Quantifying the frequency modulation in electrograms during simulated atrial fibrillation in 2D domains

Vista sencilla de ítem
dc.contributor.affiliationUgarte, J.P., GIMSC, Universidad de San Buenaventura, Medellin, Colombia
dc.contributor.affiliationGómez-Echavarría, A., MATBIOM, Universidad de Medellín, Medellín, Colombia
dc.contributor.affiliationTobón, C., MATBIOM, Universidad de Medellín, Medellín, Colombia
dc.contributor.authorUgarte J.P
dc.contributor.authorGómez-Echavarría A
dc.contributor.authorTobón C.
dc.date.accessioned2024-12-27T20:52:04Z
dc.date.available2024-12-27T20:52:04Z
dc.date.issued2024
dc.descriptionAtrial fibrillation (AF) affects millions of people in the world, causing increased morbidity and mortality. Treatment involves antiarrhythmic drugs and catheter ablation, showing high success for paroxysmal AF but challenges for persistent AF. Experimental evidence suggests reentrant waves and rotors contribute to AF substrates. Ablation procedures rely on electroanatomical maps and electrogram (EGM) signals; however, current methods used in clinical practice lack consideration for time–frequency varying EGM components. The fractional Fourier transform (FrFT) can be adopted to capture time-varying frequency components, thereby enhancing the comprehension of arrhythmogenic substrates during AF for improved ablation strategies. To this end, a FrFT-based algorithm is developed to characterize non-stationary components in EGM signals from simulated AF episodes. The proposed algorithm comprises a pre-processing step to enhance the coarser features of the EGM waveform, a windowing process for dynamic assessment of the EGM, and a FrFT order optimization stage that seeks compact signal representations in fractional Fourier domains. The resulting order is related to the rate of frequency change in the signal, making it a useful indicator for frequency-modulated components. The FrFT-based algorithm is implemented on EGM signals from AF simulations in 2D domains representing a region of the atrial tissue. Consequently, the computed optimum FrFT orders are used to build maps that are spatially correlated to the underlying propagation dynamics of the simulated AF episode. The results evince that the extreme values in the optimum orders map pinpoint the localization of fibrillatory mechanisms, generating EGM activation waveforms with varying frequency content over time. © 2024 The Authors
dc.identifier.doi10.1016/j.compbiomed.2024.109228
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/11407/8709
dc.language.isoeng
dc.publisherElsevier Ltdspa
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.relation.citationvolume182
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85205349757&doi=10.1016%2fj.compbiomed.2024.109228&partnerID=40&md5=f60d65a1dda5c6b7b159695478e0a568
dc.relation.referencesChugh, S.S., Havmoeller, R., Narayanan, K., Singh, D., Rienstra, M., Benjamin, E.J., Gillum, R.F., Murray, C.J., Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study (2014) Circulation, 129 (8), pp. 837-847
dc.relation.referencesZulkifly, H., Lip, G.Y., Lane, D.A., Epidemiology of atrial fibrillation (2018) Int. J. Clin. Pract., 72 (3), pp. 1-7
dc.relation.referencesKannel, W.B., Wolf, P.A., Benjamin, E.J., Levy, D., Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates (1998) Am. J. Cardiol., 82 (7), pp. 2N-9N
dc.relation.referencesKato, R., Lickfett, L., Meininger, G., Dickfeld, T., Wu, R., Juang, G., Angkeow, P., Berger, R., Pulmonary vein anatomy in patients undergoing catheter ablation of atrial fibrillation: lessons learned by use of magnetic resonance imaging (2003) Circulation, 107 (15), pp. 2004-2010
