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Multiscale Cohort Modeling of Atrial Electrophysiology Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

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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 fib...

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Bibliographic Details
Main Author: Nagel, Claudia (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2023
Series:Karlsruhe transactions on biomedical engineering 25
Subjects:
Electrical engineering
Electrophysiologische Modellierung und Simulation; Elektrokardiogramm; Maschinelles Lernen; Vorhofflimmern; Statistisches Shape Modell; electrophysiological modeling and simulation; electrocardiogram; machine learning; atrial fibrillation; statistical shape model
Online Access:OAPEN Library: download the publication
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Summary: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.
Physical Description:1 electronic resource (280 p.)
ISBN:KSP/1000155927
Access:Open Access

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