Mostrar el registro sencillo del ítem

Fractional generalization of entropy improves the characterization of rotors in simulated atrial fibrillation

dc.contributor.authorUgarte J.P
dc.contributor.authorTenreiro Machado J.A
dc.contributor.authorTobón C.
dc.date.accessioned2022-09-14T14:33:46Z
dc.date.available2022-09-14T14:33:46Z
dc.date.created2022
dc.identifier.issn963003
dc.identifier.urihttp://hdl.handle.net/11407/7466
dc.descriptionAtrial fibrillation (AF) underlies disordered spatiotemporal electrical activity, that increases in complexity with the persistence of the arrhythmia. It has been hypothesized that a specific arrhythmogenic mechanism, known as rotor, is the main driver sustaining the AF. Thus, the ablation of rotors has been suggested as a therapeutic strategy to terminate the arrhythmia. Nonetheless, such strategy poses a problem related with the characterization of the rotor propagating activity. This work addresses the rotor characterization by means of a fractional generalization of the entropy concept. By adopting complex order derivative operators, we endorse the definition of information content. The derived metric is used to study the AF propagation dynamics in computational models. The results evince that the fractional entropy approach yields a better spatio-temporal characterization of rotor dynamics than the conventional entropy analysis, under a wide range of simulated fibrillation conditions. © 2022 The Author(s)eng
dc.language.isoeng
dc.publisherElsevier Inc.
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85126290829&doi=10.1016%2fj.amc.2022.127077&partnerID=40&md5=4445300d05459e5e2180a6cf154cf62e
dc.sourceApplied Mathematics and Computation
dc.titleFractional generalization of entropy improves the characterization of rotors in simulated atrial fibrillation
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programCiencias Básicas
dc.type.spaArtículo
dc.identifier.doi10.1016/j.amc.2022.127077
dc.subject.keywordAtrial fibrillationeng
dc.subject.keywordElectrograms signal processingeng
dc.subject.keywordFractional entropyeng
dc.subject.keywordRotorseng
dc.subject.keywordScientific computingeng
dc.subject.keywordDiseaseseng
dc.subject.keywordSignal processingeng
dc.subject.keywordAtrial fibrillationeng
dc.subject.keywordComplex-order derivativeseng
dc.subject.keywordElectrical activitieseng
dc.subject.keywordElectrogram signal processingeng
dc.subject.keywordElectrogramseng
dc.subject.keywordEntropy concepteng
dc.subject.keywordFractional entropyeng
dc.subject.keywordFractional generalizationeng
dc.subject.keywordSignal-processingeng
dc.subject.keywordTherapeutic strategyeng
dc.subject.keywordEntropyeng
dc.relation.citationvolume425
dc.publisher.facultyFacultad de Ciencias Básicas
dc.affiliationUgarte, J.P., GIMSC, Universidad de San Buenaventura, Medellín, Colombia
dc.affiliationTenreiro Machado, J.A., Department of Electrical Engineering, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
dc.affiliationTobón, C., MATBIOM, Universidad de Medellín, Medellín, Colombia
dc.relation.referencesHeijman, J., Linz, D., Schotten, U., Dynamics of atrial fibrillation mechanisms and comorbidities (2021) Annu. Rev. Physiol., 83, pp. 83-106
dc.relation.referencesBhardwaj, R., Koruth, J.S., Novel ablation approaches for challenging atrial fibrillation cases (mapping, irrigation, and catheters) (2019) Cardiol. Clin., 37 (2), pp. 207-219
dc.relation.referencesWijesurendra, R.S., Casadei, B., Mechanisms of atrial fibrillation (2019) Heart, 105 (24), pp. 1860-1867
dc.relation.referencesJalife, J., Berenfeld, O., Mansour, M., Mother rotors and fibrillatory conduction: a mechanism of atrial fibrillation (2002) Cardiovasc. Res., 54 (2), pp. 204-216
dc.relation.referencesHanda, B.S., Roney, C.H., Houston, C., Qureshi, N.A., Li, X., Pitcher, D.S., Chowdhury, R.A., Ng, F.S., Analytical approaches for myocardial fibrillation signals (2018) Comput. Biol. Med., 102 (July), pp. 315-326
dc.relation.referencesCiaccio, E.J., Biviano, A.B., Iyer, V., Garan, H., Differences in continuous spectra of fractionated electrograms in paroxysmal versus persistent atrial fibrillation (2016) Comput. Biol. Med., 76, pp. 50-59
dc.relation.referencesCiaccio, E.J., Biviano, A.B., Whang, W., Vest, J.A., Gambhir, A., Einstein, A.J., Garan, H., Differences in repeating patterns of complex fractionated left atrial electrograms in longstanding persistent atrial fibrillation as compared with paroxysmal atrial fibrillation (2011) Circulation, 4 (4), pp. 470-477
