Multi-task Deep Neural Network for Intracardiac Activity Reconstruction in Atrial Fibrillation
The reconstruction of cardiac mapping for arrhythmia characterization remains a challenge for both invasive and non-invasive approaches.This paper presents a novel data-driven approach using a multi-task deep neural network for the reconstruction of intracardiac activity from body surface potentials in atrial fibrillation patients.The proposed framework integrates 3D convolutions and Recurrent Neural Networks for spatio-temporal feature extraction from body surface potentials, enhancing the reconstruction of electrograms.Performance was evaluated against the Tikhonov method in synthetic and real data, showing robustness to noise and electrode disconnection.
Authors
- Miguel Ángel Cámara-Vázquez (ORCID: https://orcid.org/0000-0002-1861-1585)
- Óscar Barquero‐Pérez (ORCID: https://orcid.org/0000-0002-7235-3986)
- Carlos Fambuena Santos
- Ismael Hernández‐Romero (ORCID: https://orcid.org/0000-0002-0525-7476)
- Andreu M. Climent (ORCID: https://orcid.org/0000-0002-7260-8811)
- Miriam Gutiérrez Fernández-Calvillo
- María S. Guillem (ORCID: https://orcid.org/0000-0001-5660-3693)
- Karen Lopez Linares
Publication Details
- Journal
- Computing in cardiology
- Published
- 2024-12-01
- DOI
- https://doi.org/10.22489/cinc.2024.410
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Comunidad de Madrid
- Ministerio de Ciencia, Innovación y Universidades
- European Commission
- Universidad Rey Juan Carlos
- Instituto de Salud Carlos III
- European Regional Development Fund
- EIT Health