Mostrar el registro sencillo del ítem

Detección de fibrilación auricular en señales ECG usando Redes Neuronales para pacientes específicos

dc.creatorCastillo, Jeyson A.
dc.creatorGranados, Yenny C.
dc.creatorFajardo Ariza, Carlos Augusto
dc.date2019-11-12
dc.identifierhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/4156
dc.identifier10.18359/rcin.4156
dc.descriptionAtrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of  and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.en-US
dc.descriptionLa fibrilación auricular (FA) es la arritmia cardíaca más común en todo el mundo. Se asocia con una reducción de la calidad de vida y aumenta el riesgo de accidente cerebrovascular e infarto de miocardio. Desafortunadamente muchos casos de FA son asintomáticos, lo cual aumenta el riesgo para los pacientes. Debido a su naturaleza paroxística, la detección de la FA requiere la evaluación, por parte de un cardiologo, de señales ECG de larda duración. En Colombia, es difícil dificil tener dianóstico temprano de la FA debido a los costos asociados a la detección de la FA y la distribución geográfica de los cardiólogos. Este trabajo es parte de un proyecto macro que tiene como objetivo desarrollar un dispositivo portátil para pacientes específicos que permita detectar la FA, el cual estará basado en una red neuronal convolucional (CNN). Nuestro objetivo es encontrar un modelo CNN adecuado, que luego se pueda implementar en hardware. Se aplicaron diversas técnicas para mejorar la respuesta con respecto a la exactitud, la sensibilidad, la especificidad y la precisión. El modelo final alcanza una exactitud del 97,44%, una especificidad del 97,76%, una sensibilidad del 96,97% y una precisión del 96,80%. Durante el desarrollo del modelo, el costo computacional y los recursos de memoria se tuvieron en cuenta para obtener un modelo de hardware eficiente en una futura  implementación del dispositivo.es-ES
dc.formatapplication/pdf
dc.formattext/xml
dc.languageeng
dc.publisherUniversidad Militar Nueva Granadaes-ES
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/4156/4080
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/4156/4249
dc.relation/*ref*/J. E. Forero-Gómez, J. M. Moreno, C. A. Agudelo, E. A. Rodríguez-Arias, and P. A. Sánchez-Moscoso, "Fibrilación auricular: Enfoque para el médico no cardiólogo," Latreia, vol. 30, no. 4, pp. 404-422, 2017.https://doi.org/10.17533/udea.iatreia.v30n4a05
dc.relation/*ref*/M. Romero and D. Chávez, "Carga de enfermedad atribuible a fibrilación auricular en colombia (2000-2009)," Revista Colombiana de Cardiología, vol. 21, no. 6, pp. 374-381, 2014.https://doi.org/10.1016/j.rccar.2014.08.006
dc.relation/*ref*/U. R. Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan, and M. Adam, "Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network" Information Sciences, vol. 405, pp. 81 - 90, 2017. https://doi.org/10.1016/j.ins.2017.04.012
dc.relation/*ref*/G. AL, "The MIT-BIH Atrial Fibrillation Database." https://physionet.org/physiobank/database/afdb/, June 2000. Accessed: 2019-01-26.
dc.relation/*ref*/H. V. Bhatt and G. W. Fischer, "Atrial Fibrillation: Pathophysiology and Therapeutic Options," Journal of Cardiothoracic and Vascular Anesthesia, vol. 29, no. 5, pp. 1333-1340, 2015.https://doi.org/10.1053/j.jvca.2015.05.058
dc.relation/*ref*/P. Kirchhof, S. Benussi, D. Kotecha, A. Ahlsson, D. Atar, B. Casadei, M. Castella, H.-C. Diener, H. Heidbuchel, J. Hendriks, et al., "2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS" European journal of cardio-thoracic surgery, vol. 50, no. 5, pp. e1-e88, 2016. https://doi.org/10.5603/KP.2016.0172
dc.relation/*ref*/J. K. Galvez-Olortegui, M. L. Álvarez-Vargas, T. V. Galvez-Olortegui, A. Godoy-Palomino, and L. Camacho-Saavedra, "Current clinical practice guidelines in atrial fibrillation: a review," Medwave, vol. 16, no. 01, 2016. https://doi.org/10.5867/medwave.2016.01.6365
dc.relation/*ref*/A. L. Pérez C, Bllanco M, Toledo D, "Cardiólogos en Colombia," Educación en Cardiología, vol. 10, no. 6, pp. 378-385, 2003.
dc.relation/*ref*/S. Kiranyaz, T. Ince, and M. Gabbouj, "Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks," IEEE Transactions on Biomedical Engineering, vol. 63, pp. 664-675, March 2016. https://doi.org/10.1109/TBME.2015.2468589
dc.relation/*ref*/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," 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, pp. 141-144, 2017.https://doi.org/10.1109/BHI.2017.7897225
dc.relation/*ref*/J. Rubin, S. Parvaneh, A. Rahman, B. Conroy, and S. Babaeizadeh, "Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings," Computing in Cardiology, vol. 44, pp. 2-5, 2017.https://doi.org/10.22489/CinC.2017.160-246
dc.relation/*ref*/K. Fukushima, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position," Biological cybernetics, vol. 36, pp. 193-202, 02 1980.https://doi.org/10.1007/BF00344251
dc.relation/*ref*/"Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, pp. 2278-2324, Nov 1998. https://doi.org/10.1109/5.726791
dc.relation/*ref*/S. Hayat, S. Kun, Z. Tengtao, Y. Yu, T. Tu, and Y. Du, "A deep learning framework using convolutional neural network for multi-class object recognition," in 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 194-198, June 2018.https://doi.org/10.1109/ICIVC.2018.8492777
dc.relation/*ref*/T. Ishii, R. Nakamura, H. Nakada, Y. Mochizuki, and H. Ishikawa, "Surface object recognition with cnn and svm in landsat 8 images," in 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 341-344, May 2015. https://doi.org/10.1109/MVA.2015.7153200
dc.relation/*ref*/N. Yu, P. Jiao, and Y. Zheng, "Handwritten digits recognition base on improved lenet5," in The 27th Chinese Control and Decision Conference (2015 CCDC), pp. 4871-4875, May 2015.https://doi.org/10.1109/CCDC.2015.7162796
dc.relation/*ref*/S. Pan, Y. Wang, C. Liu, and X. Ding, "A discriminative cascade cnn model for offline handwritten digit recognition," in 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 501-504, May 2015. https://doi.org/10.1109/MVA.2015.7153240
dc.relation/*ref*/K. Sirinukunwattana, S. E. A. Raza, Y. Tsang, D. R. J. Snead, I. A. Cree, and N. M. Rajpoot, "Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images," IEEE Transactions on Medical Imaging, vol. 35, pp. 1196-1206, May 2016. https://doi.org/10.1109/TMI.2016.2525803
dc.relation/*ref*/M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, "Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images," IEEE Transactions on Medical Imaging, vol. 35, pp. 1273-1284, May 2016. https://doi.org/10.1109/TMI.2016.2526689
dc.relation/*ref*/Z. Liu, X. Meng, J. Cui, Z. Huang, and J. Wu, "Automatic identification of abnormalities in 12-lead ecgs using expert features and convolutional neural networks," in 2018 International Conference on Sensor Networks and Signal Processing (SNSP), pp. 163-167, Oct 2018. https://doi.org/10.1109/SNSP.2018.00038
dc.relation/*ref*/A. Namozov and Y. I. Cho, "Convolutional neural network algorithm with parameterized activation function for melanoma classification," in 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 417-419, Oct 2018.https://doi.org/10.1109/ICTC.2018.8539451
dc.relation/*ref*/B. Khagi, C. G. Lee, and G. Kwon, "Alzheimer's disease classification from brain MRI based on transfer learning from CNN," in 2018 11th Biomedical Engineering International Conference (BMEiCON), pp. 1-4, Nov 2018. https://doi.org/10.1109/BMEiCON.2018.8609974
dc.rightsDerechos de autor 2019 Ciencia e Ingeniería Neogranadinaes-ES
dc.sourceCiencia e Ingenieria Neogranadina; Vol. 30 No. 1 (2020); 45-58en-US
dc.sourceCiencia e Ingeniería Neogranadina; Vol. 30 Núm. 1 (2020); 45-58es-ES
dc.sourceCiencia e Ingeniería Neogranadina; v. 30 n. 1 (2020); 45-58pt-BR
dc.source1909-7735
dc.source0124-8170
dc.subjectAtrial Fibrillationen-US
dc.subjectAutomatic Detectionen-US
dc.subjectConvolutional Neural Networksen-US
dc.subjectDeep Neural Networksen-US
dc.subjectECGen-US
dc.subjectDetección automáticaes-ES
dc.subjectECGes-ES
dc.subjectFibrilación Auriculares-ES
dc.subjectRedes Neuronales Convolucionaleses-ES
dc.subjectRedes Neuronales Profundases-ES
dc.titlePatient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networksen-US
dc.titleDetección de fibrilación auricular en señales ECG usando Redes Neuronales para pacientes específicoses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeTextoen-US


Archivos en el ítem

ArchivosTamañoFormatoVer

No hay archivos asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem