Logotipo del repositorio
Logotipo del repositorio
 

Revisión Sistemática de Literatura para la Detección de fibrilación auricular por medio de redes convolucionales 1D

Vista sencilla de ítem
datacite.rightshttp://purl.org/coar/access_right/c_16ecspa
dc.contributor.authorCueto, O.
dc.contributor.authorMartínez, J.
dc.contributor.authorMárquez, C.
dc.contributor.authorVillanueva, A.
dc.date.accessioned2023-01-24T13:20:24Z
dc.date.available2023-01-24T13:20:24Z
dc.date.issued2022
dc.description.abstractEl siguiente trabajo presenta un proceso investigativo sobre el diagnóstico de la FA o Fibrilación Auricular por medio de una red neuronal convolucional 1D. La FA es un cambio en el ritmo cardiaco, es decir, es una irregularidad en el tiempo de nuestros latidos que puede afectar nuestra calidad de vida al punto de matarnos y es considerado el tipo de arritmia más común del mundo. Una red neuronal convolucional son neuronas artificiales las cuales por medio de matrices bidimensionales son muy efectivas para tareas de clasificación y segmentación de información, este tipo de tarea es muy importante al momento de diagnosticar una enfermedad como la FA. Este trabajo se encamina específicamente en la búsqueda de información sobre el diagnóstico de la FA por medio de la red neuronal mencionada, con el objetivo de poder brindar y recomendar soluciones viables evaluadas por medio de su precisión, eficacia, rapidez.spa
dc.description.abstractThe following work presents an investigative process on the diagnosis of AF or Atrial Fibrillation through a 1D convolutional neural network. AF is a change in heart rhythm, that is, it is an irregularity in the timing of our heartbeats that can affect our quality of life to the point of killing us and is considered the most common type of arrhythmia in the world. A convolutional neural network are artificial neurons which through two-dimensional arrays are very effective for information classification and segmentation tasks, this type of task is very important when diagnosing a disease such as AF. This work is specifically aimed at the search for information on the diagnosis of AF through the neural network, with the aim of being able to provide and recommend viable solutions evaluated through their accuracy, effectiveness, and speed.eng
dc.format.mimetypepdfspa
dc.identifier.urihttps://hdl.handle.net/20.500.12442/11751
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/restrictedAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFibrilación auricularspa
dc.subjectOndasspa
dc.subjectRedes neuronalesspa
dc.subjectConvolucionalspa
dc.subjectInteligencia artificialspa
dc.subjectGráficosspa
dc.subjectIoTspa
dc.subjectAtrial fibrillationeng
dc.subjectWaveseng
dc.subjectNeural networkseng
dc.subjectConvolutionaleng
dc.subjectArtificial intelligenceeng
dc.subjectGraphicseng
dc.titleRevisión Sistemática de Literatura para la Detección de fibrilación auricular por medio de redes convolucionales 1Dspa
dc.title.translatedSystematic Literature Review for the Detection of atrial fibrillation by means of 1D convolutional networkseng
dc.type.driverinfo:eu-repo/semantics/bachelorThesisspa
dc.type.spaTrabajo de grado - pregradospa
dcterms.referencesLi Xiaolin, Barry Cardiff, Deepu John. (2020). A 1D Convolutional Neural Network for Heartbeat Classification from Single Lead ECG. https://ezproxy.unisimon.edu.co:2131/document/9294838eng
dcterms.referencesPeng Zhang, Yuting Chen, Fan Lin, Sifan Wu, Xiaoyun Yang, Qiang Li. (2020). Semi-supervised learning for automatic atrial fibrillation detection in 24-hour Holter monitoring. https://ezproxy.unisimon.edu.co:2131/document/9772345eng
dcterms.referencesReza Firoozabadi, Richard E Gregg, Saeed Babaeizadeh. (2018). P-wave Analysis in Atrial Fibrillation Detection Using a Neural Network Clustering Algorithm. https://ezproxy.unisimon.edu.co:2131/document/8743886eng
dcterms.referencesJelena Kornej, Christin S. Börschel, Emelia J. Benjamin, Renate B. Schnabel. (2020). Epidemiology of Atrial Fibrillation in the 21st Century. https://www.ahajournals.org/doi/10.1161/CIRCRESAHA.120.316340#:~:text=The%20overall%20prevalence%20of%20AF,is%201%25%20to%202%25.&text=AF%20prevalence%20and%20incidence%20in,burden%20of%20comorbidities%20in%20blacks.eng
dcterms.referencesJuqian Zhang, Søren Paaske Johnsen, Yutao Guo, Gregory Y.H Lip. (2022). Epidemiology of Atrial Fibrillation. file:///C:/Users/JUAN%20DIEGO/Downloads/Epidemiology%20of%20Atrial%20Fibrillation.pdfeng
dcterms.referencesAutor (N/A). Fecha (N/A). Red Neuronal Artificial, https://es.wikipedia.org/wiki/Red_neuronal_artificialspa
dcterms.referencesJuan Barrios. Fecha (N/A). Redes Neuronales Convolucionales. https://www.juanbarrios.com/redes-neurales-convolucionales/spa
dcterms.referencesAutor (N/A), Fecha (N/A). ¿Qué es el Internet de las Cosas (IoT)?. https://internetdelascosas.xyz/articulo.php?id=37&titulo=Que-es-el-Internet-de-las-cosas-IoT&secc=DIVULGACIONspa
dcterms.referencesJunaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz, Turker Ince, Moncef Gabbou. (2021). Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks. https://ezproxy.unisimon.edu.co:2131/document/9652095eng
dcterms.referencesMd Remon Hasan Apu, Fahmeda Akter, Mst Farzana Akhtar Lubna, Tanjina Helaly, Tanmoy Sarkar Pias. (2021). ECG Arrhythmia Classification Using 1D CNN Leveraging the Resampling Technique and Gaussian Mixture Model. https://ezproxy.unisimon.edu.co:2131/document/9564201eng
dcterms.referencesAdrian Vulpe-Grigorasi, Ovidiu Grigore. (2021). A Neural Network Approach for Anxiety Detection Based on ECG. https://ezproxy.unisimon.edu.co:2131/document/9657544eng
dcterms.referencesSengwoo Han, Heesang Eom, Juhyeong Kim, Cheolsoo Park. (2020). Optimal DNN architecture search using Bayesian Optimization Hyperband for arrhythmia detection. https://ezproxy.unisimon.edu.co:2131/document/9295590eng
dcterms.referencesAlexander Alexis Suárez León, José Ricardo Núñez Álvarez. (2019). 1D Convolutional Neural Network for Detecting Ventricular Heartbeats. https://ezproxy.unisimon.edu.co:2131/document/9011541spa
dcterms.referencesÇağla Sarvan & Nalan Özkurt. (2019). ECG Beat Arrhythmia Classification by using 1-D CNN in case of Class Imbalance. https://ezproxy.unisimon.edu.co:2131/document/8895014eng
dcterms.referencesYi Hui, Zhendong Yin, Mingyang Wu, Dasen Li. (2021). Wearable Devices Acquired ECG Signals Detection Method Using 1D Convolutional Neural Network. https://ezproxy.unisimon.edu.co:2131/document/9434935eng
dcterms.referencesOmar Cheikhrouhou, Redowan Mahmud, Ramzi Zouari, Muhammad Ibrahim, Atef Zaguia, Tuan Nguyen Gia. (2021). One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments. https://ezproxy.unisimon.edu.co:2131/document/9489314eng
dcterms.referencesMuhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince, Ozer Can Devecioglu, Muhammad E. H. Chowdhury, Amith Khandakar, Anas Tahir & Moncef Gabbouj. (2021). Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network. https://ezproxy.unisimon.edu.co:2131/document/9451595eng
dcterms.referencesMohammad Mahbubur Rahman Khan Mamun. (2019). Assessment of Potential Primary and Recurrent Ischemic Stroke by Detecting Atrial Fibrillation using 1D-CNN and CHA2DS2-VA Score. https://ezproxy.unisimon.edu.co:2131/document/9669885eng
dcterms.referencesChen, Yuxi Qiao, Huayiting Wen, Alexander. (2021). Binary Classification for Atrial Fibrillation Detection from ECG Based Signals using 1D-Convolutional Neural Network. https://ezproxy.unisimon.edu.co:2091/record/display.uri?eid=2-s2.0-85124698671&origin=resultslist&sort=plf-f&src=s&st1=Detection+of+Atrial+Fibrillation+use+1d&sid=12f3a4816cba82f6fe64ed3ad6f9a6fc&sot=b&sdt=b&sl=54&s=TITLE-ABS-KEY%28Detection+of+Atrial+Fibrillation+use+1d%29&relpos=6&citeCnt=0&searchTerm=eng
dcterms.referencesZhaohan Xiong, Martin K. Stiles, Jichao Zhao. (2018). Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. https://ezproxy.unisimon.edu.co:2131/document/8331487eng
dcterms.referencesMd Awsafur Rahman, Shahed Ahmed, Shaikh Anowarul Fattah. (2022). A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features. https://ezproxy.unisimon.edu.co:2131/document/9870829eng
dcterms.referencesPrabodh Panindre, Vijay Gandhi, Sunil Kumar. (2015). Comparison of Performance of Artificial Intelligence Algorithms for Real-Time Atrial Fibrillation Detection using Instantaneous Heart Rate. https://ezproxy.unisimon.edu.co:2131/document/9322658eng
dcterms.referencesSudarsan Sadasivuni, Rahul Chowdhury, Vinay Elkoori Ghantala Karnam, Imon Banerjee, Arindam Sanyal. (2021). Recurrent Neural Network Circuit for Automated Detection of Atrial Fibrillation from Raw ECG. https://ezproxy.unisimon.edu.co:2131/document/9401666eng
dcterms.referencesBo Fang; Junxin Chen, Yu Liu, Wei Wang; Ke Wang, Amit Kumar Singh, Zhihan Lv. (2019). Dual-channel Neural Network for Atrial Fibrillation Detection from a Single Lead ECG Wave. https://ezproxy.unisimon.edu.co:2131/document/958063eng
dcterms.referencesMixue Deng, Lishen Qiu, Hongqing Wang, Wei Shi, Lirong Wang. (2021). Atrial Fibrillation Classification Using Convolutional Neural Networks and Time Domain Features of ECG Sequence. https://ezproxy.unisimon.edu.co:2131/document/9343104eng
dcterms.referencesChaur-Heh Hsieh, Yan-Shuo Li, Bor-Jiunn Hwang, Ching-Hua Hsiao. (2020). Detection of Atrial Fibrillation Using 1D Convolutional Neural Network. https://www.researchgate.net/publication/340611488_Detection_of_Atrial_Fibrillation_Using_1D_Convolutional_Neural_Networkeng
dcterms.referencesÓscar Jesús Cosido Cobos, Ignacio Diaz Blanco, Ana González Muñiz. (2021). DETECCIÓN, CLASIFICACIÓN E INTERPRETACIÓN DE PATOLOGÍAS DE LA CONDUCCIÓN CARDIACA MEDIANTE EL USO DE TÉCNICAS DE DEEP LEARNING. https://digibuo.uniovi.es/dspace/bitstream/handle/10651/60332/TFM_JorgeBlancoPrieto.pdf?sequence=5&isAllowed=yspa
dcterms.referencesJeyson A. Castillo, Yenny C. Granados, Carlos A. Fajardo. (2020). Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks. https://revistas.unimilitar.edu.co/index.php/rcin/article/view/4156/4080eng
dcterms.referencesY. N. Fu’adah y K. M. Lim. (2022). Classification of Atrial Fibrillation and Congestive Heart Failure Using Convolutional Neural Network with Electrocardiogram. https://www.mdpi.com/2079-9292/11/15/2456/htmeng
dcterms.referencesY. Jin, C. Qin, Y. Huang, W. Zhao y C. Liu. (2020). Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural network. https://ezproxy.unisimon.edu.co:2055/science/article/pii/S0950705119306707?via%3Dihubeng
dcterms.referencesY. -H. Chen, A. H. Twing, D. Badawi, J. Danavi, M. McCauley, A. E. Cetin. (2020). Atrial Fibrillation Risk Prediction from Electrocardiogram and Related Health Data with Deep Neural Network. https://ezproxy.unisimon.edu.co:2131/document/9054403eng
dcterms.referencesX. Chen et al. (2021). Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals. https://ezproxy.unisimon.edu.co:2055/science/article/pii/S0169260721000845?via%3Dihubeng
dcterms.referencesK. R. Madhavi, P. Kora, L. V. Reddy, J. Avanija, K. L. S. Soujanya y P. Telagarapu. (2022). Cardiac arrhythmia detection using dual-tree wavelet transform and convolutional neural network. https://ezproxy.unisimon.edu.co:2287/article/10.1007/s00500-021-06653-weng
dcterms.referencesX. Qingsong, T. Shikui, W. Guoxing, L. Yong y X. Lei. (Fecha N/A). Discrete Biorthogonal Wavelet Transform Based Convolutional Neural Network for Atrial Fibrillation Diagnosis from Electrocardiogram. https://www.ijcai.org/proceedings/2020/607eng
dcterms.referencesT.-S. Heo, C. Kim, J.-D. Kim, C.-Y. Park y Y.-S. Kim. (2021). Prediction of Atrial Fibrillation Cases: Convolutional Neural Networks Using the Output Texts of Electrocardiography. https://sensors.myu-group.co.jp/article.php?ss=3023eng
oaire.versioninfo:eu-repo/semantics/acceptedVersionspa
sb.programaIngeniería de Sistemasspa
sb.sedeSede Barranquillaspa

Archivos

Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
PDF.pdf
Tamaño:
586.28 KB
Formato:
Adobe Portable Document Format
Descargar

Colecciones

Pregrado
Artículos

Universidad Simón Bolívar: Todos los derechos reservados / Sistema de biblioteca