Feature subset selection and classification of intracardiac electrograms during atrial fibrillation

Several approaches have been adopted for the identification of arrhythmogenic sources maintaining atrial fibrillation (AF). In this paper, we propose a classifier that discriminates between four classes of atrial electrogram (EGM). We delved into the relation between levels of fractionation in EGM s...

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Fecha de publicación:: 2017

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: eng

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repository_id_str
dc.title.spa.fl_str_mv	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
title	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
spellingShingle	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation Atrial fibrillation Electroanatomical mapping Fractionated electrograms K-NN classifier Rotor Ablation Diseases Feature extraction Genetic algorithms Nearest neighbor search Rotors Text processing Atrial electrograms Atrial fibrillation Electrograms Feature extraction methods Feature subset selection Intracardiac electrograms k-NN classifier Radio-frequency Ablation Biomedical signal processing
title_short	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
title_full	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
title_fullStr	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
title_full_unstemmed	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
title_sort	Feature subset selection and classification of intracardiac electrograms during atrial fibrillation
dc.contributor.affiliation.spa.fl_str_mv	Duque, S.I., Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, Colombia Orozco-Duque, A., Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, Colombia, GI2B, Instituto Tecnológico Metropolitano, Medellín, Colombia Kremen, V., Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic Novak, D., Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic Tobón, C., MATBIOM, Universidad de Medellín, Medellín, Colombia Bustamante, J., Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, Colombia
dc.subject.keyword.eng.fl_str_mv	Atrial fibrillation Electroanatomical mapping Fractionated electrograms K-NN classifier Rotor Ablation Diseases Feature extraction Genetic algorithms Nearest neighbor search Rotors Text processing Atrial electrograms Atrial fibrillation Electrograms Feature extraction methods Feature subset selection Intracardiac electrograms k-NN classifier Radio-frequency Ablation Biomedical signal processing
topic	Atrial fibrillation Electroanatomical mapping Fractionated electrograms K-NN classifier Rotor Ablation Diseases Feature extraction Genetic algorithms Nearest neighbor search Rotors Text processing Atrial electrograms Atrial fibrillation Electrograms Feature extraction methods Feature subset selection Intracardiac electrograms k-NN classifier Radio-frequency Ablation Biomedical signal processing
description	Several approaches have been adopted for the identification of arrhythmogenic sources maintaining atrial fibrillation (AF). In this paper, we propose a classifier that discriminates between four classes of atrial electrogram (EGM). We delved into the relation between levels of fractionation in EGM signals and the fibrillation substrates in simulated episodes of chronic AF. Several feature extraction methods were used to calculate 92 features from 429 real EGM records acquired during radiofrequency ablation of chronic AF. We selected the optimal subset of features by using a genetic algorithm, followed by K-nearest neighbors (K-NN) classification into four levels of fractionation. Sensitivity of 0.90 and specificity of 0.97 were achieved. Subsequently, the results of the classification were extrapolated to signals of a 3D human atria model and a 2D model of atrial tissue. The 3D model simulated an episode of AF maintained by a rotor in the posterior wall of the left atrium and the 2D model simulated an AF episode with one stable rotor. We used the K-NN classifier trained on a given set of real EGM signals to detect a specific class of signals presenting the highest level of fractionation located near the rotor's vortex. This method needs to be tested on real clinical data to provide evidence that it can support ablation therapy procedures. © 2017 Elsevier Ltd
publishDate	2017
dc.date.accessioned.none.fl_str_mv	2017-12-19T19:36:43Z