dc.relation.referencesGanesan, A.N., Kuklik, P., Lau, D.H., Brooks, A.G., Baumert, M., Lim, W.W., Thanigaimani, S., Sanders, P., Bipolar electrogram Shannon entropy at sites of rotational activation: implications for abaltion of atrial fibrillation (2013) Circulation, pp. 48-57
dc.relation.referencesAnnoni, E.M., Arunachalam, S.P., Kapa, S., Mulpuru, S.K., Friedman, P.A., Tolkacheva, E.G., Novel quantitative analytical approaches for rotor identification and associated implications for mapping (2018) IEEE Trans. Biomed. Eng., 65 (2), pp. 273-281
dc.relation.referencesGanesan, A.N., Kuklik, P., Gharaviri, A., Brooks, A., Chapman, D., Lau, D.H., Roberts-Thomson, K.C., Sers, P., Origin and characteristics of high Shannon entropy at the pivot of locally stable rotors: insights from computational simulation (2014) PLoS One, 9 (11)
dc.relation.referencesMachado, J.T., Fractional order generalized information (2014) Entropy, 16 (4), pp. 2350-2361
dc.relation.referencesTenreiro Machado, J.A., Fractal and entropy analysis of the Dow Jones index using multidimensional scaling (2020) Entropy, 22 (10), pp. 1-18
dc.relation.referencesSantos, A.D.F., Valério, D., Tenreiro Machado, J.A., Lopes, A.M., A fractional perspective to the modelling of Lisbon's public transportation network (2019) Transportation, 46 (5), pp. 1893-1913
dc.relation.referencesZhang, Y., Shang, P., Xiong, H., Multivariate generalized information entropy of financial time series (2019) Phys. A, 525, pp. 1212-1223
dc.relation.referencesLopes, A.M., Tenreiro Machado, J.A., Integer and fractional-order entropy analysis of earthquake data series (2016) Nonlinear Dyn., 84 (1), pp. 79-90
dc.relation.referencesMachado, J.T., Fractional derivatives and negative probabilities (2019) Commun. Nonlinear Sci. Numer. Simul., 79, p. 104913
dc.relation.referencesShim, J., Arkin, R.C., A taxonomy of robot deception and its benefits in HRI (2013) Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, pp. 2328-2335
dc.relation.referencesUgarte, J.P., Tobón, C., Lopes, A.M., Tenreiro Machado, J.A., Atrial rotor dynamics under complex fractional order diffusion (2018) Front. Physiol., 9 (JUL), pp. 1-14
dc.relation.referencesCourtemanche, M., Ramirez, R.J., Nattel, S., Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model (1998) Am. J. Physiol., 275, pp. H301-21
dc.relation.referencesFenton, F.H., Cherry, E.M., Hastings, H.M., Evans, S.J., Multiple mechanisms of spiral wave breakup in a model of cardiac electrical activity (2002) Chaos, 12 (3), pp. 852-892
dc.relation.referencesWilhelms, M., Hettmann, H., Maleckar, M.M., Koivumäki, J.T., Dössel, O., Seemann, G., Benchmarking electrophysiological models of human atrial myocytes (2013) Front. Physiol., 3, p. 487
dc.relation.referencesVoigt, N., Trausch, A., Knaut, M., Matschke, K., Varró, A., Van Wagoner, D.R., Nattel, S., Dobrev, D., Left-to-Right atrial inward rectifier potassium current gradients in patients with paroxysmal versus chronic atrial fibrillation (2010) Circulation, 3 (5), pp. 472-480
dc.relation.referencesBray, M.A., Lin, S.F., Aliev, R.R., Roth, B.J., Wikswo, J.P., Experimental and theoretical analysis of phase singularity dynamics in cardiac tissue (2001) J. Cardiovasc. Electrophysiol., 12 (6), pp. 716-722
dc.relation.referencesKennedy, J., Eberhart, R., Particle swarm optimization (2021) Proceedings of ICNN’95 - International Conference on Neural Networks, 4, pp. 1942-1948. , IEEE
dc.relation.referencesMezura-Montes, E., Coello Coello, C.A., Constraint-handling in nature-inspired numerical optimization: past, present and future (2011) Swarm Evol. Comput., 1 (4), pp. 173-194
dc.relation.referencesPedersen, M.E.H., Good Parameters for Particle Swarm Optimization (2010) Technical Report HL1001, , Hvass Laboratories
dc.relation.referencesKraemer, H.C., Kappa Coefficient (2015) Wiley StatsRef: Statistics Reference Online, pp. 1-4. , John Wiley & Sons, Ltd Chichester, UK
dc.relation.referencesCover, T., Thomas, J., Elements of Information Theory (2012), Wiley
dc.relation.referencesZaman, J., Baykaner, T., Narayan, S.M., Mapping and ablation of rotational and focal drivers in atrial fibrillation (2019) Card. Electrophysiol. Clin., 11 (4), pp. 583-595
dc.relation.referencesBhatia, N.K., Rogers, A.J., Krummen, D.E., Hossainy, S., Sauer, W., Miller, J.M., Alhusseini, M.I., Narayan, S.M., Termination of persistent atrial fibrillation by ablating sites that control large atrial areas (2020) Europace, 22 (6), pp. 897-905
dc.relation.referencesValério, D., Lopes, A., Tenreiro Machado, J., Entropy analysis of a railway network's complexity (2016) Entropy, 18 (11), p. 388
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem