Estimation of PQ distance dispersion for atrial fibrillation detection

Background and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate i...

Full description

Autores:
Giraldo-Guzmán J.
Kotas, Marian
Castells, Francisco
Contreras Ortiz, Sonia Helena
Urina-Triana, Miguel
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/10368
Acceso en línea:
https://hdl.handle.net/20.500.12585/10368
https://doi.org/10.1016/j.cmpb.2021.106167
Palabra clave:
ECG processing
Atrial fibrillation
PQ dispersion Spatio–temporal
filtering Spatio–temporal patterns
Spatio–temporal patterns
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UTB2_d668555904e749e370b4f74311af0d68
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/10368
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Estimation of PQ distance dispersion for atrial fibrillation detection
title Estimation of PQ distance dispersion for atrial fibrillation detection
spellingShingle Estimation of PQ distance dispersion for atrial fibrillation detection
ECG processing
Atrial fibrillation
PQ dispersion Spatio–temporal
filtering Spatio–temporal patterns
Spatio–temporal patterns
LEMB
title_short Estimation of PQ distance dispersion for atrial fibrillation detection
title_full Estimation of PQ distance dispersion for atrial fibrillation detection
title_fullStr Estimation of PQ distance dispersion for atrial fibrillation detection
title_full_unstemmed Estimation of PQ distance dispersion for atrial fibrillation detection
title_sort Estimation of PQ distance dispersion for atrial fibrillation detection
dc.creator.fl_str_mv Giraldo-Guzmán J.
Kotas, Marian
Castells, Francisco
Contreras Ortiz, Sonia Helena
Urina-Triana, Miguel
dc.contributor.author.none.fl_str_mv Giraldo-Guzmán J.
Kotas, Marian
Castells, Francisco
Contreras Ortiz, Sonia Helena
Urina-Triana, Miguel
dc.subject.keywords.spa.fl_str_mv ECG processing
Atrial fibrillation
PQ dispersion Spatio–temporal
filtering Spatio–temporal patterns
Spatio–temporal patterns
topic ECG processing
Atrial fibrillation
PQ dispersion Spatio–temporal
filtering Spatio–temporal patterns
Spatio–temporal patterns
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Background and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate irregularity or on a mixture of the both approaches. Since the amplitude of the atrial waves is small, their analysis can lead to false results. On the other hand, the heart rate based analysis usually leads to many unnecessary warnings. Therefore, our goal is to develop a new method for effective AF detection based on the analysis of the electrical atrial waves. Methods: The proposed method employs the fact that there is a lack of repeatable P waves preceding QRS complexes during AF. We apply the operation of spatio-temporal filtering (STF) to magnify and detect the prominent spatio-temporal patterns (STP) within the P waves in multi-channel ECG recordings. Later we measure their distances (PQ) to the succeeding QRS complexes, and we estimate dispersion of the obtained PQ series. For signals with normal sinus rhythm, this dispersion is usually very low, and contrary, for AF it is much raised. This allows for effective discrimination of this cardiologic disorder. Results: Tested on an ECG database consisting of AF cases, normal rhythm cases and cases with normal rhythm restored by the use of cardioversion, the method proposed allowed for AF detection with the accuracy of 98.75% on the basis of both 8–channel and 2–channel signals of 12 s length. When the signals length was decreased to 6 s, the accuracy varied in the range of 95% − 97.5% depending on the number of channels and the dispersion measure applied. Conclusions: Our approach allows for high accuracy of atrial fibrillation detection using the analysis of electrical atrial activity. The method can be applied to an early detection of the desease and can advantageously be used to decrease the number of false warnings in systems based on the analysis of the heart rate.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-09-22T21:30:29Z
dc.date.available.none.fl_str_mv 2021-09-22T21:30:29Z
dc.date.issued.none.fl_str_mv 2021-05-03
dc.date.submitted.none.fl_str_mv 2021-09-08
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/restrictedAccess
dc.type.spa.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.citation.spa.fl_str_mv Jader Giraldo-Guzmán, Marian Kotas, Francisco Castells, Sonia H. Contreras-Ortiz, Miguel Urina-Triana,Estimation of PQ distance dispersion for atrial fibrillation detection, Computer Methods and Programs in Biomedicine, Volume 208, 2021, 106167, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2021.106167.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/10368
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.cmpb.2021.106167
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Jader Giraldo-Guzmán, Marian Kotas, Francisco Castells, Sonia H. Contreras-Ortiz, Miguel Urina-Triana,Estimation of PQ distance dispersion for atrial fibrillation detection, Computer Methods and Programs in Biomedicine, Volume 208, 2021, 106167, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2021.106167.