dc.date.available.none.fl_str_mv	2017-12-19T19:36:43Z
dc.date.created.none.fl_str_mv	2017
dc.type.eng.fl_str_mv	Article
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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	17468094
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/4268
dc.identifier.doi.none.fl_str_mv	10.1016/j.bspc.2017.06.005
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	17468094 10.1016/j.bspc.2017.06.005 reponame:Repositorio Institucional Universidad de Medellín instname:Universidad de Medellín
url	http://hdl.handle.net/11407/4268
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-s2.0-85021174952&doi=10.1016%2fj.bspc.2017.06.005&partnerID=40&md5=e2419923ada30b09892ce3dd5ffceac5
dc.relation.ispartofes.spa.fl_str_mv	Biomedical Signal Processing and Control Biomedical Signal Processing and Control Volume 38, September 2017, Pages 182-190
dc.relation.references.spa.fl_str_mv	Almeida, T. P., Salinet, J. L., Chu, G. S., Ng, G. A., & Schlindwein, F. S. (2013). Different definitions of complex fractionated atrial electrograms do not concur with the clinical perspective. Paper presented at the Computing in Cardiology, , 40 1055-1058. Barbaro, V., Bartolini, P., Calcagnini, G., Censi, F., Michelucci, A., & Morelli, S. (1999). Mapping the organization of human atrial fibrillation using a basket catheter. Paper presented at the Computers in Cardiology, 475-478. Benjamin, E. J., Wolf, P. A., D'Agostino, R. B., Silbershatz, H., Kannel, W. B., & Levy, D. (1998). Impact of atrial fibrillation on the risk of death: The framingham heart study. Circulation, 98(10), 946-952. Berenfeld, O., & Jalife, J. (2011). Complex fractionated atrial electrograms: Is this the beast to tame in atrial fibrillation? Circulation: Arrhythmia and Electrophysiology, 4(4), 426-428. doi:10.1161/CIRCEP.111.964841 Botteron, G. W., & Smith, J. M. (1995). A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE Transactions on Biomedical Engineering, 42(6), 579-586. doi:10.1109/10.387197 Camm, A. J., Kirchhof, P., Lip, G. Y. H., Schotten, U., Savelieva, I., Ernst, S., . . . Zupan, I. (2010). Guidelines for the management of atrial fibrillation. European Heart Journal, 31(19), 2369-2429. doi:10.1093/eurheartj/ehq278 Chen, J., Lin, Y., Chen, L., Yu, J., Du, Z., Li, S., . . . Li, Z. (2014). A decade of complex fractionated electrograms catheter-based ablation for atrial fibrillation: Literature analysis, meta-analysis and systematic review. IJC Heart and Vessels, 4(1), 63-72. doi:10.1016/j.ijchv.2014.06.013 Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. doi:10.1109/TIT.1967.1053964 Everett IV, T. H., Kok, L. -., Vaughn, R. H., Moorman, J. R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586 Faes, L., Nollo, G., Antolini, R., Gaita, F., & Ravelli, F. (2002). A method for quantifying atrial fibrillation organization based on wave-morphology similarity. IEEE Transactions on Biomedical Engineering, 49(12 I), 1504-1513. doi:10.1109/TBME.2002.805472 Ganesan, A. N., Kuklik, P., Lau, D. H., Brooks, A. G., Baumert, M., Lim, W. W., . . . Sanders, P. (2013). Bipolar electrogram shannon entropy at sites of rotational activation implications for ablation of atrial fibrillation. Circulation: Arrhythmia and Electrophysiology, 6(1), 48-57. doi:10.1161/CIRCEP.112.976654 Goldberger, J. J., & Ng, J. (2010). Practical signal and image processing in clinical cardiology. Practical signal and image processing in clinical cardiology (pp. 1-400) doi:10.1007/978-1-84882-515-4 Houben, R. P. M., De Groot, N. M. S., & Allessie, M. A. (2010). Analysis of fractionated atrial fibrillation electrograms by wavelet decomposition. IEEE Transactions on Biomedical Engineering, 57(6), 1388-1398. doi:10.1109/TBME.2009.2037974 Houck, C., Joines, J., & Kay, M. (1995). A genetic algorithm for function optimization: A matlab implementation. NCSU-IE TR, 95(9). Hunter, R. J., Diab, I., Tayebjee, M., Richmond, L., Sporton, S., Earley, M. J., & Schilling, R. J. (2011). Characterization of fractionated atrial electrograms critical for maintenance of atrial fibrillation a randomized, controlled trial of ablation strategies (the CFAE AF trial). Circulation: Arrhythmia and Electrophysiology, 4(5), 622-629. doi:10.1161/CIRCEP.111.962928 Hunter, R. J., Diab, I., Thomas, G., Duncan, E., Abrams, D., Dhinoja, M., . . . Schilling, R. J. (2009). Validation of a classification