Local synchronization indices for rotors detection in atrial fibrillation: A simulation study

Stable rotors have been proposed as mechanisms that maintain atrial fibrillation which is the most common arrhythmia worldwide. The information of intracardiac electrograms (EGMs), recorded through multielectrode arrays, is used to characterize the electrical conduction dynamics and thus to identify...

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Fecha de publicación:: 2021

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: eng

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network_acronym_str	REPOUDEM2
network_name_str	Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
title	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
spellingShingle	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study atrial fibrillation Electrograms rotors signal processing Diseases Locks (fasteners) Multivariant analysis Computational simulation Electrical conduction Intracardiac electrograms Local synchronizations Multi variate analysis Multielectrode arrays Phase synchronization Synchronization index Synchronization
title_short	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
title_full	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
title_fullStr	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
title_full_unstemmed	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
title_sort	Local synchronization indices for rotors detection in atrial fibrillation: A simulation study
dc.subject.spa.fl_str_mv	atrial fibrillation Electrograms rotors signal processing
topic	atrial fibrillation Electrograms rotors signal processing Diseases Locks (fasteners) Multivariant analysis Computational simulation Electrical conduction Intracardiac electrograms Local synchronizations Multi variate analysis Multielectrode arrays Phase synchronization Synchronization index Synchronization
dc.subject.keyword.eng.fl_str_mv	Diseases Locks (fasteners) Multivariant analysis Computational simulation Electrical conduction Intracardiac electrograms Local synchronizations Multi variate analysis Multielectrode arrays Phase synchronization Synchronization index Synchronization
description	Stable rotors have been proposed as mechanisms that maintain atrial fibrillation which is the most common arrhythmia worldwide. The information of intracardiac electrograms (EGMs), recorded through multielectrode arrays, is used to characterize the electrical conduction dynamics and thus to identify rotors or reentrant propagating waves. Most of the methods of EGMs processing are based on the assessment of the individual properties of each EGM signal. Additionally, synchronization indices have been proposed to evaluate the properties of the conduction patterns by means of multivariate analysis. However, the problem of rotor detection through EGMs remains open. We evaluate the behavior of four local synchronization indices using computational simulations of different conduction patterns and the corresponding EGMs in 2D models. The results show that phase synchronization exhibits better performance than correlation, coherence, and mutual information for detecting rotors under different fibrillatory patterns. We also show that this approach outperforms a previously reported technique based on entropy analysis of individual EGM. Synchronization maps using phase-locking values calculated from adjacent EGM highlight the vicinity of the core of stable rotors, even in the presence of multiple wavefronts and wave breaks. Therefore, phase-locking maps can be a useful tool for characterizing rotors during atrial fibrillation episodes. © 2020
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-02-05T14:57:36Z
dc.date.available.none.fl_str_mv	2021-02-05T14:57:36Z
dc.date.none.fl_str_mv	2021
dc.type.eng.fl_str_mv	Article
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv	10075704
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/5893
dc.identifier.doi.none.fl_str_mv	10.1016/j.cnsns.2020.105548
identifier_str_mv	10075704 10.1016/j.cnsns.2020.105548