dc.relation.referencesMlčochová, H., Tintěra, J., Porod, V., Peichl, P., Robert, V., Kautzner, J., Magnetic resonance angiography of pulmonary veins: implications for catheter ablation of atrial fibrillation (2005) Pacing Clin. Electrophysiol., 28 (10), pp. 1073-1080
dc.relation.referencesWilber, D.J., Pappone, C., Neuzil, P., De Paola, A., Marchlinski, F., Natale, A., Macle, L., Hall, B., Comparison of antiarrhythmic drug therapy and radiofrequency catheter ablation in patients with paroxysmal atrial fibrillation: a randomized controlled trial (2010) Jama, 303 (4), pp. 333-340
dc.relation.referencesPacker, D.L., Kowal, R.C., Wheelan, K.R., Irwin, J.M., Champagne, J., Guerra, P.G., Dubuc, M., Holcomb, R.G., Cryoballoon ablation of pulmonary veins for paroxysmal atrial fibrillation: first results of the North American arctic front (STOP AF) pivotal trial (2013) J. Am. Coll. Cardiol., 61 (16), pp. 1713-1723
dc.relation.referencesCalkins, H., Hindricks, G., Cappato, R., Kim, Y.H., Saad, E.B., Aguinaga, L., Akar, J.G., Willems, S., 2017 HRS/EHRA/ecas/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation (2018) Europace, 20 (1), pp. e1-e160
dc.relation.referencesXu, C.H., Liu, X., Rotor hypothesis in the time chain of atrial fibrillation (2022) J. Geriatr. Cardiol., 19, pp. 251-253
dc.relation.referencesSalvador-Montañés, Ó., Ramirez, R.J., Takemoto, Y., Ennis, S.R., Garcia-Iglesias, D., Wang, S., Wolfer, P.J., Berenfeld, O., Panoramic endocardial optical mapping demonstrates serial rotors acceleration and increasing complexity of activity during onset of cholinergic atrial fibrillation (2021) J. Am. Heart Assoc., 10
dc.relation.referencesRiku, S., Inden, Y., Yanagisawa, S., Fujii, A., Tomomatsu, T., Nakagomi, T., Shimojo, M., Murohara, T., Distributions and number of drivers on real-time phase mapping associated with successful atrial fibrillation termination during catheter ablation for non-paroxysmal atrial fibrillation (2024) J. Interv. Cardiac Electrophysiol., 67, pp. 303-317
dc.relation.referencesSanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.F., Garrigue, S., Haïssaguerre, M., Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans (2005) Circulation, 112 (6), pp. 789-797
dc.relation.referencesStiles, M.K., Brooks, A.G., Kuklik, P., John, B., Dimitri, H., Lau, D.H., Wilson, L., Sanders, P., High-density mapping of atrial fibrillation in humans: Relationship between high-frequency activation and electrogram fractionation (2008) J. Cardiovasc. Electrophysiol., 19 (12), pp. 1245-1253
dc.relation.referencesNarayan, S.M., Krummen, D.E., Shivkumar, K., Clopton, P., Rappel, W.J., Miller, J.M., 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 (2012) J. Am. Coll. Cardiol., 60 (7), pp. 628-636
dc.relation.referencesKalifa, J., Tanaka, K., Zaitsev, A.V., Warren, M., Vaidyanathan, R., Auerbach, D., Pandit, S., Berenfeld, O., Mechanisms of wave fractionation at boundaries of high-frequency excitation in the posterior left atrium of the isolated sheep heart during atrial fibrillation (2006) Circulation, 113 (5), pp. 626-633
dc.relation.referencesLi, C., Lim, B., Hwang, M., Song, J.S., Lee, Y.S., Joung, B., Pak, H.N., The spatiotemporal stability of dominant frequency sites in in-silico modeling of 3-dimensional left atrial mapping of atrial fibrillation (2016) PLoS ONE, 11 (7), pp. 1-14
dc.relation.referencesRodrigo, M., Guillem, M.S., Climent, A.M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., Fernández-Avilés, F., Berenfeld, O., Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study (2014) Heart Rhythm, 11 (9), pp. 1584-1591
