Detección de la fibrilación auricular en el intervalo R-R

La fibrilación auricular es cuando se presenta un ritmo cardiaco irregular, normalmente se vincula con un latido rápido. Una de las formas de comprobar si la persona está padeciendo un de FA es por medio del pulso o por medio de un electrocardiograma. Para diagnosticar FA en una persona se tiene en...

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Autores:
Benavides, Andres
Coronell, Carlos
Gómez, Mateo
Corrales, Leonardo
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
spa
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/10267
Acceso en línea:
https://hdl.handle.net/20.500.12442/10267
Palabra clave:
Intervalo R-R
Fibrilación Auricular
Redes Neuronales
Diagnostico
ECG
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restrictedAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.title.spa.fl_str_mv Detección de la fibrilación auricular en el intervalo R-R
title Detección de la fibrilación auricular en el intervalo R-R
spellingShingle Detección de la fibrilación auricular en el intervalo R-R
Intervalo R-R
Fibrilación Auricular
Redes Neuronales
Diagnostico
ECG
title_short Detección de la fibrilación auricular en el intervalo R-R
title_full Detección de la fibrilación auricular en el intervalo R-R
title_fullStr Detección de la fibrilación auricular en el intervalo R-R
title_full_unstemmed Detección de la fibrilación auricular en el intervalo R-R
title_sort Detección de la fibrilación auricular en el intervalo R-R
dc.creator.fl_str_mv Benavides, Andres
Coronell, Carlos
Gómez, Mateo
Corrales, Leonardo
dc.contributor.author.none.fl_str_mv Benavides, Andres
Coronell, Carlos
Gómez, Mateo
Corrales, Leonardo
dc.subject.spa.fl_str_mv Intervalo R-R
Fibrilación Auricular
Redes Neuronales
Diagnostico
ECG
topic Intervalo R-R
Fibrilación Auricular
Redes Neuronales
Diagnostico
ECG
description La fibrilación auricular es cuando se presenta un ritmo cardiaco irregular, normalmente se vincula con un latido rápido. Una de las formas de comprobar si la persona está padeciendo un de FA es por medio del pulso o por medio de un electrocardiograma. Para diagnosticar FA en una persona se tiene en cuenta la duración de la lectura entre ondas R o conocido como intervalo R-R, además de tener en cuenta la variabilidad de la frecuencia cardiaca (HRV). El objetivo de esta investigación es comparar metodologías y determinar una capaz de detectar FA con una precisión exacta y a la vez lograr implementarlas en un dispositivo portátil para el alcance del público. Se tuvieron en cuenta varias bases de datos obtenidas del repositorio PhysioNet con datos extraídos de un ECG. Al hacer las comparaciones entre métodos, se obtuvo un método donde tiene un porcentaje del 100% en precisión.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-07-15T16:50:06Z
dc.date.available.none.fl_str_mv 2022-07-15T16:50:06Z
dc.date.issued.none.fl_str_mv 2022
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dc.type.spa.spa.fl_str_mv Trabajo de grado - pregrado
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/10267
url https://hdl.handle.net/20.500.12442/10267
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Facultad de Ingenierías
institution Universidad Simón Bolívar