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/10368
https://doi.org/10.1016/j.cmpb.2021.106167
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 12 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Colombia
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Computer Methods and Programs in Biomedicine, Vol 208, 2021
institution Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/1/1-s2.0-S0169260721002418-main_Sonia%20Helena%20Contrer.pdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/2/license_rdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/3/license.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/4/1-s2.0-S0169260721002418-main_Sonia%20Helena%20Contrer.pdf.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/5/1-s2.0-S0169260721002418-main_Sonia%20Helena%20Contrer.pdf.jpg
bitstream.checksum.fl_str_mv d43ac8fbd656a2ba5b7d898b5bc41c69
4460e5956bc1d1639be9ae6146a50347
e20ad307a1c5f3f25af9304a7a7c86b6
2faf8585ed201272e13bb32ed723560a
51a980fb1fe50844ee28554e916ab1d5
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1814021701295407104
spelling Giraldo-Guzmán J.60d2ca10-9c1c-4d09-a1f2-6904aa131033Kotas, Marian115f4d30-ba45-4bf6-bf66-152b622af835Castells, Francisco7fe49a07-6f9d-4462-adda-2fc09a85104aContreras Ortiz, Sonia Helena1d56d7f5-97c9-4429-b47d-48ebe97de2a8Urina-Triana, Miguelaceaca19-a589-4210-b883-d3699a5cc245Colombia2021-09-22T21:30:29Z2021-09-22T21:30:29Z2021-05-032021-09-08Jader Giraldo-Guzmán, Marian Kotas, Francisco Castells, Sonia H. Contreras-Ortiz, Miguel Urina-Triana,Estimation of PQ distance dispersion for atrial fibrillation detection, Computer Methods and Programs in Biomedicine, Volume 208, 2021, 106167, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2021.106167.https://hdl.handle.net/20.500.12585/10368https://doi.org/10.1016/j.cmpb.2021.106167Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarBackground and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate irregularity or on a mixture of the both approaches. Since the amplitude of the atrial waves is small, their analysis can lead to false results. On the other hand, the heart rate based analysis usually leads to many unnecessary warnings. Therefore, our goal is to develop a new method for effective AF detection based on the analysis of the electrical atrial waves. Methods: The proposed method employs the fact that there is a lack of repeatable P waves preceding QRS complexes during AF. We apply the operation of spatio-temporal filtering (STF) to magnify and detect the prominent spatio-temporal patterns (STP) within the P waves in multi-channel ECG recordings. Later we measure their distances (PQ) to the succeeding QRS complexes, and we estimate dispersion of the obtained PQ series. For signals with normal sinus rhythm, this dispersion is usually very low, and contrary, for AF it is much raised. This allows for effective discrimination of this cardiologic disorder. Results: Tested on an ECG database consisting of AF cases, normal rhythm cases and cases with normal rhythm restored by the use of cardioversion, the method proposed allowed for AF detection with the accuracy of 98.75% on the basis of both 8–channel and 2–channel signals of 12 s length. When the signals length was decreased to 6 s, the accuracy varied in the range of 95% − 97.5% depending on the number of channels and the dispersion measure applied. Conclusions: Our approach allows for high accuracy of atrial fibrillation detection using the analysis of electrical atrial activity. The method can be applied to an early detection of the desease and can advantageously be used to decrease