system to grade fractionation in atrial fibrillation and correlation with automated detection systems. Europace, 11(12), 1587-1596. doi:10.1093/europace/eup351 Jalife, J., Berenfeld, O., & Mansour, M. (2002). Mother rotors and fibrillatory conduction: A mechanism of atrial fibrillation. Cardiovascular Research, 54(2), 204-216. doi:10.1016/S0008-6363(02)00223-7 Kalifa, J., Tanaka, K., Zaitsev, A. V., Warren, M., Vaidyanathan, R., Auerbach, D., . . . Berenfeld, O. (2006). Mechanisms of wave fractionation at boundaries of high-frequency excitation in the posterior left atrium of the isolated sheep heart during atrial fibrillation. Circulation, 113(5), 626-633. doi:10.1161/CIRCULATIONAHA.105.575340 Konings, K. T. S., Smeets, J. L. R. M., Penn, O. C., Wellens, H. J. J., & Allessie, M. A. (1997). Configuration of unipolar atrial electrograms during electrically induced atrial fibrillation in humans. Circulation, 95(5), 1231-1241. Křemen, V., Lhotská, L., MacAš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002 Lau, D. H., Maesen, B., Zeemering, S., Kuklik, P., Hunnik, A. V., Lankveld, T. A. R., . . . Schotten, U. (2015). Indices of bipolar complex fractionated atrial electrograms correlate poorly with each other and atrial fibrillation substrate complexity. Heart Rhythm, 12(7), 1415-1423. doi:10.1016/j.hrthm.2015.03.017 Lau, D. H., Maesen, B., Zeemering, S., Verheule, S., Crijns, H. J., & Schotten, U. (2012). Stability of complex fractionated atrial electrograms: A systematic review. Journal of Cardiovascular Electrophysiology, 23(9), 980-987. doi:10.1111/j.1540-8167.2012.02335.x Nademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., . . . Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: Mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054 Narayan, S. M., Wright, M., Derval, N., Jadidi, A., Forclaz, A., Nault, I., . . . Hocini, M. (2011). Classifying fractionated electrograms in human atrial fibrillation using monophasic action potentials and activation mapping: Evidence for localized drivers, rate acceleration, and nonlocal signal etiologies. Heart Rhythm, 8(2), 244-253. doi:10.1016/j.hrthm.2010.10.020 Ng, J., Kadish, A. H., & Goldberger, J. J. (2007). Technical considerations for dominant frequency analysis. Journal of Cardiovascular Electrophysiology, 18(7), 757-764. doi:10.1111/j.1540-8167.2007.00810.x Nollo, G., Marconcini, M., Faes, L., Bovolo, F., Ravelli, F., & Bruzzone, L. (2008). An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. IEEE Transactions on Biomedical Engineering, 55(9), 2275-2285. doi:10.1109/TBME.2008.923155 Oral, H., Chugh, A., Good, E., Wimmer, A., Dey, S., Gadeela, N., . . . Morady, F. (2007). Radiofrequency catheter ablation of chronic atrial fibrillation guided by complex electrograms. Circulation, 115(20), 2606-2612. doi:10.1161/CIRCULATIONAHA.107.691386 Orozco-Duque, A., Bustamante, J., & Castellanos-Dominguez, G. (2016). Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping. BioMedical Engineering Online, 15(1) doi:10.1186/s12938-016-0154-5 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 Orozco-Duque, A., Ugarte, J. P., Tobon, C., Saiz, J., & Bustamante, J. (2013). Approximate entropy can localize rotors, but not ectopic foci during chronic atrial fibrillation: A simulation study. Paper presented at the Computing in Cardiology, 40 903-906. Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), 230-236. doi:10.1109/TBME.1985.325532 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. Ravelli, F., & Masè, M. (2014). Computational mapping in atrial fibrillation: How the integration of signal-derived maps may guide the localization of critical sources. Europace, 16(5), 714-723. doi:10.1093/europace/eut376 Scherr, D., Dalal, D., Cheema, A., Cheng, A., Henrikson, C. A., Spragg, D., . . . Dong, J. (2007). Automated detection and characterization of complex fractionated atrial electrograms in human left atrium during atrial fibrillation. Heart Rhythm, 4(8), 1013-1020. doi:10.1016/j.hrthm.2007.04.021 Schilling, C. (2012). Analysis of Atrial Electrograms, 17. Schilling, C., Keller, M., Scherr, D., Oesterlein, T., Haïssaguerre, M., Schmitt, C., . . . Luik, A. (2015). Fuzzy decision tree to classify complex fractionated atrial electrograms. Biomedizinische Technik, 60(3), 245-255. doi:10.1515/bmt-2014-0110 Seitz, J., Horvilleur, J., Lacotte, J., Mouhoub, Y., Salerno, F., Moynagh, A., . . . Pisapia, A. (2013). Automated detection of complex fractionated atrial electrograms in substrate-based atrial fibrillation ablation: Better discrimination with a new setting of CARTO® algorithm. Journal of Atrial Fibrillation, 6(2), 23-30. Tobón, C., Rodríguez, J. F., Ferrero, J. M., Hornero, F., & Saiz, J. (2012). Dominant frequency and organization index maps in a realistic three-dimensional computational model of atrial fibrillation. Europace, 14(SUPPL. 