Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales

El electrocardiograma fue reconocido como una herramienta para la deteccion de aritmias. La mas conocida es la fibrilacion auricular, que puede o no representar un problema para la salud. Este articulo tiene como objetivo la implementacion de redes neuronales y dispositivos IOT para la deteccion de...

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Autores:
Altamar Montero, Alfonso
Núñez Vega, Edwin
Rúa Ríos, Luis
Romero Altamar, Gineth
Quintero Altamar, Alexander
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
spa
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/10269
Acceso en línea:
https://hdl.handle.net/20.500.12442/10269
Palabra clave:
Fibrilación auricular
Inteligencia artificial
Redes neuronales
Electrocardiograma (ECG)
Dataset
Rights
restrictedAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.title.spa.fl_str_mv Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
title Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
spellingShingle Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
Fibrilación auricular
Inteligencia artificial
Redes neuronales
Electrocardiograma (ECG)
Dataset
title_short Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
title_full Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
title_fullStr Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
title_full_unstemmed Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
title_sort Implementación de IOT para la detección de la Onda P, utilizando Redes Neuronales
dc.creator.fl_str_mv Altamar Montero, Alfonso
Núñez Vega, Edwin
Rúa Ríos, Luis
Romero Altamar, Gineth
Quintero Altamar, Alexander
dc.contributor.author.none.fl_str_mv Altamar Montero, Alfonso
Núñez Vega, Edwin
Rúa Ríos, Luis
Romero Altamar, Gineth
Quintero Altamar, Alexander
dc.subject.spa.fl_str_mv Fibrilación auricular
Inteligencia artificial
Redes neuronales
Electrocardiograma (ECG)
topic Fibrilación auricular
Inteligencia artificial
Redes neuronales
Electrocardiograma (ECG)
Dataset
dc.subject.eng.fl_str_mv Dataset
description El electrocardiograma fue reconocido como una herramienta para la deteccion de aritmias. La mas conocida es la fibrilacion auricular, que puede o no representar un problema para la salud. Este articulo tiene como objetivo la implementacion de redes neuronales y dispositivos IOT para la deteccion de la fibrilacion auricular. En busqueda de prevenir a quienes paceden de esta arrimita cardiaca. Los resultados encontrados se basaran en diferentes investigaciones estudiadas que han aplicado estos conocimientos. Se concluye una deteccion efectiva de la aritmia con los metodos implementados
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-07-15T19:18:09Z
dc.date.available.none.fl_str_mv 2022-07-15T19:18:09Z
dc.date.issued.none.fl_str_mv 2022
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dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.spa.spa.fl_str_mv Trabajo de grado - pregrado
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/10269
url https://hdl.handle.net/20.500.12442/10269
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rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.publisher.spa.fl_str_mv Ediciones Universidad Simón Bolívar
Facultad de Ingenierías
institution Universidad Simón Bolívar
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spelling Altamar Montero, Alfonsobfacba9d-003a-4d33-8df6-3fdd6e4ea2a1Núñez Vega, Edwin865ea8f4-5d2e-4724-b1b1-e79dae6e108aRúa Ríos, Luis712bf4bc-2033-4038-b4d3-b3b1a8cf9aa2Romero Altamar, Gineth94c769be-a9eb-4e97-b6b3-e354544c478bQuintero Altamar, Alexandere6967dc5-7511-4c03-a01b-3edc2272e5322022-07-15T19:18:09Z2022-07-15T19:18:09Z2022https://hdl.handle.net/20.500.12442/10269El electrocardiograma fue reconocido como una herramienta para la deteccion de aritmias. La mas conocida es la fibrilacion auricular, que puede o no representar un problema para la salud. Este articulo tiene como objetivo la implementacion de redes neuronales y dispositivos IOT para la deteccion de la fibrilacion auricular. En busqueda de prevenir a quienes paceden de esta arrimita cardiaca. Los resultados encontrados se basaran en diferentes investigaciones estudiadas que han aplicado estos conocimientos. Se concluye una deteccion efectiva de la aritmia con los metodos implementadosThe ECG was discovered to be a useful tool for detecting arrhythmias. Atrial fibrillation is the most well-known, and it may or may not be a health issue. The goal of this essay is to use neural networks and IoT devices to identify atrial fibrillation. In order to prevent people who suffer from cardiac arrhythmia. The findings will be based on many research studies that have used this information. The implemented methods result in an effective detection of arrhythmia.pdfspaEdiciones Universidad Simón BolívarFacultad de IngenieríasAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecFibrilación auricularInteligencia artificialRedes neuronalesElectrocardiograma (ECG)DatasetImplementación de IOT para la detección de la Onda P, utilizando Redes Neuronalesinfo:eu-repo/semantics/bachelorThesisTrabajo de grado - pregradohttp://purl.org/coar/resource_type/c_7a1fY. Sattar, «statpearls,» 22 05 2022. [En línea]. Available: Yasar Sattar.gdfgdfgdfg.Babson College, Deloitte Consulting, «National Library of Medicine,» 06 06 2019. [En línea]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/.«NHS,» 17 05 2021. [En línea]. Available: 3. https://www.nhs.uk/conditions/atrial-fibrillation/#:~:text=Atrial%20fibrillation%20is%20a%20heart,in%20your%20wrist%20or%20neck.Mixue Deng; Lishen Qiu; Hongqing Wang; Wei Shi; Lirong Wang. Atrial Fibrillation Classification Using Convolutional Neural Networks and Time Domain Features of ECG Sequence. 2020.Vincenzo Randazzo; Jacopo Ferretti; Eros Pasero. ECG WATCH: a real time wireless wearable ECG. 2019.M. Carrara, L. Carozzi, T.J. Moss, M. de Pasquale, S. Cerutti, M. Ferrario, et al. Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy. 2022.Zhaohan Xionga. Martin K.Stiles. Anne M.Gillisc. Jichao Zhao. Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks. 2018Nurul Huda. Sadia Khan. Ragib Abid. Samiul Based Shuvo. A Low-cost, Low-energy Wearable ECG System with Cloud-Based Arrhythmia Detection. 2020.Vincenzo Randazzo;Jacopo Ferretti;Eros Pasero. ECG WATCH: a real time wireless wearable ECG. 2018.Movva Pavani;K. Kishore Kumar. Development of a Low-Cost ECG Device. 2018Masaya Kisohara;Yuto Masuda;Emi. Atrial Fibrillation Detection Using a Feedforward Neural Network. 2022.Mona Alsaleem;Md Saiful Islam POSTER: Atrial Fibrillation Detection Using a Double-Layer Bi-Directional LSTM Neural. 2019S.M Ahsanuzzaman;Toufiq Ahmed;Md. Atiqur Rahman. Low Cost, Portable ECG Monitoring and Alarming System Based on Deep Learning. 2020.Qingxue Zhang. ECG Based Biometric Human Identification Using Convolutional Neural Network in Smart Health Applications. 2020.Yu-Jin Lin;Chen-Wei Chuang;Chun-Yueh Yen;Sheng-Hsin Huang;Peng-Wei Huang;Ju-Yi Chen;Shuenn-Yuh Lee. Artificial Intelligence of Things Wearable System for Cardiac Disease Detection. 2020.Hao Dang;Muyi Sun;Guanhong Zhang;Xingqun Qi;Xiaoguang Zhou;Qing Chang. A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals. 2019.Masaya Kisohara;Yuto Masuda;Emi Yuda;Junichiro Hayano. A Real-Time IoT Based Arrhythmia Classifier Using Convolutional Neural NetworksNeural Network Detection of Atrial Fibrillation by Lorenz Plot Images of Interbeat Interval Variation. 2018.Zhaohan Xiong;Martin K. Stiles;Jichao Zhao. Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. 2017.Ricardo Salinas-Martínez;Johannes De Bie;Nicoletta Marzocchi;Frida Sandberg. Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network. 2020.S.T. Mathew, J. Patel and S. Joseph, "Atrial fibrillation: mechanistic insights and treatment options", European journal of internal medicine, vol. 20, no. 7, pp. 672-681, 2009.S. Islam, N. Ammour and N. Alajlan, "Atrial fibrillation detection with multiparametric RR interval feature and machine learning technique", 2017 International Conference on Informatics Health & Technology (ICIHT), 2017.J. Farhadi et al., "Classification of atrial fibrillation using stacked auto encoders neural networks", 2018 Computing in Cardiology Conference (CinC), 2018.Y. Xia et al., "Detecting atrial fibrillation by deep convolutional neural networks", Computers in biology and medicine, vol. 93, pp. 84-92, 2018.Erratum to “Acid bake-leach process for the treatment of enargite concentrates”, Hydrometallurgy 119–120 (2012), pp. 30–39V. Maknickas and A. Maknickas, "Atrial fibrillation classification using qrs complex features and lstm", 2017 Computing in Cardiology (CinC), 2017.F. Andreotti et al., "Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG", 2017 Computing in Cardiology (CinC), 2017.Journal 31(2) (2014) 211{215. XIX. TsvetkovV. Ya. Logical analysis and variable scales. Slavic Forum 4(22) (2018) 103{109. XX. Wang S. et al. Transit traffic analysis zone delineating method based on Thiessen polygon. Sustainability 6(4) (2014) 1821{1832. ViewRincón, J.A.; Guerra-Ojeda, S.; Carrascosa, C.; Julian, V. An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks. Sensors 2020, 20, 7353.L. Sornmo, M. Stridth, D. Husser, A. Bollmann, and S.B. Olsson,“Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management,” Phil. Trans. Roy. Soc, vol. A367, pp. 235–253, October 2008.Jonathan Rubin; Saman Parvaneh; Asif Rahman; Bryan Conroy; Saeed Babaeizadeh. Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings. 2017.Isac N Lira;Pedro Marinho R de Oliveira;Walter Freitas;Vicente Zarzoso. Automated Atrial Fibrillation Source Detection Using Shallow Convolutional Neural Networks. 2020 Computing in Cardiology.Trivikrama Bhat;Akanksha;Shrikara;Shreya Bhat;Manoj. A Real-Time IoT Based Arrhythmia Classifier Using Convolutional Neural Networks. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics.K. G. Rani Roopha Devi;R. Mahendra Chozhan;R. Murugesan. Cognitive IoT Integration for Smart Healthcare: Case Study for Heart Disease Detection and Monitoring. 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC).Mohammad Mahmudur Rahman Khan;Md. Abu Bakr Siddique;Shadman Sakib;Anas Aziz;Abyaz Kader Tanzeem;Ziad Hossain. Electrocardiogram Heartbeat Classification Using Convolutional Neural Networks for the Detection of Cardiac Arrhythmia. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).J. McCarthy, «Stanford University,» 24 11 2004. [En línea]. Available: 4. https://borghese.di.unimi.it/Teaching/AdvancedIntelligentSystems/Old/IntelligentSystems_2008_2009/Old/IntelligentSystems_2005_2006/Documents/Symbolic/04_McCarthy_whatisai.pdf.«Aprende Machine Learning,» 29 11 2018. [En línea]. Available: 11. https://www.aprendemachinelearning.com/como-funcionan-las-convolutional-neural-networks-vision-por-ordenador/.R. M. George Moody, «PhysioNet,» 24 02 2005. [En línea]. Available: https://physionet.org/content/mitdb/1.0.0/.I. C. Education, «IBM,» 17 08 2020. [En línea]. 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