url	http://hdl.handle.net/11407/5893
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.isversionof.none.fl_str_mv	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092479892&doi=10.1016%2fj.cnsns.2020.105548&partnerID=40&md5=8cffda396651c89620cf84dd152cfce1
dc.relation.citationvolume.none.fl_str_mv	94
dc.relation.references.none.fl_str_mv	Jalife, J., Berenfeld, O., Mansour, M., Mother rotors and fibrillatory conduction: a mechanism of atrial fibrillation (2002) Cardiovasc. Res., 54 (2), pp. 204-216 Narayan, S.M., Shivkumar, K., Krummen, D.E., Miller, J.M., Rappel, W.-J., Panoramic electrophysiological mapping but not electrogram morphology identifies stable sources for human atrial fibrillation: stable atrial fibrillation rotors and focal sources relate poorly to fractionated electrograms (2013) Circ Arrhythm Electrophysiol, 6 (1), pp. 58-67 Narayan, S.M., Krummen, D.E., Rappel, W.-J., Clinical mapping approach to diagnose electrical rotors and focal impulse sources for human atrial fibrillation (2012) J Cardiovasc Electrophysiol, 23 (5), pp. 447-454 Haissaguerre, M., Hocini, M., Denis, A., Shah, A.J., Komatsu, Y., Yamashita, S., Driver domains in persistent atrial fibrillation (2014) Circulation, 130 (7), pp. 530-538 Podziemski, P., Zeemering, S., Kuklik, P., van Hunnik, A., Maesen, B., Maessen, J., Rotors detected by phase analysis of filtered, epicardial atrial fibrillation electrograms colocalize with regions of conduction block (2018) Circ Arrhythm Electrophysiol, 11 (10), p. e005858 Narayan, S.M., Baykaner, T., Clopton, P., Schricker, A., Lalani, G.G., Krummen, D.E., Ablation of rotor and focal sources reduces late recurrence of atrial fibrillation compared with trigger ablation alone: extended follow-up of the confirm trial (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation) (2014) J Am Coll Cardiol, 63 (17), pp. 1761-1768 Ravelli, F., Masè, M., Computational mapping in atrial fibrillation: how the integration of signal-derived maps may guide the localization of critical sources (2014) Europace, 16 (5), pp. 714-723 Ugarte, J.P., Orozco-Duque, A., Tobón, C., Kremen, V., Novak, D., Saiz, J., Dynamic approximate entropy electroanatomic maps detect rotors in a simulated atrial fibrillation model (2014) PloS One, 9 (12) Ugarte, J.P., Tobón, C., Orozco-Duque, A., Entropy mapping approach for functional reentry detection in atrial fibrillation: an in-silico study (2019) Entropy, 21 (2), p. 194 Lin, Y.-J., Lo, M.-T., Chang, S.-L., Lo, L.-W., Hu, Y.-F., Chao, T.-F., Benefits of atrial substrate modification guided by electrogram similarity and phase mapping techniques to eliminate rotors and focal sources versus conventional defragmentation in persistent atrial fibrillation (2016) JACC Clin Electrophysiol, 2 (6), pp. 667-678 Kuklik, P., Zeemering, S., van Hunnik, A., Maesen, B., Pison, L., Lau, D.H., Identification of rotors during human atrial fibrillation using contact mapping and phase singularity detection: technical considerations (2016) IEEE Trans Biomed Eng, 64 (2), pp. 310-318 Pandit, S.V., Jalife, J., Rotors and the dynamics of cardiac fibrillation (2013) Circ Res, 112 (5), pp. 849-862 Vijayakumar, R., Vasireddi, S.K., Cuculich, P.S., Faddis, M.N., Rudy, Y., Methodology considerations in phase mapping of human cardiac arrhythmias (2016) Circ Arrhythm Electrophysiol, 9 (11), p. e004409 Vidmar, D., Narayan, S.M., Rappel, W.-J., Phase synchrony reveals organization in human atrial fibrillation (2015) Am J Physiol-HeartCirc Physiol, 309 (12), pp. H2118-H2126 Zeemering, S., Podziemski, P., van Hunnik, A., Maesen, B., Bonizzi, P., Schotten, U., Electrogram coupling as a measure of local conduction during atrial fibrillation (2015) 2015 Computing in cardiology conference (CinC), pp. 813-816. , IEEE Barbaro, V., Bartolini, P., Calcagnini, G., Censi, F., Michelucci, A., Measure of synchronisation of right atrial depolarisation wavefronts during atrial fibrillation (2002) Med Biol Eng Comput, 40 (1), pp. 56-62 Kuklik, P., Schäffer, B., Hoffmann, B.A., Ganesan, A.N., Schreiber, D., Moser, J.M., Local electrical dyssynchrony during atrial fibrillation: theoretical considerations and initial catheter ablation results (2016) PloS One, 11 (10), p. e0164236 Liang, Z., Bai, Y., Li X. Synchronization measures in EEG signals. 