5), v25-v32. doi:10.1093/europace/eus268 Tobón, C., Ruiz-Villa, C. A., Heidenreich, E., Romero, L., Hornero, F., & Saiz, J. (2013). A three-dimensional human atrial model with fiber orientation. electrograms and arrhythmic activation patterns relationship. PLoS ONE, 8(2) doi:10.1371/journal.pone.0050883 Ugarte, J. P., Orozco-Duque, A., N, C. T., Kremen, V., Novak, D., Saiz, J., . . . Bustamante, J. (2014). Dynamic approximate entropy electroanatomic maps detect rotors in a simulated atrial fibrillation model. PLoS ONE, 9(12) doi:10.1371/journal.pone.0114577 Ugarte, J. P., Tobón, C., Orozco-Duque, A., Becerra, M. A., & Bustamante, J. (2015). Effect of the electrograms density in detecting and ablating the tip of the rotor during chronic atrial fibrillation: An in silico study. Europace, 17, ii97-ii104. doi:10.1093/europace/euv244 WELLS, J. L.,Jr, KARP, R. B., KOUCHOUKOS, N. T., MACLEAN, W. A. H., JAMES, T. N., & WALDO, A. L. (1978). Characterization of atrial fibrillation in man: Studies following open heart surgery. Pacing and Clinical Electrophysiology, 1(4), 426-438. doi:10.1111/j.1540-8159.1978.tb03504.x Zhang, D. (2005). Wavelet approach for ECG baseline wander correction and noise reduction. Paper presented at the Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 7 VOLS 1212-1215. Zlochiver, S., Yamazaki, M., Kalifa, J., & Berenfeld, O. (2008). Rotor meandering contributes to irregularity in electrograms during atrial fibrillation. Heart Rhythm, 5(6), 846-854. doi:10.1016/j.hrthm.2008.03.010
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	Elsevier Ltd
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
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spelling	2017-12-19T19:36:43Z2017-12-19T19:36:43Z201717468094http://hdl.handle.net/11407/426810.1016/j.bspc.2017.06.005reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínSeveral approaches have been adopted for the identification of arrhythmogenic sources maintaining atrial fibrillation (AF). In this paper, we propose a classifier that discriminates between four classes of atrial electrogram (EGM). We delved into the relation between levels of fractionation in EGM signals and the fibrillation substrates in simulated episodes of chronic AF. Several feature extraction methods were used to calculate 92 features from 429 real EGM records acquired during radiofrequency ablation of chronic AF. We selected the optimal subset of features by using a genetic algorithm, followed by K-nearest neighbors (K-NN) classification into four levels of fractionation. Sensitivity of 0.90 and specificity of 0.97 were achieved. Subsequently, the results of the classification were extrapolated to signals of a 3D human atria model and a 2D model of atrial tissue. The 3D model simulated an episode of AF maintained by a rotor in the posterior wall of the left atrium and the 2D model simulated an AF episode with one stable rotor. We used the K-NN classifier trained on a given set of real EGM signals to detect a specific class of signals presenting the highest level of fractionation located near the rotor's vortex. This method needs to be tested on real clinical data to provide evidence that it can support ablation therapy procedures. © 2017 Elsevier LtdengElsevier LtdFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85021174952&doi=10.1016%2fj.bspc.2017.06.005&partnerID=40&md5=e2419923ada30b09892ce3dd5ffceac5Biomedical Signal Processing and ControlBiomedical Signal Processing and Control Volume 38, September 2017, Pages 182-190Almeida, T. P., Salinet, J. L., Chu, G. S., Ng, G. A., & Schlindwein, F. S. (2013). Different definitions of complex fractionated atrial electrograms do not concur with the clinical perspective. Paper presented at the Computing in Cardiology, , 40 1055-1058.Barbaro, V., Bartolini, P., Calcagnini, G., Censi, F., Michelucci, A., & Morelli, S. (1999). Mapping the organization of human atrial fibrillation using a basket