Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion

Computational simulations are used as tool to study atrial fibrillation and its maintaining mechanisms. Phase analysis has been used to elucidate the mechanisms by which a reentry is generated. However, clinical application of phase mapping requires a signal preprocessing stage that could affect the...

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Fecha de publicación:
2017
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/4258
Acceso en línea:
http://hdl.handle.net/11407/4258
Palabra clave:
Atrial fibrillation
Fractional diffusion
Nonlinear measures
Phase analysis
Rotors
Ablation
Biomedical engineering
Diffusion
Diseases
Rotors
Signal processing
Atrial fibrillation
Computational simulation
Fractional derivatives
Fractional diffusion
Fractional diffusion equation
Nonlinear measure
Phase analysis
Signal processing technique
Nonlinear analysis
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http://purl.org/coar/access_right/c_16ec
id REPOUDEM2_b1bf911283d2b55fd3ebad94aea8ec3b
oai_identifier_str oai:repository.udem.edu.co:11407/4258
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.spa.fl_str_mv Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
title Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
spellingShingle Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
Atrial fibrillation
Fractional diffusion
Nonlinear measures
Phase analysis
Rotors
Ablation
Biomedical engineering
Diffusion
Diseases
Rotors
Signal processing
Atrial fibrillation
Computational simulation
Fractional derivatives
Fractional diffusion
Fractional diffusion equation
Nonlinear measure
Phase analysis
Signal processing technique
Nonlinear analysis
title_short Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
title_full Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
title_fullStr Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
title_full_unstemmed Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
title_sort Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion
dc.contributor.affiliation.spa.fl_str_mv Ugarte, J.P., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, Colombia
Duque, S.I., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, Colombia
Duque, A.O., Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Medellín, Colombia
Tobón, C., Grupo de Investigación en Materiales Nanoestructurados y Biomodelación, Universidad de Medellín, Medellín, Colombia
Bustamante, J., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, Colombia
Andrade-Caicedo, H., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, Colombia
dc.subject.keyword.eng.fl_str_mv Atrial fibrillation
Fractional diffusion
Nonlinear measures
Phase analysis
Rotors
Ablation
Biomedical engineering
Diffusion
Diseases
Rotors
Signal processing
Atrial fibrillation
Computational simulation
Fractional derivatives
Fractional diffusion
Fractional diffusion equation
Nonlinear measure
Phase analysis
Signal processing technique
Nonlinear analysis
topic Atrial fibrillation
Fractional diffusion
Nonlinear measures
Phase analysis
Rotors
Ablation
Biomedical engineering
Diffusion
Diseases
Rotors
Signal processing
Atrial fibrillation
Computational simulation
Fractional derivatives
Fractional diffusion
Fractional diffusion equation
Nonlinear measure
Phase analysis
Signal processing technique
Nonlinear analysis
description Computational simulations are used as tool to study atrial fibrillation and its maintaining mechanisms. Phase analysis has been used to elucidate the mechanisms by which a reentry is generated. However, clinical application of phase mapping requires a signal preprocessing stage that could affect the activation sequences. In this work we use the fractional diffusion equation to generate fibrillatory dynamics, including stable and meandering rotors, and multiple wavelets, by varying the order of the spatial fractional derivatives obtaining different complexity levels of propagation in a 2D domain. We applied nonlinear measures to characterize the propagation patterns from electrograms. Our results show that electroanatomical maps constructed using approximate entropy and multifractal analysis, are able to detect the tip of stable and meandering rotors, and to mark the occurrence of collisions and wave breaks. Application of these signal processing techniques to clinical practice is feasible and could improve atrial fibrillation ablation procedures. © Springer Nature Singapore Pte Ltd. 2017.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2017-12-19T19:36:42Z
dc.date.available.none.fl_str_mv 2017-12-19T19:36:42Z
dc.date.created.none.fl_str_mv 2017
dc.type.eng.fl_str_mv Conference Paper
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.driver.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.identifier.isbn.none.fl_str_mv 9789811040856
dc.identifier.issn.none.fl_str_mv 16800737
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/4258
dc.identifier.doi.none.fl_str_mv 10.1007/978-981-10-4086-3_136
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad de Medellín
dc.identifier.instname.spa.fl_str_mv instname:Universidad de Medellín
identifier_str_mv 9789811040856
16800737
10.1007/978-981-10-4086-3_136
reponame:Repositorio Institucional Universidad de Medellín
instname:Universidad de Medellín
url http://hdl.handle.net/11407/4258
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018383521&doi=10.1007%2f978-981-10-4086-3_136&partnerID=40&md5=6ca3f6c0bfc02238feb5e0952ffe2eff
dc.relation.ispartofes.spa.fl_str_mv IFMBE Proceedings
IFMBE Proceedings Volume 60, 2017, Pages 541-544
dc.relation.references.spa.fl_str_mv Alfonso, B. -., David, K., Vicente, G., Blanca, R., & Kevin, B. (2014). Fractional Diffusion Models of Cardiac Electrical Propagation: Role of Structural Heterogeneity in Dispersion of Repolarization Journal of the Royal Society Interface, 11.
