Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG

Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG

El electrocardiograma (ECG) es una herramienta esencial en el diagnóstico de enfermedades cardiovasculares, proporcionando información valiosa sobre el ritmo y la función del corazón. Tradicionalmente, los médicos se basaban en características heurísticas identificadas manualmente para detectar anomalías en el ECG. Sin embargo, esta metodología presentaba limitaciones en términos de precisión y fiabilidad. Con el objetivo de mejorar la precisión en la identificación de arritmias cardiacas, esta investigación se enfocó en el desarrollo de modelos basados en redes neuronales convolucionales. Se utilizaron dos conjuntos de datos: el dataset PhysioNet MIT-BIH, ampliamente utilizado en la comunidad científica, y datos adquiridos por el simulador... Ver más

Guardado en:

Revista EIA

1794-1237

2463-0950

astudillo Delgado, Victor manuel

Revelo Luna, David

Muñoz Chaves, Javier Andres

UNIVERSIDAD EIA

10.24050/reia.v21i41.1719

21

2024-01-01

4105 pp. 1

22

Colombia

Segmentación de latidos

Electrocardiograma (ECG)

Arritmias cardiacas

Dataset PhysioNet MIT-BIH

clasificación de ECG

data Augmentation

Hiperparámetros

redes neuronales convolucionales

simulador de arritmias

matriz de confusion

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

http://purl.org/coar/access_right/c_abf2

info:eu-repo/semantics/openAccess

Revista EIA - 2023

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spelling Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
Lanatá, A.; Valenza, G.; Mancuso, C. and Scilingo, E. (2011). Robust multiple cardiac arrhythmia detection through bispectrum analysis. Expert Systems with Applications, 38(6), 6798-6804. doi:https://doi.org/10.1016/j.eswa.2010.12.066
Moody, G. and Mark, R. (1989). QRS morphology representation and noise estimation using the Karhunen-Loeve transform. Proceedings. Computers in Cardiology, 269-272. doi:10.1109/CIC.1989.130540
Moody, G. and Mark, R. (2001). The Impact of the MIT-BIH Arrhythmia Database. IEEE engineering in medicine and biology, 20(3), 45-50. doi:https://doi.org/10.13026/C2F305
Moavenian, M. and Khorrami, H. (2010). A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification. Expert Systems with Applications, 37(4), 3088-3093. doi:https://doi.org/10.1016/j.eswa.2009.09.021
Luz, E.; Nunes, T.; de Albuquerque, V.; Papa, J. and Menotti, D. (2013). ECG arrhythmia classification based on optimum-path forest. Expert Systems with Applications, 40(9), 3561-3573. doi:https://doi.org/10.1016/j.eswa.2012.12.063
Liu, B.; Liu, J.; Wang, G.; Huang, K.; Li, F.; Zheng, Y.; Luo, Y. and Zhou, F. (2015). A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Computers in Biology and Medicine, 61, 178-184. doi:https://doi.org/10.1016/j.compbiomed.2014.08.010
Lin, S.W.; Ying, K.C.; Chen, S.C. and Lee, Z. J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 32(4), 1817-1824. doi:https://doi.org/10.1016/j.eswa.2007.08.088
Li, T. and Zhou, M. (2016). ECG Classification UsingWavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285
Laguna, P.; Jane, R.; Olmos, S.; Thakor, N.; Rix, H. and Caminal, P. (1996). Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model. Medical & biological engineering & computing, 34(1), 58-88. doi:https://doi.org/10.1007/BF02637023
Pal, S. (2019). ECG Monitoring: Present Status and Future Trend. En Encyclopedia of Biomedical Engineering. University of Calcutta, Kolkata, India. pp. 363-379. doi:https://doi.org/10.1016/B978-0-12-801238-3.10892-X
Kojuri, J.; Boostani, R.; Dehghani, P.; Nowroozipour, F. and Saki, N. (2015). Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. Journal of Cardiovascular Disease Research, 6(2), 51-59. doi:10.5530/jcdr.2015.2.2
Khorrami, H. and Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:https://doi.org/10.1016/j.eswa.2010.02.033