dc.relation.referencesSalinet, J., Schlindwein, F.S., Stafford, P., Almeida, T.P., Li, X., Vanheusden, F.J., Guillem, M.S., Ng, G.A., Propagation of meandering rotors surrounded by areas of high dominant frequency in persistent atrial fibrillation (2017) Heart Rhythm, 14 (9), pp. 1269-1278
dc.relation.referencesClayton, R.H., Nash, M.P., Analysis of cardiac fibrillation using phase mapping (2015) Cardiac Electrophysiol. Clin., 7 (1), pp. 49-58
dc.relation.referencesRodrigo, M., Climent, A.M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., Guillem, M.S., Technical considerations on phase mapping for identification of atrial reentrant activity in direct-and inverse-computed electrograms (2017) Circ.: Arrhythm. Electrophysiol., 10 (9)
dc.relation.referencesUgarte, 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), pp. 1-17
dc.relation.referencesRoldan, E.M.C., Calero, S., Hidalgo, V.M., Enero, J., Rieta, J.J., Alcaraz, R., Multi-scale entropy evaluates the proarrhythmic condition of persistent atrial fibrillation patients predicting early failure of electrical cardioversion (2020) Entropy, 22 (7), pp. 1-17
dc.relation.referencesHayn, D., Edegger, K., Scherr, D., Lercher, P., Rotman, B., Klein, W., Schreier, G., Automated prediction of spontaneous termination of atrial fibrillation from electrocardiograms (2004) Comput. Cardiol., 31, pp. 117-120
dc.relation.referencesRoss-Howe, S., Tizhoosh, H.R., Atrial fibrillation detection using deep features and convolutional networks (2019) 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, pp. 1-4. , IEEE
dc.relation.referencesCohen, L., Chapter 7 - the short-time Fourier transform (1995) Time-Frequency Analysis, pp. 93-112. , Prentice-Hall, Inc. Upper Saddle River, NJ, USA
dc.relation.referencesAsgari, S., Mehrnia, A., Moussavi, M., Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine (2015) Comput. Biol. Med., 60, pp. 132-142
dc.relation.referencesHe, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., Zhang, H., Automatic detection of atrial fibrillation based on continuous wavelet transform and 2D convolutional neural networks (2018) Front. Physiol., 9 (AUG), pp. 1-11
dc.relation.referencesSharma, M., Rajendra Acharya, U., A new method to identify coronary artery disease with ECG signals and time-frequency concentrated antisymmetric biorthogonal wavelet filter bank (2019) Pattern Recognit. Lett., 125, pp. 235-240
dc.relation.referencesRioul, O., Vetterli, M., Wavelets & signal processing (1991) IEEE Signal Process. Mag., 8 (4), pp. 14-38
dc.relation.referencesBoashash, B., Time-Frequency Signal Analysis and Processing: a Comprehensive Reference (2015), Academic Press
dc.relation.referencesCohen, L., Chapter 8 - the wigner distribution (1995) Time-Frequency Analysis, pp. 113-135. , Prentice-Hall, Inc. Upper Saddle River, NJ, USA
dc.relation.referencesAlmeida, L.B., The fractional Fourier transform and time-frequency representations (1994) IEEE Trans. Signal Process., 42 (11), pp. 3084-3091
dc.relation.referencesKutay, M.A., Ozaktas, H., Ozaktas, H.M., Ankan, O., The fractional Fourier domain decomposition (1999) Signal Process., 77 (1), pp. 105-109
dc.relation.referencesOzaktas, H.M., Kutay, M.A., The Fractional Fourier Transform (2001), pp. 1477-1483. , 2001 European Control Conference, ECC, Porto, Portugal
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.-Heart Circ. Physiol., 275 (1), pp. H301-H321
dc.relation.referencesKneller, 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
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.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.referencesBosch, R.F., Zeng, X., Grammer, J.B., Popovic, K., Mewis, C., Kühlkamp, V., Ionic mechanisms of electrical remodeling in human atrial fibrillation. (1999) Cardiovasc. Res., 44 (1), pp. 121-131