the number of false warnings in systems based on the analysis of the heart rate.12 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Computer Methods and Programs in Biomedicine, Vol 208, 2021Estimation of PQ distance dispersion for atrial fibrillation detectioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1ECG processingAtrial fibrillationPQ dispersion Spatio–temporalfiltering Spatio–temporal patternsSpatio–temporal patternsLEMBCartagena de IndiasOrganization, W.H. (2017) Cardiovascular diseases http://www.who.int/mediacentre/factsheets/fs317/en/Kamel, H., Okin, P.M., Elkind, M.S.V., Iadecola, C. Atrial Fibrillation and Mechanisms of Stroke: Time for a New Model (Open Access) (2016) Stroke, 47 (3), pp. 895-900. Cited 243 times. http://stroke.ahajournals.org/ doi: 10.1161/STROKEAHA.115.012004Sörnmo, L. Atrial Fibrillation from an Engineering Perspective (2018) . Cited 11 times. SpringerZhou, X., Ding, H., Wu, W., Zhang, Y. A real-time Atrial fibrillation detection algorithm based on the instantaneous state of heart rate (Open Access) (2015) PLoS ONE, 10 (9), art. no. 0136544. Cited 52 times. http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0136544&representation=PDF doi: 10.1371/journal.pone.0136544Islam, M.S., Ammour, N., Alajlan, N., Aboalsamh, H. Rhythm-based heartbeat duration normalization for atrial fibrillation detection (2016) Computers in Biology and Medicine, 72, pp. 160-169. Cited 17 times. www.elsevier.com/locate/compbiomed doi: 10.1016/j.compbiomed.2016.03.015Czabanski, R., Horoba, K., Wrobel, J., Matonia, A., Martinek, R., Kupka, T., Jezewski, M., (...), Leski, J.M. Detection of atrial fibrillation episodes in long‐term heart rhythm signals using a support vector machine (Open Access) (2020) Sensors (Switzerland), 20 (3), art. no. 765. Cited 13 times. https://www.mdpi.com/1424-8220/20/3/765/pdf doi: 10.3390/s20030765Climent, A.M., Guillem, M.D.L.S., Husser, D., Castells, F., Millet, J., Bollmann, A. Poincaré surface profiles of RR intervals: A novel noninvasive method for the evaluation of preferential AV nodal conduction during atrial fibrillation (2009) IEEE Transactions on Biomedical Engineering, 56 (2), art. no. 4595675, pp. 433-442. Cited 24 times. doi: 10.1109/TBME.2008.2003273Ebrahimzadeh, E., Kalantari, M., Joulani, M., Shahraki, R.S., Fayaz, F., Ahmadi, F. Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal (2018) Computer Methods and Programs in Biomedicine, 165, pp. 53-67. Cited 33 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2018.07.014Buscema, P.M., Grossi, E., Massini, G., Breda, M., Della Torre, F. Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems (2020) Computer Methods and Programs in Biomedicine, 191, art. no. 105401. Cited 10 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2020.105401Alcaraz, R., Martínez, A., Rieta, J.J. Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation (2015) Computer Methods and Programs in Biomedicine, 119 (2), pp. 110-119. Cited 8 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2015.01.006Stridh, M., Sörnmo, L. Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation (2001) IEEE Transactions on Biomedical Engineering, 48 (1), pp. 105-111. Cited 282 times. doi: 10.1109/10.900266Castells, F., Rieta, J.J., Millet, J., Zarzoso, V. Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias (2005) IEEE Transactions on Biomedical Engineering, 52 (2), pp. 258-267. Cited 126 times. doi: 10.1109/TBME.2004.840473Ladavich, S., Ghoraani, B. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity (2015) Biomedical Signal Processing and Control, 18, pp. 274-281. Cited 81 times. http://www.elsevier.com/wps/find/journalbibliographicinfo.cws_home/706718/description#bibliographicinfo doi: 10.1016/j.bspc.2015.01.007Pürerfellner, H., Pokushalov, E., Sarkar, S., Koehler, J., Zhou, R., Urban, L., Hindricks, G. P-wave evidence as a method for improving algorithm to detect atrial fibrillation in insertable cardiac monitors (Open Access) (2014) Heart Rhythm, 11 (9), pp. 1575-1583. Cited 69 times. http://www.elsevier.com/inca/publications/store/7/0/2/3/3/3/index.htt doi: 10.1016/j.hrthm.2014.06.006Babaeizadeh, S., Gregg, R.E., Helfenbein, E.D., Lindauer, J.M., Zhou, S.H. Improvements in atrial fibrillation detection for real-time monitoring (2009) Journal of Electrocardiology, 42 (6), pp. 522-526. Cited 107 times. doi: 10.1016/j.jelectrocard.2009.06.006Couceiro, R., Carvalho, P., Henriques, J., Antunes, M., Harris, M., Habetha, J. (2008) , pp. 1-5. Detection of atrial fibrillation using model-based ECG analysisHe, 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 (Open Access) (2018) Frontiers in Physiology, 9 (AUG), art. no. 1206. Cited 46 times. https://www.frontiersin.org/articles/10.3389/fphys.2018.01206/full doi: 10.3389/fphys.2018.01206Xia, Y., Wulan, N., Wang, K., Zhang, H. Detecting atrial fibrillation by deep convolutional neural networks (2018) Computers in Biology and Medicine, 93, pp. 84-92. Cited 131 times. www.elsevier.com/locate/compbiomed doi: 10.1016/j.compbiomed.2017.12.007Shi, H., Wang, H., Qin, C., Zhao, L., Liu, C. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning (2020) Computer Methods and Programs in Biomedicine, 187, art. no. 105219. Cited 9 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2019.105219Yildirim, O., Talo, M., Ciaccio, E.J., Tan, R.S., Acharya, U.R. Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records (Open Access) (2020) Computer Methods and Programs in Biomedicine, 197, art. no. 105740. Cited 12 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2020.105740Jones, N.R., Taylor, C.J., Hobbs, F.D.R., Bowman, L., Casadei, B. Screening for atrial fibrillation: A call for evidence (Open Access) (2020) European Heart Journal, 41 (10), pp. 1075-1085. Cited 39 times. http://eurheartj.oxfordjournals.org/ doi: 10.1093/eurheartj/ehz834Mandrola, J., Foy, A. Downsides of detecting atrial fibrillation in asymptomatic patients (2019) American Family Physician, 99 (6), pp. 354-355. Cited 5 times. https://www.aafp.org/afp/2019/0315/p354.pdfKotas, M., Jezewski, J., Horoba, K., Matonia, A. Application of spatio-temporal filtering to fetal electrocardiogram enhancement (2011) Computer Methods and Programs in Biomedicine, 104 (1), pp. 1-9. Cited 39 times. doi: 10.1016/j.cmpb.2010.07.004Castells, F. (2003) Blind source separation with prior source knowledge for the analysis of atrial tachyarrhythmias. Signal modelling, estimation and validation Universitat Politècnica de València, Spain Ph.D. thesisGoldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., (...), Stanley, H.E. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. (2000) Circulation, 101 (23), pp. E215-220. Cited 7358 times.Kay, S.M. Fundamentals of Statistical Signal Processing (1993) . Cited 14934 times. Prentice Hall PTRYang, B., Li, H., Wang, Q., Zhang, Y. Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces (2016) Computer Methods and Programs in Biomedicine, 129, pp. 21-28. Cited 50 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2016.02.020Miladinović, A., Ajčević, M., Jarmolowska, J., Marusic, U., Colussi, M., Silveri, G., Battaglini, P.P., (...), Accardo, A. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study (2021) Computer Methods and Programs in Biomedicine, 198, art. no. 105808. Cited 4 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2020.105808Pan, J., Tompkins, W.J. A Real-Time QRS Detection Algorithm (1985) IEEE Transactions on Biomedical Engineering, BME-32 (3), pp. 230-236. Cited 4593 times. doi: 10.1109/TBME.1985.325532Azami, H., Mohammadi, K., Bozorgtabar, B. (2012) An improved signal segmentation using moving average and Savitzky-Golay filter.Miljković, N., Popović, N., Djordjević, O., Konstantinović, L., Šekara, T.B. ECG artifact cancellation in surface EMG signals by fractional order calculus application (2017) Computer Methods and Programs in Biomedicine, 140, pp. 259-264. Cited 25 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2016.12.017Croarkin, C., Tobias, P., Filliben, J., Hembree, B., Guthrie, W. (2006) NIST/SEMATECH e-handbook of statistical methods, NIST/SEMATECH, July. Available online:. http://www.itl.nist.gov/div898/handbookMartínez, J.P., Almeida, R., Olmos, S., Rocha, A.P., Laguna, P. A Wavelet-Based ECG Delineator Evaluation on Standard Databases (2004) IEEE Transactions on Biomedical Engineering, 51 (4), pp. 570-581. Cited 1096 times. doi: 10.1109/TBME.2003.821031Saul, L.K., Allen, J.B. Periodic component analysis: an eigenvalue method for representing periodic structure in speech (2000) Nips, pp. 807-813. Cited 18 times.Sameni, R., Jutten, C., Shamsollahi, M.B. Multichannel electrocardiogram decomposition using periodic component analysis (Open Access) (2008) IEEE Transactions on Biomedical Engineering, 55 (8), art. no. 4, pp. 1935-1940. Cited 140 times. doi: 10.1109/TBME.2008.919714Monasterio, V., Clifford, G.D., Laguna, P., Martínez, J.P. A multilead scheme based on periodic component analysis for T-Wave alternans analysis in the ECG (Open Access) (2010) Annals of Biomedical Engineering, 38 (8), pp. 2532-2541. Cited 39 times. http://link.springer.com/journal/volumesAndIssues/10439 doi: 10.1007/s10439-010-0029-zLeski, J.M., Kotas, M. Hierarchical clustering with planar segments as prototypes (2015) Pattern Recognition Letters, 54, pp. 1-10. Cited 9 times. http://www.journals.elsevier.com/pattern-recognition-letters/ doi: 10.1016/j.patrec.2014.11.012Leski, J.M., Kotas, M. On robust fuzzy c-regression models (2015) Fuzzy Sets and Systems, 279, art. no. 6709, pp. 112-129. Cited 17 times. http://www.journals.elsevier.com/fuzzy-sets-and-systems/ doi: 10.1016/j.fss.2014.12.004http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL1-s2.0-S0169260721002418-main_Sonia Helena Contrer.pdf1-s2.0-S0169260721002418-main_Sonia Helena Contrer.pdfapplication/pdf3841035https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/1/1-s2.0-S0169260721002418-main_Sonia%20Helena%20Contrer.pdfd43ac8fbd656a2ba5b7d898b5bc41c69MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT1-s2.0-S0169260721002418-main_Sonia Helena Contrer.pdf.txt1-s2.0-S0169260721002418-main_Sonia Helena Contrer.pdf.txtExtracted texttext/plain46719https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/4/1-s2.0-S0169260721002418-main_Sonia%20Helena%20Contrer.pdf.txt2faf8585ed201272e13bb32ed723560aMD54THUMBNAIL1-s2.0-S0169260721002418-main_Sonia Helena Contrer.pdf.jpg1-s2.0-S0169260721002418-main_Sonia Helena Contrer.pdf.jpgGenerated Thumbnailimage/jpeg100575https://repositorio.utb.edu.co/bitstream/20.500.12585/10368/5/1-s2.0-S0169260721002418-main_Sonia%20Helena%20Contrer.pdf.jpg51a980fb1fe50844ee28554e916ab1d5MD5520.500.12585/10368oai:repositorio.utb.edu.co:20.500.12585/103682023-05-26 10:33:24.3Repositorio Institucional UTBrepositorioutb@utb.edu.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