Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality

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dc.contributor.affiliationBecerra M.A., Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Medellín, 050034, Colombia
dc.contributor.affiliationPeluffo-Ordoñez D.H., Modeling, Simulation and Data Analysis (MSDA) Research Program, VI Polytechnic University, Mohammed, Ben Guerir, 43150, Morocco, Smart Data Analysis Systems Group (SDAS), 43150, Ben Guerir, Morocco
dc.contributor.affiliationVela J., Smart Data Analysis Systems Group (SDAS), 43150, Ben Guerir, Morocco
dc.contributor.affiliationMejía C., Instituto Tecnológico Metropolitano, Medellín, 050013, Colombia
dc.contributor.affiliationUgarte J.P., Grupo de Investigación en Modelado y Simulación Computacional (GIMSC), Facultad de Ingenierías, Universidad de San Buenaventura, Medellín, 051053, Colombia
dc.contributor.affiliationTobón C., Materiales Nanoestructurados y Biomodelación (MATBIOM), Universidad de Medellín, Medellín, 050026, Colombia
dc.contributor.authorBecerra M.A.
dc.contributor.authorPeluffo-Ordoñez D.H.
dc.contributor.authorVela J.
dc.contributor.authorMejía C.
dc.contributor.authorUgarte J.P.
dc.contributor.authorTobón C.
dc.date.accessioned2025-09-08T14:23:51Z
dc.date.available2025-09-08T14:23:51Z
dc.date.issued2025
dc.descriptionPersistent atrial fibrillation (AF), a prevalent cardiac arrhythmia, is primarily sustained by rotor-type reentries, with their localization crucial for successful ablation treatment. Fractionated atrial electrogram (EGM) signals have been associated with the tips of the rotors and are thus considered as ablation targets. However, the typical noise problems of physiological signals affect the results of EGM processing tools, and consequently the ablation outcome. This study proposes a data fusion framework based on the Joint Directors of Laboratories model with six levels and information quality (IQ) assessment for locating rotor tips from EGMs simulated in a two-dimensional model of human atrial tissue under AF conditions. Validation tests were conducted using a set of 13 IQ criteria and their corresponding metrics. First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. The signals were then preprocessed, and five statistical features (sample entropy, approximate entropy, Shannon entropy, mean amplitude, and standard deviation) were extracted to generate rotor location maps using a wavelet fusion technique. Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. The proposed framework outperformed existing EGM-based rotor detection methods, demonstrating superior functionality and performance compared to existing EGM-based rotor detection methods. It achieved an accuracy of approximately 90%, with improvements of up to 10% through tuning and adjustments based on IQ variables, aligned with higher-level system requirements. The novelty of this approach lies in evaluating the IQ across signal-processing stages and optimizing it through data fusion to enhance rotor tip position estimation. This advancement could help specialists make more informed decisions in EGM acquisition and treatment application. © 2025 by the authors.
dc.identifier.doi10.3390/app15073665
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn20763417
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9115
dc.language.isoeng
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.relation.citationissue7
dc.relation.citationvolume15
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105002280240&doi=10.3390%2fapp15073665&partnerID=40&md5=1fcf685776df475801d10d7b0988f880
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dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceApplied Sciences (Switzerland)
dc.sourceAppl. Sci.
dc.sourceScopus
dc.subjectData fusion
dc.subjectElectrogram
dc.subjectInformation quality
dc.subjectJDL model
dc.subjectData fusion
dc.subjectDiseases
dc.subjectElectrocardiography
dc.subjectFailure analysis
dc.subjectRisk assessment
dc.subjectAtrial fibrillation
dc.subjectCardiac arrhythmia
dc.subjectDetection methods
dc.subjectElectrograms
dc.subjectFusion quality
dc.subjectInformation quality
dc.subjectJDL model
dc.subjectLocalisation
dc.subjectQuality criteria
dc.subjectRotor tip
dc.subjectAblation
dc.titleRotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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