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Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

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dc.contributor.author Doste Beltrán, Rubén
dc.contributor.author Lozano, Miguel
dc.contributor.author Jimenez-Perez, Guillermo
dc.contributor.author Mont, Lluís
dc.contributor.author Berruezo Sánchez, Antonio
dc.contributor.author Penela, Diego
dc.contributor.author Camara, Oscar
dc.contributor.author Sebastián Aguilar, Rafael, 1978-
dc.date.accessioned 2023-03-03T07:49:06Z
dc.date.available 2023-03-03T07:49:06Z
dc.date.issued 2022
dc.identifier.citation Doste R, Lozano M, Jimenez-Perez G, Mont L, Berruezo A, Penela D, Camara O, Sebastian R. Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias. Front Physiol. 2022;13:909372. DOI: 10.3389/fphys.2022.909372
dc.identifier.issn 1664-042X
dc.identifier.uri http://hdl.handle.net/10230/56026
dc.description.abstract In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.
dc.description.sponsorship This work has been funded by Generalitat Valenciana Grant AICO/2021/318 (Consolidables 2021) and Grant PID2020-114291RB-I00 funded by MCIN/10.13039/501100011033 and by “ERDF A way of making Europe”.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Frontiers in Physiology. 2022;13:909372.
dc.relation.isreferencedby https://www.frontiersin.org/articles/10.3389/fphys.2022.909372/full#supplementary-material
dc.rights © 2022 Doste, Lozano, Jimenez-Perez, Mont, Berruezo, Penela, Camara and Sebastian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fphys.2022.909372
dc.subject.keyword machine learning
dc.subject.keyword electrophysiological simulations
dc.subject.keyword outflow tract ventricular arrhythmias
dc.subject.keyword synthetic databases
dc.subject.keyword virtual population
dc.subject.keyword digital twin
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-114291RB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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