dc.relation.referencesDobrev, D., Graf, E., Wettwer, E., Himmel, H., Hála, O., Doerfel, C., Christ, T., Ravens, U., Molecular basis of downregulation of G-protein–coupled inward rectifying K+ current (IK,ach) in chronic human atrial fibrillation (2001) Circulation, 104 (21), pp. 2551-2557
dc.relation.referencesVan Wagoner, D.R., Pond, A.L., McCarthy, P.M., Trimmer, J.S., Nerbonne, J.M., Outward K+ current densities and Kv1.5 expression are reduced in chronic human atrial fibrillation (1997) Circ. Res., 80 (6), pp. 772-781
dc.relation.referencesUgarte, J.P., Tobón, C., Lopes, A.M., Machado, J.A.T., A complex order model of atrial electrical propagation from fractal porous cell membrane (2020) Fractals, 28 (6)
dc.relation.referencesYang, Q., Liu, F., Turner, I., Numerical methods for fractional partial differential equations with Riesz space fractional derivatives (2010) Appl. Math. Model., 34, pp. 200-218
dc.relation.referencesBotteron, G., Smith, J., A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart (1995) IEEE Trans. Biomed. Eng., 42 (6), pp. 579-586
dc.relation.referencesEverett, T.H., Kok, L.C., Vaughn, R.H., Moorman, J.R., Haines, D.E., Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy (2001) IEEE Trans. Biomed. Eng., 48 (9), pp. 969-978
dc.relation.referencesNg, J., Goldberger, J.J., Understanding and interpreting dominant frequency analysis of af electrograms (2007) J. Cardiovasc. Electrophysiol., 18 (6), pp. 680-685
dc.relation.referencesNg, J., Kadish, A.H., Goldberger, J.J., Technical considerations for dominant frequency analysis (2007) J. Cardiovasc. Electrophysiol., 18 (7), pp. 757-764
dc.relation.referencesHarris, F.J., On the use of windows for harmonic analysis with the discrete Fourier transform (1978) Proc. IEEE, 66 (1), pp. 51-83
dc.relation.referencesNuttall, A., Some windows with very good sidelobe behavior (1981) IEEE Trans. Acoust. Speech Signal Process., 29 (1), pp. 84-91
dc.relation.referencesUgarte, J.P., Gómez-Echavarría, A., Tobón, C., Optimal compactness of fractional Fourier domain characterizes frequency modulated signals (2023) Chaos Solitons Fractals, 177
dc.relation.referencesRoney, C.H., Cantwell, C.D., Qureshi, N.A., Chowdhury, R.A., Dupont, E., Lim, P.B., Vigmond, E.J., Peters, N.S., Rotor tracking using phase of electrograms recorded during atrial fibrillation (2017) Ann. Biomed. Eng., 45 (4), pp. 910-923
dc.relation.referencesKuklik, P., Zeemering, S., Van Hunnik, A., Maesen, B., Pison, L., Lau, D.H., Maessen, J., Schotten, U., Identification of rotors during human atrial fibrillation using contact mapping and phase singularity detection: Technical considerations (2017) IEEE Trans. Biomed. Eng., 64 (2), pp. 310-318
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.referencesOzaktas, H.M., Kutay, M.A., Mendlovic, D., Introduction to the fractional Fourier transform and its applications (1999) Adv. Imaging Electron Phys., 106, pp. 239-291
dc.relation.referencesZheng, L., Shi, D., Maximum amplitude method for estimating compact fractional fourier domain (2010) IEEE Signal Process. Lett., 17 (3), pp. 293-296
dc.relation.referencesSerbes, A., Compact fractional Fourier domains (2017) IEEE Signal Process. Lett., 24 (4), pp. 427-431
dc.relation.referencesNaghsh, M.M., Modarres-Hashemi, M., ISAR image formation based on minimum entropy criterion and fractional Fourier transform (2009) IEICE Trans. Commun., E92-B (8), pp. 2714-2722
dc.relation.referencesGómez-Echavarría, A., Ugarte, J.P., Tobón, C., The fractional Fourier transform as a biomedical signal and image processing tool: A review (2020) Biocybern. Biomed. Eng.