catheter. Paper presented at the Computers in Cardiology, 475-478.Benjamin, E. J., Wolf, P. A., D'Agostino, R. B., Silbershatz, H., Kannel, W. B., & Levy, D. (1998). Impact of atrial fibrillation on the risk of death: The framingham heart study. Circulation, 98(10), 946-952.Berenfeld, O., & Jalife, J. (2011). Complex fractionated atrial electrograms: Is this the beast to tame in atrial fibrillation? Circulation: Arrhythmia and Electrophysiology, 4(4), 426-428. doi:10.1161/CIRCEP.111.964841Botteron, G. W., & Smith, J. M. (1995). A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE Transactions on Biomedical Engineering, 42(6), 579-586. doi:10.1109/10.387197Camm, A. J., Kirchhof, P., Lip, G. Y. H., Schotten, U., Savelieva, I., Ernst, S., . . . Zupan, I. (2010). Guidelines for the management of atrial fibrillation. European Heart Journal, 31(19), 2369-2429. doi:10.1093/eurheartj/ehq278Chen, J., Lin, Y., Chen, L., Yu, J., Du, Z., Li, S., . . . Li, Z. (2014). A decade of complex fractionated electrograms catheter-based ablation for atrial fibrillation: Literature analysis, meta-analysis and systematic review. IJC Heart and Vessels, 4(1), 63-72. doi:10.1016/j.ijchv.2014.06.013Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. doi:10.1109/TIT.1967.1053964Everett IV, T. H., Kok, L. -., Vaughn, R. H., Moorman, J. R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586Faes, L., Nollo, G., Antolini, R., Gaita, F., & Ravelli, F. (2002). A method for quantifying atrial fibrillation organization based on wave-morphology similarity. IEEE Transactions on Biomedical Engineering, 49(12 I), 1504-1513. doi:10.1109/TBME.2002.805472Ganesan, A. N., Kuklik, P., Lau, D. H., Brooks, A. G., Baumert, M., Lim, W. W., . . . Sanders, P. (2013). Bipolar electrogram shannon entropy at sites of rotational activation implications for ablation of atrial fibrillation. Circulation: Arrhythmia and Electrophysiology, 6(1), 48-57. doi:10.1161/CIRCEP.112.976654Goldberger, J. J., & Ng, J. (2010). Practical signal and image processing in clinical cardiology. Practical signal and image processing in clinical cardiology (pp. 1-400) doi:10.1007/978-1-84882-515-4Houben, R. P. M., De Groot, N. M. S., & Allessie, M. A. (2010). Analysis of fractionated atrial fibrillation electrograms by wavelet decomposition. IEEE Transactions on Biomedical Engineering, 57(6), 1388-1398. doi:10.1109/TBME.2009.2037974Houck, C., Joines, J., & Kay, M. (1995). A genetic algorithm for function optimization: A matlab implementation. NCSU-IE TR, 95(9).Hunter, R. J., Diab, I., Tayebjee, M., Richmond, L., Sporton, S., Earley, M. J., & Schilling, R. J. (2011). Characterization of fractionated atrial electrograms critical for maintenance of atrial fibrillation a randomized, controlled trial of ablation strategies (the CFAE AF trial). Circulation: Arrhythmia and Electrophysiology, 4(5), 622-629. doi:10.1161/CIRCEP.111.962928Hunter, R. J., Diab, I., Thomas, G., Duncan, E., Abrams, D., Dhinoja, M., . . . Schilling, R. J. (2009). Validation of a classification system to grade fractionation in atrial fibrillation and correlation with automated detection systems. Europace, 11(12), 1587-1596. doi:10.1093/europace/eup351Jalife, J., Berenfeld, O., & Mansour, M. (2002). Mother rotors and fibrillatory conduction: A mechanism of atrial fibrillation. Cardiovascular Research, 54(2), 204-216. doi:10.1016/S0008-6363(02)00223-7Kalifa, J., Tanaka, K., Zaitsev, A. V., Warren, M., Vaidyanathan, R., Auerbach, D., . . . Berenfeld, O. (2006). Mechanisms of wave fractionation at boundaries of high-frequency excitation in the posterior left atrium of the isolated sheep heart during atrial fibrillation. Circulation, 113(5), 626-633. doi:10.1161/CIRCULATIONAHA.105.575340Konings, K. T. S., Smeets, J. L. R. M., Penn, O. C., Wellens, H. J. J., & Allessie, M. A. (1997). Configuration of unipolar atrial electrograms during electrically induced atrial fibrillation in humans. Circulation, 95(5), 1231-1241.Křemen, V., Lhotská, L., MacAš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002Lau, D. H., Maesen, B., Zeemering, S., Kuklik, P., Hunnik, A. V., Lankveld, T. A. R., . . . Schotten, U. (2015). Indices of bipolar complex fractionated atrial electrograms correlate poorly with each other and atrial fibrillation substrate complexity. Heart Rhythm, 12(7), 1415-1423. doi:10.1016/j.hrthm.2015.03.017Lau, D. H., Maesen, B., Zeemering, S., Verheule, S., Crijns, H. J., & Schotten, U. (2012). Stability of complex fractionated atrial electrograms: A systematic review. Journal of Cardiovascular Electrophysiology, 23(9), 980-987. doi:10.1111/j.1540-8167.2012.02335.xNademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., . . . Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: Mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054Narayan, S. M., Wright, M., Derval, N., Jadidi, A., Forclaz, A., Nault, I., . . . Hocini, M. (2011). Classifying fractionated electrograms in human atrial fibrillation using monophasic action potentials and activation mapping: Evidence for localized drivers, rate acceleration, and nonlocal signal etiologies. Heart Rhythm, 8(2), 244-253. doi:10.1016/j.hrthm.2010.10.020Ng, J., Kadish, A. H., & Goldberger, J. J. (2007). Technical considerations for dominant frequency analysis. Journal of Cardiovascular Electrophysiology, 18(7), 757-764. doi:10.1111/j.1540-8167.2007.00810.xNollo, G., Marconcini, M., Faes, L., Bovolo, F., Ravelli, F., & Bruzzone, L. (2008). An automatic system for the analysis and classification of human atrial fibrillation patterns from intracardiac electrograms. 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Heart Rhythm, 5(6), 846-854. doi:10.1016/j.hrthm.2008.03.010ScopusFeature subset selection and classification of intracardiac electrograms during atrial fibrillationArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Duque, S.I., Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, ColombiaOrozco-Duque, A., Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, Colombia, GI2B, Instituto Tecnológico Metropolitano, Medellín, ColombiaKremen, V., Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech RepublicNovak, D., Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicTobón, C., MATBIOM, Universidad de Medellín, Medellín, ColombiaBustamante, J., Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, ColombiaDuque S.I.Orozco-Duque A.Kremen V.Novak D.Tobón C.Bustamante J.Bioengineering Center, Universidad Pontificia Bolivariana, Medellín, ColombiaGI2B, Instituto Tecnológico Metropolitano, Medellín, ColombiaCzech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech RepublicDepartment of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech RepublicMATBIOM, Universidad de Medellín, Medellín, ColombiaAtrial fibrillationElectroanatomical mappingFractionated electrogramsK-NN classifierRotorAblationDiseasesFeature extractionGenetic algorithmsNearest neighbor searchRotorsText processingAtrial electrogramsAtrial fibrillationElectrogramsFeature extraction methodsFeature subset selectionIntracardiac electrogramsk-NN classifierRadio-frequency AblationBiomedical signal processingSeveral approaches have been adopted for the identification of arrhythmogenic sources maintaining atrial fibrillation (AF). In this paper, we propose a classifier that discriminates between four classes of atrial electrogram (EGM). We delved into the relation between levels of fractionation in EGM signals and the fibrillation substrates in simulated episodes of chronic AF. Several feature extraction methods were used to calculate 92 features from 429 real EGM records acquired during radiofrequency ablation of chronic AF. We selected the optimal subset of features by using a genetic algorithm, followed by K-nearest neighbors (K-NN) classification into four levels of fractionation. Sensitivity of 0.90 and specificity of 0.97 were achieved. Subsequently, the results of the classification were extrapolated to signals of a 3D human atria model and a 2D model of atrial tissue. The 3D model simulated an episode of AF maintained by a rotor in the posterior wall of the left atrium and the 2D model simulated an AF episode with one stable rotor. We used the K-NN classifier trained on a given set of real EGM signals to detect a specific class of signals presenting the highest level of fractionation located near the rotor's vortex. This method needs to be tested on real clinical data to provide evidence that it can support ablation therapy procedures. © 2017 Elsevier Ltdhttp://purl.org/coar/access_right/c_16ec11407/4268oai:repository.udem.edu.co:11407/42682020-05-27 15:48:52.894Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Feature subset selection and classification of intracardiac electrograms during atrial fibrillation

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