaging (ECGI), has recently been explored to enable non-invasive visualization of cardiac electrophysiology.
ECGI is a promising field of research for various cardiovascular applications and in particular for the risk stratification of VT. The aim of the consortium is to develop a novel technology, the mobile ECGI, which enables the merging of electrocardiogram (ECG) data, mechanical information of the heart from magnetic resonance imaging (MRI) and rhythm information from portable long-term ECG systems. The project will address the following issues: - the development and adaptation of a learning algorithm for the classification of ECG data with CNNs and recurrent networks, - the creation and optimization of a "deep learning" method for the nonlinear interpatient registration of MRI heart images with output of uncertainties, - the completion of the algorithm for graph-CNN-based extraction of surface grids of the heart, - the implementation and evaluation of an algorithm for learning-based estimation of surface motion, - the implementation of a geometric deep learning algorithm for finding 4D heart correspondences and evaluation of a clustering approach for subdividing patient data into similar motion patterns, - the development of inverse solution of electrical heart activity for moving models and retrospective proof of concept of the mobile ECGI method.
Statusfinished
Effective start/end date01.01.2031.03.24

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 9 - Industry, Innovation, and Infrastructure

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 205-07 Medical Informatics and Medical Bioinformatics

Funding Institution

  • Federal Ministries: BMBF (Education and Research)