Andres, O. -., John, B., & German, C. -. (2016). Semi-Supervised Clustering of Fractionated Electrograms for Electroanatomical Atrial Mapping Biomedical Engineering Online, 15, 1-19.
Berenfeld, O., & Jalife, J. (2016). Mechanisms of atrial fibrillation: Rotors, ionic determinants, and excitation frequency. Heart Failure Clinics, 12(2), 167-178. doi:10.1016/j.hfc.2015.08.014
Bray, M. -., Shien-Fong, L. I. N., Aliev, R. R., Roth, B. J., & Wikswo Jr., J. P. (2001). Experimental and theoretical analysis of phase singularity dynamics in cardiac tissue. Journal of Cardiovascular Electrophysiology, 12(6), 716-722.
Clayton Richard, H., & Nash Martyn, P. (2015). Analysis of Cardiac Fibrillation using Phase Mapping Cardiac Electrophysiology Clinics, 7, 49-58.
Clayton, R. H., Bernus, O., Cherry, E. M., Dierckx, H., Fenton, F. H., Mirabella, L., . . . Zhang, H. (2011). Models of cardiac tissue electrophysiology: Progress, challenges and open questions. Progress in Biophysics and Molecular Biology, 104(1-3), 22-48. doi:10.1016/j.pbiomolbio.2010.05.008
Fenton Flavio, H., Cherry Elizabeth, M., Hastings Harold, M., & Evans Steven, J. (2002). Multiple Mechanisms of Spiral Wave Breakup in a Model of Cardiac Electrical Activity Chaos, 12, 852-892.
January Craig, T., Samuel, W. L., & Alpert, J. S. (2014). AHA/ACC/HRS Guideline for the Management of Patients with Atrial Fibrillation: Executive Summary Journal of the American College of Cardiology.
Kantelhardt Jan, W., Zschiegner Stephan, A., Eva, K. -., Armin, B., Shlomo, H., & Eugene, S. H. (2002). Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series Physica A, 316, 87-101.
Liu, F., Turner, I., Anh, V., Yang, Q., & Burrage, K. (2012). A numerical method for the fractional fitzhugh,nagumo monodomain model. ANZIAM Journal, 54(SUPPL), C608-C629.
Mathias, B., Prashanthan, S., & Ganesan, A. (2016). Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation Proceeding of the IEEE, 104, 416-431.
Mohammad, S., Gerhard, H., Martin, B., Breithardt, G., & Josephson Mark, E. (2013).
Narayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W. -., & Miller, J. M. (2012). Treatment of atrial fibrillation by the ablation of localized sources: CONFIRM (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation) trial. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022
Orozco-Duque, A., Novak, D., Kremen, V., & Bustamante, J. (2015). Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation. Physiological Measurement, 36(11), 2269-2284. doi:10.1088/0967-3334/36/11/2269
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88(6), 2297-2301.
Ugarte, J. P., Orozco-Duque, A., & Tobón, C. (2014). Dynamic Approximate Entropy Electroanatomic Maps Detect Rotors in a Simulated Atrial Fibrillation Model Plos One, 9.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.spa.fl_str_mv Springer Verlag
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias Básicas
dc.source.spa.fl_str_mv Scopus
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling 2017-12-19T19:36:42Z2017-12-19T19:36:42Z2017978981104085616800737http://hdl.handle.net/11407/425810.1007/978-981-10-4086-3_136reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínComputational simulations are used as tool to study atrial fibrillation and its maintaining mechanisms. Phase analysis has been used to elucidate the mechanisms by which a reentry is generated. However, clinical application of phase mapping requires a signal preprocessing stage that could affect the activation sequences. In this work we use the fractional diffusion equation to generate fibrillatory dynamics, including stable and meandering rotors, and multiple wavelets, by varying the order of the spatial fractional derivatives obtaining different complexity levels of propagation in a 2D domain. We applied nonlinear measures to characterize the propagation patterns from electrograms. Our results show that electroanatomical maps constructed using approximate entropy and multifractal analysis, are able to detect the tip of stable and meandering rotors, and to mark the occurrence of collisions and wave breaks. Application of these signal processing techniques to clinical practice is feasible and could improve atrial fibrillation ablation procedures. © Springer Nature Singapore Pte Ltd. 2017.engSpringer VerlagFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018383521&doi=10.1007%2f978-981-10-4086-3_136&partnerID=40&md5=6ca3f6c0bfc02238feb5e0952ffe2effIFMBE ProceedingsIFMBE Proceedings Volume 60, 2017, Pages 541-544Alfonso, B. -., David, K., Vicente, G., Blanca, R., & Kevin, B. (2014). Fractional Diffusion Models of Cardiac Electrical Propagation: Role of Structural Heterogeneity in Dispersion of Repolarization Journal of the Royal Society Interface, 11.Andres, O. -., John, B., & German, C. -. (2016). Semi-Supervised Clustering of Fractionated Electrograms for Electroanatomical Atrial Mapping Biomedical Engineering Online, 15, 1-19.Berenfeld, O., & Jalife, J. (2016). Mechanisms of atrial fibrillation: Rotors, ionic determinants, and excitation frequency. Heart Failure