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dc.contributor.authorCastillo, Jeyson A.
dc.contributor.authorGranados, Yenny C.
dc.contributor.authorFajardo Ariza, Carlos Augusto
dc.date.accessioned2020-01-08T19:11:41Z
dc.date.available2020-01-08T19:11:41Z
dc.date.issued2019-11-08
dc.identifierhttp://revistas.unimilitar.edu.co/index.php/rcin/article/view/4156
dc.identifier10.18359/rcin.4156
dc.identifier.urihttp://hdl.handle.net/10654/33461
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.eng
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.spa
dc.formatapplication/pdf
dc.language.isoeng
dc.publisherUniversidad Militar Nueva Granadaspa
dc.rightsDerechos de autor 2019 Ciencia e Ingeniería Neogranadinaspa
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourceCiencia e Ingenieria Neogranadina; Vol 30 No 1 (2020)eng
dc.sourceCiencia e Ingeniería Neogranadina; Vol. 30 Núm. 1 (2020)spa
dc.sourceCiencia e Ingeniería Neogranadina; v. 30 n. 1 (2020)por
dc.source1909-7735
dc.source0124-8170
dc.titlePatient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networkseng
dc.titleDetección de fibrilación auricular en señales ECG usando Redes Neuronales para pacientes específicosspa
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeTextoeng
dc.relation.referenceshttp://revistas.unimilitar.edu.co/index.php/rcin/article/view/4156/3391
dc.relation.references/*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 [2] 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 [3] 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 [4] G. AL, "The MIT-BIH Atrial Fibrillation Database." https://physionet.org/physiobank/database/afdb/, June 2000. Accessed: 2019-01-26. [5] 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 [6] 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 [7] 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 [8] A. L. Pérez C, Bllanco M, Toledo D, "Cardiólogos en Colombia," Educación en Cardiología, vol. 10, no. 6, pp. 378-385, 2003. [9] 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 [10] 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 [11] 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 [12] 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 [13] "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 [14] 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 [15] 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 [16] 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 [17] 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 [18] 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 [19] 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 [20] 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 [21] 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 [22] 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.subject.proposalAtrial Fibrillationeng
dc.subject.proposalAutomatic Detectioneng
dc.subject.proposalConvolutional Neural Networkseng
dc.subject.proposalDeep Neural Networkseng
dc.subject.proposalECGeng
dc.subject.proposalDetección automáticaspa
dc.subject.proposalECGspa
dc.subject.proposalFibrilación Auricularspa
dc.subject.proposalRedes Neuronales Convolucionalesspa
dc.subject.proposalRedes Neuronales Profundasspa


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