Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms
Abstract:
An early detection and diagnosis of atrial fibrillation sets the course for timely intervention to prevent potentially occurring comorbidities. Electrocardiogram data resulting from electrophysiological cohort modeling and simulation can be a valuable data resource for improving automated atrial fibrillation risk stratification with machine learning techniques and thus, reduces the risk of stroke in affected patients.
| Zugehörige Institution(en) am KIT | Institut für Biomedizinische Technik (IBT) |
| Publikationstyp | Hochschulschrift |
| Publikationsjahr | 2023 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-7315-1281-3 ISSN: 1864-5933 KITopen-ID: 1000155927 |
| Umfang | XII, 252 S. |
| Serie | Karlsruhe transactions on biomedical engineering / Ed.: Karlsruhe Institute of Technology / Institute of Biomedical Engineering ; 27 |
| Art der Arbeit | Dissertation |
| Fakultät | Fakultät für Elektrotechnik und Informationstechnik (ETIT) |
| Institut | Institut für Biomedizinische Technik (IBT) |
| Prüfungsdaten | 17.11.2022 |
| Prüfungsdatum | 17.11.2022 |
| Schlagwörter | Electrophysiologische Modellierung und Simulation, Elektrokardiogramm, Maschinelles Lernen, Vorhofflimmern, Statistisches Shape Modell, electrophysiological modeling and simulation, electrocardiogram, machine learning, atrial fibrillation, statistical shape model |
| Relationen in KITopen | |
| Referent/Betreuer | Loewe, Axel Rodriguez, Blanca Dössel, Olaf |