Kachuee, M.; Fazeli, S., and Sarrafzadeh, M. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. IEEE International Conference on Healthcare Informatics, 2018 IEEE International Conference on Healthcare Informatics. (ICHI). New York City, NY, USA pp. 443-444 doi:10.1109/ICHI.2018.00092
Huang, J.; Chen, B.; Yao, B. and He, W. (2019). ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network. IEEE Access, 7, 92871-92880. doi:https://doi.org/10.1109/ACCESS.2019.2928017
Gómez Herrero, G.; Gotchev, A.; Christov, I. and Egiazarian, K. (2005). Feature extraction for heartbeat classification using independent component analysis and matching pursuits. Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Philadelphia, PA, USA 4, pp.725-728. doi:10.1109/ICASSP.2005.1416111
Goldberger, A.L; Amaral, L.A.; Glass, L; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K and Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220. doi:https://doi.org/10.1161/01.CIR.101.23.e215
Chazal, P.; O'Dwyer, M. and Reilly, R. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51(7), 1196-1206. doi:10.1109/TBME.2004.827359
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
National Library of Medicine. (10 de 12 de 2020). MedlinePlus. Recuperado el 24 de 02 de 2022, de MedlinePlus: https://medlineplus.gov/lab-tests/electrocardiogram/
Pyakillya, B.; Kazachenko, N. and Mikhailovsky, N. (2017). Deep Learning for {ECG} Classification. Journal of Physics: Conference Series, IOP Publishing. 913, 012004. doi:https://doi.org/10.1088/1742-6596/913/1/012004
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http://purl.org/coar/resource_type/c_6501
Zhang, L.; Karimzadeh, M.; Welch, M.; McIntosh, C. and Wang, B. (2021). Chapter 7 - Analytics methods and tools for integration of biomedical data in medicine. Xing, L.; Giger, M.L., and Min, J.K. Artificial Intelligence in Medicine. Academic Press, pp. 113-129. doi:https://doi.org/10.1016/B978-0-12-821259-2.00007-7
Revelo Luna, D. A.; Mejía Manzano, J. E. and Munoz Chaves, J. A. (2021). Effect of Pre-processing of CT Images on the Performance of Deep Neural Networks Based Diagnosis of COVID-19. Journal of Scientific & Industrial Research, 80(11), 992-1000
Yu, S. N. and Chou, K.T. (2009). Selection of significant independent components for ECG beat classification. Expert Systems with Applications, 36(2), 2088-2096. doi:https://doi.org/10.1016/j.eswa.2007.12.016
Yu, S. N. and Chen, Y. H. (2007). Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters, 28(10), 1142-1150. doi:https://doi.org/10.1016/j.patrec.2007.01.017
Ye, C.; Coimbra, M. T.; and Kumar, V. (2010). Arrhythmia detection and classification using morphological and dynamic features of ECG signals. Annual International Conference of the IEEE Engineering in Medicine and Biology 2010, Buenos Aires, Argentina, pp.1918-1921. doi:10.1109/IEMBS.2010.5627645
World Heath Organization. (11 de 06 de 2021). World Heath Organization. Obtenido de https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Wiggins, M.; Saad, A.; Litt, B. and Vachtsevanos, G. (2008). Evolving a Bayesian classifier for ECG-based age classification in medical applications. Applied Soft Computing, 8(1), 599-608. doi:https://doi.org/10.1016/j.asoc.2007.03.009
Verma, K. K. (2021). Deep Learning Approach to Recognize COVID-19, SARS and Streptococcus. Journal of Scientific & Industrial Research, 80(01), 51-59.
Song, M.H.; Lee, J.; Cho, S.P.; Lee, K.J. and Yoo, S.K. (2005). Support vector machine based arrhythmia classification using reduced features. International journal of control automation and systems, 3(4), 571-579. doi:http://dx.doi.org/OAK-2005-06773
Safdarian, N.; Jafarnia D.N., and Attarodi, G. (2014). A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal. Journal of Biomedical Science and Engineering, 07, 818-824. doi:10.4236/jbise.2014.710081
Revista EIA - 2023
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Publication
41
El electrocardiograma (ECG) es una herramienta esencial en el diagnóstico de enfermedades cardiovasculares, proporcionando información valiosa sobre el ritmo y la función del corazón. Tradicionalmente, los médicos se basaban en características heurísticas identificadas manualmente para detectar anomalías en el ECG. Sin embargo, esta metodología presentaba limitaciones en términos de precisión y fiabilidad. Con el objetivo de mejorar la precisión en la identificación de arritmias cardiacas, esta investigación se enfocó en el desarrollo de modelos basados en redes neuronales convolucionales. Se utilizaron dos conjuntos de datos: el dataset PhysioNet MIT-BIH, ampliamente utilizado en la comunidad científica, y datos adquiridos por el simulador de arritmias Bio-Tek BP Pump NIBP. Se entrenaron cinco modelos con diferentes arquitecturas, incluyendo modelos convencionales como VGG16, ResNet-50 y AlexNet, así como dos arquitecturas propuestas por los autores. Todos los modelos se entrenaron con el mismo número de muestras y configuración de hiperparámetros. La evaluación del desempeño se realizó utilizando métricas comunes como exactitud, recall, F1-score y exactitud —accuracy—. Los resultados demostraron que la arquitectura VGG16 fue la más eficaz en la clasificación de arritmias cardiacas, alcanzando una exactitud del 98,8% en el conjunto de datos MIT-BIH. Además, al evaluar los datos de prueba del simulador Bio-Tek BP Pump NIBP, el modelo customize-2 demostró el mejor rendimiento con una exactitud del 96,3%. Estos resultados son prometedores, ya que demuestran el potencial de las redes neuronales convolucionales para mejorar la precisión en el diagnóstico de arritmias cardiacas. Los modelos desarrollados en esta investigación podrían ser una herramienta útil para los médicos en la detección temprana y el tratamiento adecuado de estas afecciones cardiovasculares.
astudillo Delgado, Victor manuel
Revelo Luna, David
Muñoz Chaves, Javier Andres
Segmentación de latidos
Electrocardiograma (ECG)
Arritmias cardiacas
Dataset PhysioNet MIT-BIH
clasificación de ECG
data Augmentation
Hiperparámetros
redes neuronales convolucionales
simulador de arritmias
21
matriz de confusion
Núm. 41 , Año 2024 : .
Artículo de revista
https://revistas.eia.edu.co/index.php/reveia/article/view/1719
application/pdf
Fondo Editorial EIA - Universidad EIA
Revista EIA
The electrocardiogram (ECG) is an essential tool in the diagnosis of cardiovascular disease, providing valuable information about heart rhythm and function. Traditionally, physicians relied on manually identified heuristic features to detect ECG abnormalities. However, this methodology had limitations in terms of accuracy and reliability. With the aim of improving accuracy in the identification of cardiac arrhythmias, this research focused on the development of models based on convolutional neural networks. Two data sets were used: the PhysioNet MIT-BIH dataset, widely used in the scientific community, and data acquired by the Bio-Tek BP Pump NIBP arrhythmia simulator. Five models were trained with different architectures, including conventional models such as (VGG16, ResNet-50 and AlexNet), as well as two architectures proposed by the authors. All models were trained with the same number of samples and hyperparameter settings. Performance evaluation was performed using common metrics such as precision, recall, F1-score and accuracy. The results showed that the VGG16 architecture was the most effective in classifying cardiac arrhythmias, achieving an accuracy of 98.8% on the MIT-BIH dataset. Furthermore, when evaluating test data from the Bio-Tek BP Pump NIBP simulator, the customize-2 model demonstrated the best performance with an accuracy of 96.3%. These results are promising, as they demonstrate the potential of convolutional neural networks to improve accuracy in the diagnosis of cardiac arrhythmias. The models developed in this research could be a useful tool for clinicians in the early detection and appropriate treatment of these cardiovascular conditions.
Beat segmentation
Electrocardiogram (ECG)
Cardiac arrhythmias
PhysioNet MIT-BIH Dataset
ECG classification
confusion matrix
data Augmentation
Hyperparameters
convolutional neural networks
arrhythmia simulator
Classification of cardiac arrhythmias using convolutional neural networks in ECG samples
Journal article
2463-0950
https://revistas.eia.edu.co/index.php/reveia/article/download/1719/1579
1794-1237
2024-01-01
2024-01-01 09:26:25
22
4105 pp. 1
2024-01-01 09:26:25
10.24050/reia.v21i41.1719
https://doi.org/10.24050/reia.v21i41.1719
institution UNIVERSIDAD EIA
thumbnail https://nuevo.metarevistas.org/UNIVERSIDADEIA/logo.png
country_str Colombia
collection Revista EIA
title Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
spellingShingle Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
astudillo Delgado, Victor manuel
Revelo Luna, David
Muñoz Chaves, Javier Andres
Segmentación de latidos
Electrocardiograma (ECG)
Arritmias cardiacas
Dataset PhysioNet MIT-BIH
clasificación de ECG
data Augmentation
Hiperparámetros
redes neuronales convolucionales
simulador de arritmias
matriz de confusion
Beat segmentation
Electrocardiogram (ECG)
Cardiac arrhythmias
PhysioNet MIT-BIH Dataset
ECG classification
confusion matrix
data Augmentation
Hyperparameters
convolutional neural networks
arrhythmia simulator
title_short Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
title_full Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
title_fullStr Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
title_full_unstemmed Clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ECG
title_sort clasificación de arritmias cardiacas usando redes neuronales convolucionales en muestras de ecg
title_eng Classification of cardiac arrhythmias using convolutional neural networks in ECG samples
description El electrocardiograma (ECG) es una herramienta esencial en el diagnóstico de enfermedades cardiovasculares, proporcionando información valiosa sobre el ritmo y la función del corazón. Tradicionalmente, los médicos se basaban en características heurísticas identificadas manualmente para detectar anomalías en el ECG. Sin embargo, esta metodología presentaba limitaciones en términos de precisión y fiabilidad. Con el objetivo de mejorar la precisión en la identificación de arritmias cardiacas, esta investigación se enfocó en el desarrollo de modelos basados en redes neuronales convolucionales. Se utilizaron dos conjuntos de datos: el dataset PhysioNet MIT-BIH, ampliamente utilizado en la comunidad científica, y datos adquiridos por el simulador de arritmias Bio-Tek BP Pump NIBP. Se entrenaron cinco modelos con diferentes arquitecturas, incluyendo modelos convencionales como VGG16, ResNet-50 y AlexNet, así como dos arquitecturas propuestas por los autores. Todos los modelos se entrenaron con el mismo número de muestras y configuración de hiperparámetros. La evaluación del desempeño se realizó utilizando métricas comunes como exactitud, recall, F1-score y exactitud —accuracy—. Los resultados demostraron que la arquitectura VGG16 fue la más eficaz en la clasificación de arritmias cardiacas, alcanzando una exactitud del 98,8% en el conjunto de datos MIT-BIH. Además, al evaluar los datos de prueba del simulador Bio-Tek BP Pump NIBP, el modelo customize-2 demostró el mejor rendimiento con una exactitud del 96,3%. Estos resultados son prometedores, ya que demuestran el potencial de las redes neuronales convolucionales para mejorar la precisión en el diagnóstico de arritmias cardiacas. Los modelos desarrollados en esta investigación podrían ser una herramienta útil para los médicos en la detección temprana y el tratamiento adecuado de estas afecciones cardiovasculares.
