Detección de la fibrilación auricular en el intervalo R-R

datacite.rightshttp://purl.org/coar/access_right/c_16eceng
dc.contributor.authorBenavides, Andres
dc.contributor.authorCoronell, Carlos
dc.contributor.authorGómez, Mateo
dc.contributor.authorCorrales, Leonardo
dc.date.accessioned2022-07-15T16:50:06Z
dc.date.available2022-07-15T16:50:06Z
dc.date.issued2022
dc.description.abstractLa 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.spa
dc.description.abstractAtrial 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.eng
dc.format.mimetypepdfspa
dc.identifier.urihttps://hdl.handle.net/20.500.12442/10267
dc.language.isospaspa
dc.publisherEdiciones Universidad Simón Bolívarspa
dc.publisherFacultad de Ingenieríasspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccesseng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntervalo R-Rspa
dc.subjectFibrilación Auricularspa
dc.subjectRedes Neuronalesspa
dc.subjectDiagnosticospa
dc.subjectECGspa
dc.titleDetección de la fibrilación auricular en el intervalo R-Rspa
dc.type.driverinfo:eu-repo/semantics/bachelorThesiseng
dc.type.spaTrabajo de grado - pregradospa
dcterms.references“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.spa
dcterms.referencesG. 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.spa
dcterms.referencesT. 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.spa
dcterms.referencesL. Veloza, C. Jiménez, D. Quiñones, F. Polanía, L. C. Pachón-Valero, and C. Y. Rodríguez-Triviño, “Heart rate variability as a predictive factor of cardiovascular diseases,” Revista Colombiana de Cardiologia, vol. 26, no. 4. Elsevier B.V., pp. 205–210, Jul. 01, 2019. doi: 10.1016/j.rccar.2019.01.006.eng
dcterms.references“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.spa
dcterms.referencesE. Prabhakararao and S. Dandapat, “Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead FPGA signals,” in Proceedings of 2020 IEEE Applied Signal Processing Conference, ASPCON 2020, Oct. 2020, pp. 339–343. doi: 10.1109/ASPCON49795.2020.9276690.eng
dcterms.referencesA. 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.spa
dcterms.referencesL. M. Eerikäinen, A. G. Bonomi, L. R. C. Dekker, R. Vullings, and R. M. 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.eng
dcterms.referencesS. 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.eng
dcterms.referencesS. 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.eng
dcterms.referencesS. 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.eng
dcterms.referencesO. 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.eng
dcterms.referencesIEEE 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.eng
dcterms.referencesP. 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.eng
dcterms.referencesX. 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.eng
dcterms.referencesF. 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.eng
dcterms.referencesJ. 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.eng
dcterms.referencesC. 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.eng
dcterms.referencesA. 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/EMBC46164.2021.9630957eng
dcterms.referencesM. 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.eng
dcterms.referencesÁ. 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.eng
dcterms.referencesJ. 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.eng
dcterms.referencesZ. 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.eng
dcterms.referencesJ. 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.eng
dcterms.referencesA. 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.eng
dcterms.referencesB. 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.eng
dcterms.referencesT. 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.spa
dcterms.referencesMoody 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).eng
dcterms.referencesMoody GB, Mark RG. Un nuevo método para detectar fibrilación auricular usando intervalos RR. Informática en Cardiología. 10:227-230 (1983).spa
dcterms.referencesS. Saberi, V. Esmaeili, F. Towhidkhah, and M. H. Moradi, “Predicting atrial fibrillation termination using ECG features, a comparison.”eng
dcterms.referencesK. 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.eng
dcterms.referencesD. 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.eng
dcterms.referencesD. 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.eng
dcterms.referencesP. 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.eng
dcterms.references“El Internet de las cosas (IOT): el futuro de la interconectividad”, Lenovo Tech Today México, 17-ago-2017. [En línea]. Disponible en: https://techtoday.lenovo.com/mx/es/solutions/smb/el-internet-de-las-cosas-iot-el-futuro-de-la-interconectividad.spa
oaire.versioninfo:eu-repo/semantics/acceptedVersioneng
sb.programaIngeniería de Sistemasspa
sb.sedeSede Barranquillaspa

Archivos