2016 Sazonov, A.V., Ho, C.K., Bergmans, J.W., Arends, J.B., Griep, P.A., Verbitskiy, E.A., An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the eeg (2009) Biol Cybern, 100 (2), p. 129 Courtemanche, M., Ramirez, R.J., Nattel, S., Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model (1998) Am J Physiol-HeartCirc Physiol, 275 (1), pp. H301-H321 Kneller, J., Zou, R., Vigmond, E.J., Wang, Z., Leon, L.J., Nattel, S., Cholinergic atrial fibrillation in a computer model of a two-dimensional sheet of canine atrial cells with realistic ionic properties (2002) Circ Res, 90 (9), pp. e73-e87 Fenton, 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 Ugarte, J.P., Tobón, C., Lopes, A.M., Machado, J., Atrial rotor dynamics under complex fractional order diffusion (2018) Front Physiol, 9, p. 975 Yang, Q., Liu, F., Turner, I., Numerical methods for fractional partial differential equations with Riesz space fractional derivatives (2010) Appl Math Modell, 34, pp. 200-218 Zwillinger, D., Kokoska, S., Standard probability and statistics tables and formulae (1999), CRC Press Ross, B.C., Mutual information between discrete and continuous data sets (2014) PloS One, 9 (2) Niso, G., Bruña, R., Pereda, E., Gutiérrez, R., Bajo, R., Maestú, F., Hermes: towards an integrated toolbox to characterize functional and effective brain connectivity (2013) Neuroinformatics, 11 (4), pp. 405-434 Aydore, S., Pantazis, D., Leahy, R.M., A note on the phase locking value and its properties (2013) Neuroimage, 74, pp. 231-244 Kuklik, P., Zeemering, S., Maesen, B., Maessen, J., Crijns, H.J., Verheule, S., Reconstruction of instantaneous phase of unipolar atrial contact electrogram using a concept of sinusoidal recomposition and hilbert transform (2014) IEEE Trans Biomed Eng, 62 (1), pp. 296-302 Roney, C.H., Cantwell, C.D., Qureshi, N.A., Chowdhury, R.A., Dupont, E., Lim, P.B., Rotor tracking using phase of electrograms recorded during atrial fibrillation (2017) Ann Biomed Eng, 45 (4), pp. 910-923 Pincus, S.M., Approximate entropy as a measure of system complexity. (1991) ProcNatl Acad Sci, 88 (6), pp. 2297-2301 Mainardi, L., Porta, A., Calcagnini, G., Bartolini, P., Michelucci, A., Cerutti, S., Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes (2001) Med Biol Eng Comput, 39 (2), pp. 249-254 Mainardi, L.T., Corino, V.D., Lombardi, L., Tondo, C., Mantica, M., Lombardi, F., Linear and nonlinear coupling between atrial signals (2006) IEEE Eng Med Biol Mag, 25 (6), pp. 63-70
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.none.fl_str_mv	Elsevier B.V.
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Básicas
publisher.none.fl_str_mv	Elsevier B.V.
dc.source.none.fl_str_mv	Communications in Nonlinear Science and Numerical Simulation
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	20212021-02-05T14:57:36Z2021-02-05T14:57:36Z10075704http://hdl.handle.net/11407/589310.1016/j.cnsns.2020.105548Stable rotors have been proposed as mechanisms that maintain atrial fibrillation which is the most common arrhythmia worldwide. The information of intracardiac electrograms (EGMs), recorded through multielectrode arrays, is used to characterize the electrical conduction dynamics and thus to identify rotors or reentrant propagating waves. Most of the methods of EGMs processing are based on the assessment of the individual properties of each EGM signal. Additionally, synchronization indices have been proposed to evaluate the properties of the conduction patterns by means of multivariate analysis. However, the problem of rotor detection through EGMs remains open. We evaluate the behavior of four local synchronization indices using computational simulations of different conduction patterns and the corresponding EGMs in 2D models. The results show that phase synchronization exhibits better performance than correlation, coherence, and mutual information for detecting rotors under different fibrillatory patterns. We also show that this approach outperforms a previously reported technique based on entropy analysis of individual EGM. Synchronization maps using phase-locking values calculated from adjacent EGM highlight the vicinity of the core of stable rotors, even in the presence of multiple wavefronts and wave breaks. Therefore, phase-locking maps can be a useful tool for characterizing rotors during atrial fibrillation episodes. © 2020engElsevier B.V.Facultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092479892&doi=10.1016%2fj.cnsns.2020.105548&partnerID=40&md5=8cffda396651c89620cf84dd152cfce194Jalife, J., Berenfeld, O., Mansour, M., Mother rotors and fibrillatory conduction: a mechanism of atrial fibrillation (2002) Cardiovasc. Res., 54 (2), pp. 204-216Narayan, S.M., Shivkumar, K., Krummen, D.E., Miller, J.M., Rappel, W.-J., Panoramic electrophysiological mapping but not electrogram morphology identifies stable sources for human atrial fibrillation: stable atrial fibrillation rotors and focal sources relate poorly to fractionated electrograms (2013) Circ Arrhythm Electrophysiol, 6 (1), pp. 58-67Narayan, S.M., Krummen, D.E., Rappel, W.-J., Clinical mapping approach to diagnose electrical rotors and focal impulse sources for human atrial fibrillation (2012) J Cardiovasc Electrophysiol, 23 (5), pp. 447-454Haissaguerre, M., Hocini, M., Denis, A., Shah, A.J., Komatsu, Y., Yamashita, S., Driver domains in persistent atrial fibrillation (2014) Circulation, 130 (7), pp. 530-538Podziemski, P., Zeemering, S., Kuklik, P., van Hunnik, A., Maesen, B., Maessen, J., Rotors detected by phase analysis of filtered, epicardial atrial fibrillation electrograms colocalize with regions of conduction block (2018) Circ Arrhythm Electrophysiol, 11 (10), p. e005858Narayan, S.M., Baykaner, T., Clopton, P., Schricker, A., Lalani, G.G., Krummen, D.E., Ablation of rotor and focal sources reduces late recurrence of atrial fibrillation compared with trigger ablation alone: extended follow-up of the confirm trial (conventional ablation for atrial fibrillation with or without focal impulse and rotor modulation) (2014) J Am Coll Cardiol, 63 (17), pp. 1761-1768Ravelli, F., Masè, M., Computational mapping in atrial fibrillation: how the integration of signal-derived maps may guide the localization of critical sources (2014) Europace, 16 (5), pp. 714-723Ugarte, J.P., Orozco-Duque, A., Tobón, C., Kremen, V., Novak, D., Saiz, J., Dynamic approximate entropy electroanatomic maps detect rotors in a simulated atrial fibrillation model (2014) PloS One, 9 (12)Ugarte, J.P., Tobón, C., Orozco-Duque, A., Entropy mapping approach for functional reentry detection in atrial fibrillation: an in-silico study (2019) Entropy, 21 (2), p. 194Lin, Y.-J., Lo, M.-T., Chang, S.-L., Lo, L.-W., Hu, Y.-F., Chao, T.-F., Benefits of atrial substrate modification guided by electrogram similarity and phase mapping techniques to eliminate rotors and focal sources versus conventional defragmentation in persistent atrial fibrillation (2016) JACC Clin Electrophysiol, 2 (6), pp. 667-678Kuklik, P., Zeemering, S., van Hunnik, A., Maesen, B., Pison, L., Lau, D.H., Identification of rotors during human atrial fibrillation using contact mapping and phase singularity detection: technical considerations (2016) IEEE Trans Biomed Eng, 64 (2), pp. 310-318Pandit, S.V., Jalife, J., Rotors and the dynamics of cardiac fibrillation (2013) Circ Res, 112 (5), pp. 849-862Vijayakumar, R., Vasireddi, S.K., Cuculich, P.S., Faddis, M.N., Rudy, Y., Methodology considerations in phase mapping of human cardiac arrhythmias (2016) Circ Arrhythm Electrophysiol, 9 (11), p. e004409Vidmar, D., Narayan, S.M., Rappel, W.