description_eng The electrocardiogram (ECG) is an essential tool in the diagnosis of cardiovascular disease, providing valuable information about heart rhythm and function. Traditionally, physicians relied on manually identified heuristic features to detect ECG abnormalities. However, this methodology had limitations in terms of accuracy and reliability. With the aim of improving accuracy in the identification of cardiac arrhythmias, this research focused on the development of models based on convolutional neural networks. Two data sets were used: the PhysioNet MIT-BIH dataset, widely used in the scientific community, and data acquired by the Bio-Tek BP Pump NIBP arrhythmia simulator. Five models were trained with different architectures, including conventional models such as (VGG16, ResNet-50 and AlexNet), as well as two architectures proposed by the authors. All models were trained with the same number of samples and hyperparameter settings. Performance evaluation was performed using common metrics such as precision, recall, F1-score and accuracy. The results showed that the VGG16 architecture was the most effective in classifying cardiac arrhythmias, achieving an accuracy of 98.8% on the MIT-BIH dataset. Furthermore, when evaluating test data from the Bio-Tek BP Pump NIBP simulator, the customize-2 model demonstrated the best performance with an accuracy of 96.3%. These results are promising, as they demonstrate the potential of convolutional neural networks to improve accuracy in the diagnosis of cardiac arrhythmias. The models developed in this research could be a useful tool for clinicians in the early detection and appropriate treatment of these cardiovascular conditions.
author astudillo Delgado, Victor manuel
Revelo Luna, David
Muñoz Chaves, Javier Andres
author_facet astudillo Delgado, Victor manuel
Revelo Luna, David
Muñoz Chaves, Javier Andres
topicspa_str_mv Segmentación de latidos
Electrocardiograma (ECG)
Arritmias cardiacas
Dataset PhysioNet MIT-BIH
clasificación de ECG
data Augmentation
Hiperparámetros
redes neuronales convolucionales
simulador de arritmias
matriz de confusion
topic Segmentación de latidos
Electrocardiograma (ECG)
Arritmias cardiacas
Dataset PhysioNet MIT-BIH
clasificación de ECG
data Augmentation
Hiperparámetros
redes neuronales convolucionales
simulador de arritmias
matriz de confusion
Beat segmentation
Electrocardiogram (ECG)
Cardiac arrhythmias
PhysioNet MIT-BIH Dataset
ECG classification
confusion matrix
data Augmentation
Hyperparameters
convolutional neural networks
arrhythmia simulator
topic_facet Segmentación de latidos
Electrocardiograma (ECG)
Arritmias cardiacas
Dataset PhysioNet MIT-BIH
clasificación de ECG
data Augmentation
Hiperparámetros
redes neuronales convolucionales
simulador de arritmias
matriz de confusion
Beat segmentation
Electrocardiogram (ECG)
Cardiac arrhythmias
PhysioNet MIT-BIH Dataset
ECG classification
confusion matrix
data Augmentation
Hyperparameters
convolutional neural networks
arrhythmia simulator
citationvolume 21
citationissue 41
citationedition Núm. 41 , Año 2024 : .
publisher Fondo Editorial EIA - Universidad EIA
ispartofjournal Revista EIA
source https://revistas.eia.edu.co/index.php/reveia/article/view/1719
language Español
format Article
rights Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
Revista EIA - 2023
https://creativecommons.org/licenses/by-nc-nd/4.0
references Lanatá, A.; Valenza, G.; Mancuso, C. and Scilingo, E. (2011). Robust multiple cardiac arrhythmia detection through bispectrum analysis. Expert Systems with Applications, 38(6), 6798-6804. doi:https://doi.org/10.1016/j.eswa.2010.12.066
Moody, G. and Mark, R. (1989). QRS morphology representation and noise estimation using the Karhunen-Loeve transform. Proceedings. Computers in Cardiology, 269-272. doi:10.1109/CIC.1989.130540
Moody, G. and Mark, R. (2001). The Impact of the MIT-BIH Arrhythmia Database. IEEE engineering in medicine and biology, 20(3), 45-50. doi:https://doi.org/10.13026/C2F305
Moavenian, M. and Khorrami, H. (2010). A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification. Expert Systems with Applications, 37(4), 3088-3093. doi:https://doi.org/10.1016/j.eswa.2009.09.021