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spelling Benavides, Andresc79cac15-11a1-41bd-a050-83ca395437f9Coronell, Carlosd8db2319-89ec-4ead-b3bd-3f74be71dc31Gómez, Mateob7475a09-1444-42ec-84bf-b495f8b7d518Corrales, Leonardo925386bd-4c97-45e2-8574-5cd9f42e1bb42022-07-15T16:50:06Z2022-07-15T16:50:06Z2022https://hdl.handle.net/20.500.12442/10267La fibrilación auricular es cuando se presenta un ritmo cardiaco irregular, normalmente se vincula con un latido rápido. Una de las formas de comprobar si la persona está padeciendo un de FA es por medio del pulso o por medio de un electrocardiograma. Para diagnosticar FA en una persona se tiene en cuenta la duración de la lectura entre ondas R o conocido como intervalo R-R, además de tener en cuenta la variabilidad de la frecuencia cardiaca (HRV). El objetivo de esta investigación es comparar metodologías y determinar una capaz de detectar FA con una precisión exacta y a la vez lograr implementarlas en un dispositivo portátil para el alcance del público. Se tuvieron en cuenta varias bases de datos obtenidas del repositorio PhysioNet con datos extraídos de un ECG. Al hacer las comparaciones entre métodos, se obtuvo un método donde tiene un porcentaje del 100% en precisión.Atrial fibrillation is when an irregular heart rhythm occurs, usually associated with a rapid heartbeat. One of the ways to check if the person is suffering from AF is through the pulse or through an electrocardiogram. To diagnose AF in a person, the duration of the reading between R waves or known as the R-R interval is taken into account, in addition to taking into account the variability of the heart rate (HRV). The objective of this research is to compare methodologies and determine one capable of detecting AF with exact precision and at the same time to implement them in a portable device for the public. Several databases obtained from the PhysioNet repository with data extracted from an-ECG were taken into account. When making the comparisons between methods, a method was obtained where it has a percentage of 100% in precision.pdfspaEdiciones Universidad Simón BolívarFacultad de IngenieríasAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecIntervalo R-RFibrilación AuricularRedes NeuronalesDiagnosticoECGDetección de la fibrilación auricular en el intervalo R-Rinfo:eu-repo/semantics/bachelorThesisTrabajo de grado - pregradohttp://purl.org/coar/resource_type/c_7a1f“Fibrilación auricular”, Mayoclinic.org, 14-dic-2021. [En línea]. Disponible en: https://www.mayoclinic.org/es-es/diseases-conditions/atrial-fibrillation/symptoms-causes/syc-20350624.G. Mora-Pabón, “Evaluación de la fibrilación auricular mediante electrocardiograma y Holter,” Revista Colombiana de Cardiologia, vol. 23, pp. 27–33, Dec. 2016, doi: 10.1016/j.rccar.2016.10.006.T. Guterman, “Variabilidad de la frecuencia cardiaca, una herramienta útil”, Efdeportes.com. [En línea]. Disponible en: https://www.efdeportes.com/efd121/variabilidad-de-la-frecuencia-cardiaca-una-herramienta-util.htm.“D. G. F. Ramírez, “repositorio universidad de los andes”, https://repositorio.uniandes.edu.co/bitstream/handle/1992/55580/26181.pdf?sequence=1. [En línea]. Disponible en: https://repositorio.uniandes.edu.co/bitstream/handle/1992/55580/26181.pdf?sequence=1.A. Brasoveanu, M. Moodie, and R. Agrawal, “Textual evidence for the perfunctoriness of independent medical reviews,” in CEUR Workshop Proceedings, 2020, vol. 2657, pp. 1–9. doi: 10.1145/nnnnnnn.nnnnnnn.T. Guterman, “Variabilidad de la frecuencia cardiaca, una herramienta útil”, Efdeportes.com. [En línea]. Disponible en: https://www.efdeportes.com/efd121/variabilidad-de-la-frecuencia-cardiaca-una-herramienta-util.htm.Moody GB, Mark RG. Un nuevo método para detectar fibrilación auricular usando intervalos RR. 