KIT | KIT-Bibliothek | Impressum | Datenschutz

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

Sánchez, Jorge 1; Luongo, Giorgio 1; Nothstein, Mark 1; Unger, Laura A. 1; Saiz, Javier; Trenor, Beatriz; Luik, Armin; Dössel, Olaf 1; Loewe, Axel ORCID iD icon 1
1 Institut für Biomedizinische Technik (IBT), Karlsruher Institut für Technologie (KIT)


In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000136132
Veröffentlicht am 12.08.2021
DOI: 10.3389/fphys.2021.699291
Zitationen: 15
Web of Science
Zitationen: 11
Zitationen: 16
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 05.07.2021
Sprache Englisch
Identifikator ISSN: 1664-042X
KITopen-ID: 1000136132
Erschienen in Frontiers in Physiology
Verlag Frontiers Media SA
Band 12
Seiten Art.-Nr. 699291
Nachgewiesen in Scopus
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page