Luz, E.; Nunes, T.; de Albuquerque, V.; Papa, J. and Menotti, D. (2013). ECG arrhythmia classification based on optimum-path forest. Expert Systems with Applications, 40(9), 3561-3573. doi:https://doi.org/10.1016/j.eswa.2012.12.063
Liu, B.; Liu, J.; Wang, G.; Huang, K.; Li, F.; Zheng, Y.; Luo, Y. and Zhou, F. (2015). A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Computers in Biology and Medicine, 61, 178-184. doi:https://doi.org/10.1016/j.compbiomed.2014.08.010
Lin, S.W.; Ying, K.C.; Chen, S.C. and Lee, Z. J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 32(4), 1817-1824. doi:https://doi.org/10.1016/j.eswa.2007.08.088
Li, T. and Zhou, M. (2016). ECG Classification UsingWavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285
Laguna, P.; Jane, R.; Olmos, S.; Thakor, N.; Rix, H. and Caminal, P. (1996). Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model. Medical & biological engineering & computing, 34(1), 58-88. doi:https://doi.org/10.1007/BF02637023
Pal, S. (2019). ECG Monitoring: Present Status and Future Trend. En Encyclopedia of Biomedical Engineering. University of Calcutta, Kolkata, India. pp. 363-379. doi:https://doi.org/10.1016/B978-0-12-801238-3.10892-X
Kojuri, J.; Boostani, R.; Dehghani, P.; Nowroozipour, F. and Saki, N. (2015). Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. Journal of Cardiovascular Disease Research, 6(2), 51-59. doi:10.5530/jcdr.2015.2.2
Khorrami, H. and Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:https://doi.org/10.1016/j.eswa.2010.02.033
Kachuee, M.; Fazeli, S., and Sarrafzadeh, M. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. IEEE International Conference on Healthcare Informatics, 2018 IEEE International Conference on Healthcare Informatics. (ICHI). New York City, NY, USA pp. 443-444 doi:10.1109/ICHI.2018.00092
Huang, J.; Chen, B.; Yao, B. and He, W. (2019). ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network. IEEE Access, 7, 92871-92880. doi:https://doi.org/10.1109/ACCESS.2019.2928017
Gómez Herrero, G.; Gotchev, A.; Christov, I. and Egiazarian, K. (2005). Feature extraction for heartbeat classification using independent component analysis and matching pursuits. Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Philadelphia, PA, USA 4, pp.725-728. doi:10.1109/ICASSP.2005.1416111
Goldberger, A.L; Amaral, L.A.; Glass, L; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K and Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220. doi:https://doi.org/10.1161/01.CIR.101.23.e215
Chazal, P.; O'Dwyer, M. and Reilly, R. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51(7), 1196-1206. doi:10.1109/TBME.2004.827359
National Library of Medicine. (10 de 12 de 2020). MedlinePlus. Recuperado el 24 de 02 de 2022, de MedlinePlus: https://medlineplus.gov/lab-tests/electrocardiogram/
Pyakillya, B.; Kazachenko, N. and Mikhailovsky, N. (2017). Deep Learning for {ECG} Classification. Journal of Physics: Conference Series, IOP Publishing. 913, 012004. doi:https://doi.org/10.1088/1742-6596/913/1/012004
Zhang, L.; Karimzadeh, M.; Welch, M.; McIntosh, C. and Wang, B. (2021). Chapter 7 - Analytics methods and tools for integration of biomedical data in medicine. Xing, L.; Giger, M.L., and Min, J.K. Artificial Intelligence in Medicine. Academic Press, pp. 113-129. doi:https://doi.org/10.1016/B978-0-12-821259-2.00007-7
Revelo Luna, D. A.; Mejía Manzano, J. E. and Munoz Chaves, J. A. (2021). Effect of Pre-processing of CT Images on the Performance of Deep Neural Networks Based Diagnosis of COVID-19. Journal of Scientific & Industrial Research, 80(11), 992-1000
Yu, S. N. and Chou, K.T. (2009). Selection of significant independent components for ECG beat classification. Expert Systems with Applications, 36(2), 2088-2096. doi:https://doi.org/10.1016/j.eswa.2007.12.016
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