Non-stationary components in Electrograms localize arrhythmogenic substrates in a 3D model of human atria

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dc.contributor.affiliationGómez-Echavarría A., MATBIOM, Universidad de Medellín, Medellin, Colombia
dc.contributor.affiliationUgarte J.P., GIMSC, Universidad de San Buenaventura, Medellín, Colombia
dc.contributor.affiliationTobón C., MATBIOM, Universidad de Medellín, Medellin, Colombia
dc.contributor.authorGómez-Echavarría A.
dc.contributor.authorUgarte J.P.
dc.contributor.authorTobón C.
dc.date.accessioned2025-09-08T14:23:42Z
dc.date.available2025-09-08T14:23:42Z
dc.date.issued2025
dc.descriptionCatheter ablation, as a treatment for atrial fibrillation (AF), often yields low success rates in the advanced stages of the arrhythmia. Ablation procedures are guided by atrial mapping using electrogram (EGM) signals, which reflect local electrical activations. The primary goal is to identify arrhythmogenic mechanisms, such as rotors, to serve as ablation targets. Given the chaotic nature of AF propagation, these electrical activations occur at variable rates. This work introduces a novel signal processing approach based on the fractional Fourier transform (FrFT) to characterize the non-stationary content in EGM signals. A 3D biophysical and anatomical model of human atria was used to simulate AF, and unipolar EGMs were calculated. The FrFT-based algorithm was applied to all EGM signals, estimating the optimal FrFT order to capture linear frequency modulations. Electroanatomical maps of these optimal FrFT orders were generated. Results revealed that the AF EGMs exhibit non-stationarity, which can be characterized using the FrFT. Rotors displayed a distinct pattern of non-stationarity, allowing for dynamic tracking, while transient mechanisms were identifiable through variations in the FrFT order, showing different patterns than those of rotors. As a generalization of the classical Fourier analysis, FrFT mapping offers clinically interpretable insights into the rate of change in EGM frequency content over time. This method proves valuable for characterizing AF spatiotemporal dynamics by leveraging the non-stationary information inherent in fibrillatory propagation. © 2025 Elsevier Ltd
dc.identifier.doi10.1016/j.compbiomed.2025.110126
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn104825
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9090
dc.language.isoeng
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.relation.citationvolume192
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105003221256&doi=10.1016%2fj.compbiomed.2025.110126&partnerID=40&md5=4c3ceb23a00501091d59b0a5abef87b1
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dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceComputers in Biology and Medicine
dc.sourceComput. Biol. Med.
dc.sourceScopus
dc.subjectCardiac computational modeling
dc.subjectElectroanatomical maps
dc.subjectFractional Fourier transform
dc.subjectMetaheuristics optimization
dc.subjectNon-stationary signals
dc.subjectElectrocardiograms
dc.subjectFourier analysis
dc.subjectFourier transforms
dc.subjectFrequency modulation
dc.subjectAtrial fibrillation
dc.subjectCardiac computational modeling
dc.subjectComputational modelling
dc.subjectElectroanatomical map
dc.subjectElectrograms
dc.subjectFractional Fourier transforms
dc.subjectMetaheuristic optimization
dc.subjectNonstationary
dc.subjectNonstationary signals
dc.subjectTransform order
dc.subjectAblation
dc.titleNon-stationary components in Electrograms localize arrhythmogenic substrates in a 3D model of human atria
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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