Clinics, 12(2), 167-178. doi:10.1016/j.hfc.2015.08.014Bray, M. -., Shien-Fong, L. I. N., Aliev, R. R., Roth, B. J., & Wikswo Jr., J. P. (2001). Experimental and theoretical analysis of phase singularity dynamics in cardiac tissue. Journal of Cardiovascular Electrophysiology, 12(6), 716-722.Clayton Richard, H., & Nash Martyn, P. (2015). Analysis of Cardiac Fibrillation using Phase Mapping Cardiac Electrophysiology Clinics, 7, 49-58.Clayton, R. H., Bernus, O., Cherry, E. M., Dierckx, H., Fenton, F. H., Mirabella, L., . . . Zhang, H. (2011). Models of cardiac tissue electrophysiology: Progress, challenges and open questions. Progress in Biophysics and Molecular Biology, 104(1-3), 22-48. doi:10.1016/j.pbiomolbio.2010.05.008Fenton Flavio, H., Cherry Elizabeth, M., Hastings Harold, M., & Evans Steven, J. (2002). Multiple Mechanisms of Spiral Wave Breakup in a Model of Cardiac Electrical Activity Chaos, 12, 852-892.January Craig, T., Samuel, W. L., & Alpert, J. S. (2014). AHA/ACC/HRS Guideline for the Management of Patients with Atrial Fibrillation: Executive Summary Journal of the American College of Cardiology.Kantelhardt Jan, W., Zschiegner Stephan, A., Eva, K. -., Armin, B., Shlomo, H., & Eugene, S. H. (2002). Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series Physica A, 316, 87-101.Liu, F., Turner, I., Anh, V., Yang, Q., & Burrage, K. (2012). A numerical method for the fractional fitzhugh,nagumo monodomain model. ANZIAM Journal, 54(SUPPL), C608-C629.Mathias, B., Prashanthan, S., & Ganesan, A. (2016). Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation Proceeding of the IEEE, 104, 416-431.Mohammad, S., Gerhard, H., Martin, B., Breithardt, G., & Josephson Mark, E. (2013).Narayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W. -., & Miller, J. M. (2012). Treatment of atrial fibrillation by the ablation of localized sources: CONFIRM (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation) trial. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022Orozco-Duque, A., Novak, D., Kremen, V., & Bustamante, J. (2015). Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation. Physiological Measurement, 36(11), 2269-2284. doi:10.1088/0967-3334/36/11/2269Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88(6), 2297-2301.Ugarte, J. P., Orozco-Duque, A., & Tobón, C. (2014). Dynamic Approximate Entropy Electroanatomic Maps Detect Rotors in a Simulated Atrial Fibrillation Model Plos One, 9.ScopusNonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusionConference Paperinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fUgarte, J.P., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, ColombiaDuque, S.I., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, ColombiaDuque, A.O., Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Medellín, ColombiaTobón, C., Grupo de Investigación en Materiales Nanoestructurados y Biomodelación, Universidad de Medellín, Medellín, ColombiaBustamante, J., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, ColombiaAndrade-Caicedo, H., Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, ColombiaUgarte J.P.Duque S.I.Duque A.O.Tobón C.Bustamante J.Andrade-Caicedo H.Grupo de Dinámica Cardiovascular, Universidad Pontificia Bolivariana, Medellín, ColombiaGrupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Medellín, ColombiaGrupo de Investigación en Materiales Nanoestructurados y Biomodelación, Universidad de Medellín, Medellín, ColombiaAtrial fibrillationFractional diffusionNonlinear measuresPhase analysisRotorsAblationBiomedical engineeringDiffusionDiseasesRotorsSignal processingAtrial fibrillationComputational simulationFractional derivativesFractional diffusionFractional diffusion equationNonlinear measurePhase analysisSignal processing techniqueNonlinear analysisComputational simulations are used as tool to study atrial fibrillation and its maintaining mechanisms. Phase analysis has been used to elucidate the mechanisms by which a reentry is generated. However, clinical application of phase mapping requires a signal preprocessing stage that could affect the activation sequences. In this work we use the fractional diffusion equation to generate fibrillatory dynamics, including stable and meandering rotors, and multiple wavelets, by varying the order of the spatial fractional derivatives obtaining different complexity levels of propagation in a 2D domain. We applied nonlinear measures to characterize the propagation patterns from electrograms. Our results show that electroanatomical maps constructed using approximate entropy and multifractal analysis, are able to detect the tip of stable and meandering rotors, and to mark the occurrence of collisions and wave breaks. Application of these signal processing techniques to clinical practice is feasible and could improve atrial fibrillation ablation procedures. © Springer Nature Singapore Pte Ltd. 2017.http://purl.org/coar/access_right/c_16ec11407/4258oai:repository.udem.edu.co:11407/42582020-05-27 16:31:07.317Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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