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Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG

Nagel, Claudia; Dössel, Olaf; Loewe, Axel ORCID iD icon


Features extracted from P waves of the 12-lead electrocardiogram (ECG) have proven valuable for non-invasively estimating the left atrial fibrotic volume fraction associated with the arrhythmogenesis of atrial fibrillation. However, feature extraction in the clinical context is prone to errors and oftentimes yields unreliable results in the presence of noise. This leads to inaccurate input values provided to machine learning algorithms tailored at estimating the amount of atrial fibrosis with clinical ECGs.Another important aspect for clinical translation is the network’s generalization ability regarding newECGs.To quantify a network’s sensitivity to inaccurately extracted P wave features, we added Gaussian noise to the features extracted from 540,000 simulated ECGs consisting of P wave duration, dispersion, terminal force in lead V1, peak-to-peak amplitudes, and additionallythoracic and atrial volumes. For assessing generalization, we evaluated the network performance for train-validation-test splits divided such that ECGs simulated with the same atria or torso geometry only belongedto either the trainingand validationor the test set. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000141730
Veröffentlicht am 13.01.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2364-5504
KITopen-ID: 1000141730
Erschienen in Current Directions in Biomedical Engineering
Verlag De Gruyter
Band 7
Heft 2
Seiten 307-310
Schlagwörter atrial fibrosis, neural networks, regression, sensitivity, generalization, Pwaves, ECG simulations
Nachgewiesen in Dimensions
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