dc.relation.referencesGencer, M., Bilgin, G., Aydin, N., Embolic Doppler ultrasound signal detection via fractional Fourier transform , pp. 3050-3053. , 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, Osaka, Japan, (ISSN: 1557170X) 2013
dc.relation.referencesWang, S., Zhang, Y., Yang, X., Sun, P., Dong, Z., Liu, A., Yuan, T.F., Pathological brain detection by a novel image feature-fractional fourier entropy (2015) Entropy, 17 (12), pp. 8278-8296
dc.relation.referencesZhang, Y.-D., Sun, Y., Phillips, P., Liu, G., Zhou, X., Wang, S., A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy (2016) J. Med. Syst., 40 (7)
dc.relation.referencesZhang, Y.-D., Wang, S.-H., Liu, G., Yang, J., Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform (2016) Adv. Mech. Eng., 8 (2), pp. 1-11
dc.relation.referencesSud, S., Blind separation of twin fetal heartbeats in an electrocardiogram using the fractional Fourier transform (2016) Int. J. Eng. Res. Appl., 6 (4), pp. 14-18
dc.relation.referencesFei, K., Wang, W., Yang, Q., Tang, S., Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure (2017) Neurocomputing, 249, pp. 290-298
dc.relation.referencesBelousov, Y.M., Elkin, N.N., Man'ko, V.I., Popov, E.G., Revenko, S.V., Tomographic representation of electrocardiogram signals (2018) J. Russ. Laser Res., 39 (3), pp. 302-313
dc.relation.referencesKeshishzadeh, S., Fallah, A., Rashidi, S., Electroencephalogram based biometrics: A fractional Fourier transform approach (2018) Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications, pp. 1-5. , Association for Computing Machinery Amsterdam, Netherlands
dc.relation.referencesZhang, Y., Zhang, Q., Wu, S., Biomedical Signal Detection Based on Fractional Fourier Transform (2008), pp. 349-352. , 5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in Conjunction with 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008, Shenzhen, China
dc.relation.referencesTao, R., Li, Y.-L., Wang, Y., Short-time fractional Fourier transform and its applications (2010) IEEE Trans. Signal Process., 58 (5), pp. 2568-2580
dc.relation.referencesQuintanilla, J.G., Alfonso-Almazán, J.M., Pérez-Castellano, N., Pandit, S.V., Jalife, J., Pérez-Villacastín, J., Filgueiras-Rama, D., Instantaneous amplitude and frequency modulations detect the footprint of rotational activity and reveal Stable Driver Regions as targets for persistent atrial fibrillation ablation (2019) Circ. Res., 125 (6), pp. 609-627
dc.relation.referencesSakata, K., Okuyama, Y., Ozawa, T., Haraguchi, R., Nakazawa, K., Tsuchiya, T., Horie, M., Ashihara, T., Not all rotors, effective ablation targets for nonparoxysmal atrial fibrillation, are included in areas suggested by conventional indirect indicators of atrial fibrillation drivers: Extra mapping project (2018) J. Arrhythm., 34 (2), pp. 176-184
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceComputers in Biology and Medicine
dc.sourceComput. Biol. Med.
dc.sourceScopus
dc.subjectCardiac computational modelingeng
dc.subjectFractional Fourier transformeng
dc.subjectMetaheuristic optimizationeng
dc.subjectNonstationary signalseng
dc.subjectRotorseng
dc.subjectElectrocardiogramseng
dc.subjectFourier transformseng
dc.subjectFrequency modulationeng
dc.subjectImage codingeng
dc.subjectImage segmentationeng
dc.subjectLight modulationeng
dc.subjectPhotomappingeng
dc.subjectSignal modulationeng
dc.subjectAntiarrhythmic drugeng
dc.subjectAtrial fibrillationeng
dc.subjectCardiac computational modelingeng
dc.subjectComputational modellingeng
dc.subjectElectrogramseng
dc.subjectFractional Fourier transformseng
dc.subjectMetaheuristic optimizationeng
dc.subjectNonstationary signalseng
dc.subjectTransform ordereng
dc.subjectWaveformseng
dc.subjectAblationeng
dc.titleQuantifying the frequency modulation in electrograms during simulated atrial fibrillation in 2D domainseng
dc.typeArticle
dc.type.localArtículo de revistaspa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

Archivos

Colecciones

Indexados Scopus