-J., Phase synchrony reveals organization in human atrial fibrillation (2015) Am J Physiol-HeartCirc Physiol, 309 (12), pp. H2118-H2126Zeemering, S., Podziemski, P., van Hunnik, A., Maesen, B., Bonizzi, P., Schotten, U., Electrogram coupling as a measure of local conduction during atrial fibrillation (2015) 2015 Computing in cardiology conference (CinC), pp. 813-816. , IEEEBarbaro, V., Bartolini, P., Calcagnini, G., Censi, F., Michelucci, A., Measure of synchronisation of right atrial depolarisation wavefronts during atrial fibrillation (2002) Med Biol Eng Comput, 40 (1), pp. 56-62Kuklik, P., Schäffer, B., Hoffmann, B.A., Ganesan, A.N., Schreiber, D., Moser, J.M., Local electrical dyssynchrony during atrial fibrillation: theoretical considerations and initial catheter ablation results (2016) PloS One, 11 (10), p. e0164236Liang, Z., Bai, Y., Li X. Synchronization measures in EEG signals. 2016Sazonov, A.V., Ho, C.K., Bergmans, J.W., Arends, J.B., Griep, P.A., Verbitskiy, E.A., An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the eeg (2009) Biol Cybern, 100 (2), p. 129Courtemanche, M., Ramirez, R.J., Nattel, S., Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model (1998) Am J Physiol-HeartCirc Physiol, 275 (1), pp. H301-H321Kneller, J., Zou, R., Vigmond, E.J., Wang, Z., Leon, L.J., Nattel, S., Cholinergic atrial fibrillation in a computer model of a two-dimensional sheet of canine atrial cells with realistic ionic properties (2002) Circ Res, 90 (9), pp. e73-e87Fenton, 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. 852-892Ugarte, J.P., Tobón, C., Lopes, A.M., Machado, J., Atrial rotor dynamics under complex fractional order diffusion (2018) Front Physiol, 9, p. 975Yang, Q., Liu, F., Turner, I., Numerical methods for fractional partial differential equations with Riesz space fractional derivatives (2010) Appl Math Modell, 34, pp. 200-218Zwillinger, D., Kokoska, S., Standard probability and statistics tables and formulae (1999), CRC PressRoss, B.C., Mutual information between discrete and continuous data sets (2014) PloS One, 9 (2)Niso, G., Bruña, R., Pereda, E., Gutiérrez, R., Bajo, R., Maestú, F., Hermes: towards an integrated toolbox to characterize functional and effective brain connectivity (2013) Neuroinformatics, 11 (4), pp. 405-434Aydore, S., Pantazis, D., Leahy, R.M., A note on the phase locking value and its properties (2013) Neuroimage, 74, pp. 231-244Kuklik, P., Zeemering, S., Maesen, B., Maessen, J., Crijns, H.J., Verheule, S., Reconstruction of instantaneous phase of unipolar atrial contact electrogram using a concept of sinusoidal recomposition and hilbert transform (2014) IEEE Trans Biomed Eng, 62 (1), pp. 296-302Roney, C.H., Cantwell, C.D., Qureshi, N.A., Chowdhury, R.A., Dupont, E., Lim, P.B., Rotor tracking using phase of electrograms recorded during atrial fibrillation (2017) Ann Biomed Eng, 45 (4), pp. 910-923Pincus, S.M., Approximate entropy as a measure of system complexity. (1991) ProcNatl Acad Sci, 88 (6), pp. 2297-2301Mainardi, L., Porta, A., Calcagnini, G., Bartolini, P., Michelucci, A., Cerutti, S., Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes (2001) Med Biol Eng Comput, 39 (2), pp. 249-254Mainardi, L.T., Corino, V.D., Lombardi, L., Tondo, C., Mantica, M., Lombardi, F., Linear and nonlinear coupling between atrial signals (2006) IEEE Eng Med Biol Mag, 25 (6), pp. 63-70Communications in Nonlinear Science and Numerical Simulationatrial fibrillationElectrogramsrotorssignal processingDiseasesLocks (fasteners)Multivariant analysisComputational simulationElectrical conductionIntracardiac electrogramsLocal synchronizationsMulti variate analysisMultielectrode arraysPhase synchronizationSynchronization indexSynchronizationLocal synchronization indices for rotors detection in atrial fibrillation: A simulation studyArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Orozco-Duque, A., MATBIOM, Universidad de Medellín, Medellín, ColombiaUgarte, J.P., GIMSC, Universidad de San Buenaventura, Medellín, ColombiaTobón, C., MATBIOM, Universidad de Medellín, Medellín, Colombiahttp://purl.org/coar/access_right/c_16ecOrozco-Duque A.Ugarte J.P.Tobón C.11407/5893oai:repository.udem.edu.co:11407/58932021-02-05 09:57:36.376Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Local synchronization indices for rotors detection in atrial fibrillation: A simulation study

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