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Aarts, “Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review,” Cardiovascular Digital Health Journal, vol. 1, no. 1, pp. 45–51, Jul. 2020, doi: 10.1016/j.cvdhj.2020.03.001.S. Banerjee, A. Paul, A. Agarwal, and S. K. Jindal, “Real Time Arrhythmia Detecting Wearable using a Novel Deep Learning Model,” in Proceedings - 2020 International Conference on Interdisciplinary Cyber Physical Systems, ICPS 2020, Dec. 2020, pp. 14–19. doi: 10.1109/ICPS51508.2020.00009.S. P. Shashikumar, A. J. Shah, Q. Li, G. D. Clifford, and S. Nemati, “A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology,” in 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, Apr. 2017, pp. 141–144. doi: 10.1109/BHI.2017.7897225.S. Sadasivuni, R. Chowdhury, V. E. G. Karnam, I. Banerjee, and A. Sanyal, “Recurrent neural network circuit for automated detection of atrial fibrillation from raw ECG,” in Proceedings - IEEE International Symposium on Circuits and Systems, 2021, vol. 2021-May. doi: 10.1109/ISCAS51556.2021.9401666.O. Aligholipour, M. Kuntalp, and S. Sadaghiyanfam, “Silent paroxysmal atrial fibrillation detection by neural networks based on ECG records,” Apr. 2019. doi: 10.1109/EBBT.2019.8741771.IEEE Circuits and Systems Society and Institute of Electrical and Electronics Engineers, 2019 IEEE International Symposium on Circuits and Systems (ISCAS) : proceedings : ISCAS 2019 : Sapporo, Japan, May 26-29 2019.P. Panindre, V. Gandhi, and S. Kumar, “Comparison of Performance of Artificial Intelligence Algorithms for Real-Time Atrial Fibrillation Detection using Instantaneous Heart Rate,” in HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI, Dec. 2020, pp. 168–172. doi: 10.1109/HONET50430.2020.9322658.X. Fan, Q. Yao, Y. Cai, F. Miao, F. Sun, and Y. Li, “Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation from Single Lead Short ECG Recordings,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 6, pp. 1744–1753, Nov. 2018, doi: 10.1109/JBHI.2018.2858789.F. Ma, J. Zhang, W. Liang, and J. Xue, “Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices,” Mathematical Problems in Engineering, vol. 2020, 2020, doi: 10.1155/2020/9159158.J. van Zaen et al., “Atrial Fibrillation Detection from PPG Interbeat Intervals via a Recurrent Neural Network,” in 2019 Computing in Cardiology Conference (CinC), Dec. 2019, vol. 45. doi: 10.22489/cinc.2019.084.C. T. January et al., “2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: A report of the American college of Cardiology/American heart association task force on practice guidelines and the heart rhythm society,” J Am Coll Cardiol, vol. 64, no. 21, pp. e1–e76, Dec. 2014, doi: 10.1016/j.jacc.2014.03.022.A. Faraone, H. Sigurthorsdottir, and R. Delgado-Gonzalo, “Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2021, pp. 6795–6799. doi: 10.1109/EMBC46164.2021.9630957M. Shao, Z. Zhou, G. Bin, Y. Bai, and S. Wu, “A wearable electrocardiogram telemonitoring system for atrial fibrillation detection,” Sensors (Switzerland), vol. 20, no. 3, Feb. 2020, doi: 10.3390/s20030606.Á. H. Herraiz, A. Martínez-Rodrigo, V. Bertomeu-González, A. Quesada, J. J. Rieta, and R. Alcaraz, “A deep learning approach for featureless robust quality assessment of intermittent atrial fibrillation recordings from portable and wearable devices,” Entropy, vol. 22, no. 7, Jul. 2020, doi: 10.3390/E22070733.J. Ramesh, Z. Solatidehkordi, R. Aburukba, and A. Sagahyroon, “Atrial fibrillation classification with smart wearables using short-term heart rate variability and deep convolutional neural networks,” Sensors, vol. 21, no. 21, Nov. 2021, doi: 10.3390/s21217233.Z. Xiong, M. K. Stiles, A. M. Gillis, and J. Zhao, “Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks,” Computers in Biology and Medicine, vol. 146, p. 105551, Jul. 2022, doi: 10.1016/j.compbiomed.2022.105551.J. A. Rincon, S. Guerra-Ojeda, C. Carrascosa, and V. Julian, “An IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks,” Sensors (Switzerland), vol. 20, no. 24, pp. 1–19, Dec. 2020, doi: 10.3390/s20247353.A. Brasoveanu, M. Moodie, and R. Agrawal, “Textual evidence for the perfunctoriness of independent medical reviews,” in CEUR Workshop Proceedings, 2020, vol. 2657, pp. 1–9. doi: 10.1145/nnnnnnn.nnnnnnn.B. Król-Józaga, “Atrial fibrillation detection using convolutional neural networks on 2-dimensional representation of ECG signal,” Biomedical Signal Processing and Control, vol. 74, Apr. 2022, doi: 10.1016/j.bspc.2021.103470.Moody GB, Goldberger AL, McClennen S, Swiryn SP. Predicting the Onset of Paroxysmal Atrial Fibrillation: The Computers in Cardiology Challenge 2001. Computers in Cardiology 28:113-116 (2001).S. Saberi, V. Esmaeili, F. Towhidkhah, and M. H. Moradi, “Predicting atrial fibrillation termination using ECG features, a comparison.”K. Tahsin, M. F. Hossain, and M. A. Rahman, “Computer Aided Atrial Fibrillation Detection from the Statistical Attributes of ECG Signal,” 2021. doi: 10.1109/ICECIT54077.2021.9641198.D. R. Seshadri et al., “Accuracy of the apple watch 4 to measure heart rate in patients with atrial fibrillation,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, 2020, doi: 10.1109/JTEHM.2019.2950397.D. Lai, X. Zhang, Y. Zhang, and M. Belal Bin Heyat, Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum *; Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum * 2019. doi: 10.0/Linux-x86_64.P. Siwindarto, A. B. DIanisma, Z. Abidin, and S. S. Mahmadov, “ECG signal processing for early detection of atrial and ventricular fibrillation based on R-R interval,” in EECCIS 2020 - 2020 10th Electrical Power, Electronics, Communications, Controls, and Informatics Seminar, Aug. 2020, pp. 142–146. doi: 10.1109/EECCIS49483.2020.9263454.Sede BarranquillaIngeniería de SistemasORIGINALPDF.pdfPDF.pdfapplication/pdf893218https://bonga.unisimon.edu.co/bitstreams/d5b67158-e573-4aa3-859b-22458aedd9d2/download2f2acc4de80c4436a52b2bb734df6c3bMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/b2a6e68a-8faf-4ca3-a10e-2792c1b1d58a/download4460e5956bc1d1639be9ae6146a50347MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-83000https://bonga.unisimon.edu.co/bitstreams/b7992a6f-3ba9-4422-a577-1b13867a8516/download2a1661e5960a7bab4fd8dda692fb677cMD54TEXTDetección_Fibrilación_Auricular_IntervaloR-R_Artículo.pdf.txtDetección_Fibrilación_Auricular_IntervaloR-R_Artículo.pdf.txtExtracted texttext/plain45750https://bonga.unisimon.edu.co/bitstreams/e2283344-8e0c-41f2-bfcf-068fedcff91b/download05933b29dec27e84f80e0c79dba6fc47MD55PDF.txtPDF.txtExtracted texttext/plain45750https://bonga.unisimon.edu.co/bitstreams/ef5e52d0-0b77-4d17-97af-c1a86560e866/download05933b29dec27e84f80e0c79dba6fc47MD57PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain45750https://bonga.unisimon.edu.co/bitstreams/5502143e-0730-455c-bff6-ab0aa651a424/download05933b29dec27e84f80e0c79dba6fc47MD59THUMBNAILDetección_Fibrilación_Auricular_IntervaloR-R_Artículo.pdf.jpgDetección_Fibrilación_Auricular_IntervaloR-R_Artículo.pdf.jpgGenerated Thumbnailimage/jpeg5295https://bonga.unisimon.edu.co/bitstreams/9ea27cb8-ff33-4d35-896e-76e9de549788/download6963734405c987974a0c30d20dbdb78bMD56PDF.jpgPDF.jpgGenerated Thumbnailimage/jpeg5295https://bonga.unisimon.edu.co/bitstreams/ac307265-fed7-48c9-9a07-c9b5f21e3179/download6963734405c987974a0c30d20dbdb78bMD58PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg5295https://bonga.unisimon.edu.co/bitstreams/76da236b-70ad-47e3-9763-53be2c80ea20/download6963734405c987974a0c30d20dbdb78bMD51020.500.12442/10267oai:bonga.unisimon.edu.co:20.500.12442/102672024-08-14 21:54:43.934http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalrestrictedhttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.co

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