Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | http://tede.unioeste.br/handle/tede/5114 |
Resumo: | The aging process makes changes in the cardiovascular structure, increasing the risk of cardiovascular diseases and making people more dependent and vulnerable. The National Institute of Social Security points out that every year around 250 thousand new cases of stroke occur in Brazil, with approximately 40 % of retirement requests because of strokes and heart attacks. Heart disease is one of the most prominent causes of death in the world. Most cases of sudden death occur without previous symptoms, while some non-lethal arrhythmias such as ventricular extrasystoles precede others directly related to sudden death. In this sense, it is advisable to monitor individuals at high risk on a daily basis who are not hospitalized. In addition, considering the aging of the population and the increase in the number of people living alone, it is important that remote monitoring systems for various types of biomedical signals. For this reason a Monitoring System of the Human Body, to work in real time, is being developed at UNIOESTE to helps the monitoring of elderly patients or with cardiac risk. This work is part of the Monitoring System of the Human Body and The main objective is to implement a method for classifying QRS complexes and send alarms to a hospital, doctor and / or guardian. The classifier was chosen comparing 3 methods using the same database. To choose the classifier, we used several approaches, including entropy, fractal dimension and statistical measurements using the same database comparing the performance obtained on each approach. The algorithms tested were: J48, Multilayer Perceptron and Random Forests. |
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Kauati, Adriana Tokuhashihttp://lattes.cnpq.br/4808167232091229Campos, Marcello Luiz Rodrigues dehttp://lattes.cnpq.br/2402401592333107Machado, Renato Bobsinhttp://lattes.cnpq.br/8407723021436270http://lattes.cnpq.br/3165569301590377Hübner, Lucas Guilherme2020-11-19T16:29:14Z2020-02-10HÜBNER, Lucas Guilherme. Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas. 2020. 172 f. Dissertação (Mestrado em Engenharia Elétrica e Computação) - Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, 2020.http://tede.unioeste.br/handle/tede/5114The aging process makes changes in the cardiovascular structure, increasing the risk of cardiovascular diseases and making people more dependent and vulnerable. The National Institute of Social Security points out that every year around 250 thousand new cases of stroke occur in Brazil, with approximately 40 % of retirement requests because of strokes and heart attacks. Heart disease is one of the most prominent causes of death in the world. Most cases of sudden death occur without previous symptoms, while some non-lethal arrhythmias such as ventricular extrasystoles precede others directly related to sudden death. In this sense, it is advisable to monitor individuals at high risk on a daily basis who are not hospitalized. In addition, considering the aging of the population and the increase in the number of people living alone, it is important that remote monitoring systems for various types of biomedical signals. For this reason a Monitoring System of the Human Body, to work in real time, is being developed at UNIOESTE to helps the monitoring of elderly patients or with cardiac risk. This work is part of the Monitoring System of the Human Body and The main objective is to implement a method for classifying QRS complexes and send alarms to a hospital, doctor and / or guardian. The classifier was chosen comparing 3 methods using the same database. To choose the classifier, we used several approaches, including entropy, fractal dimension and statistical measurements using the same database comparing the performance obtained on each approach. The algorithms tested were: J48, Multilayer Perceptron and Random Forests.O processo de envelhecimento faz mudanças na estrutura cardiovascular, aumentando assim o risco de doenças cardiovasculares e tornando as pessoas cada vez menos independentes e mais vulneráveis. O Instituto Nacional do Seguro Social aponta que todo ano surgem cerca de 250 mil novos casos de Acidente Vascular Cerebral no Brasil, sendo que aproximadamente 40% dos pedidos de aposentadoria decorrem de derrames e infartos. Doenças cardíacas são uma das causas mais proeminentes de morte no mundo inteiro e a maioria dos casos de morte súbita ocorre sem sintomas prévios, já algumas arritmias não letais como extrassístoles ventriculares, precedem outras diretamente relacionadas à morte súbita. Neste sentido, convém monitorar no dia a dia os indivíduos com alto risco, não hospitalizados. Além disso, considerando o envelhecimento da população e aumento de pessoas que moram sozinhas, é importante que sistemas de monitoramento remoto de vários tipos de sinais biomédicos. Por este motivo está sendo desenvolvido na, UNIOESTE, pesquisas visando um Sistema de Monitoramento do Corpo Humano em tempo real, que auxilie no monitoramento de pacientes idosos ou com risco cardíaco. Assim, este trabalho se insere no Sistema de Monitoramento do Corpo Humano e o principal objetivo é a implementação de um método de classificação de arritmias cardíacas para soar alarmes a serem enviados a um hospital, médico e/ou responsável. Para a escolha do classificador foram comparados o desempenho de diversas abordagens, como calculo de entropia, dimensão fractal e medidas estatísticas utilizando o banco de dados MIT-BIH Arrhythmia Database. Os algoritmos testados foram: Random Forests, J48 e Multilayer Perceptron, tendo o Random Forests obtido o melhor desempenho.Submitted by Wagner Junior (wagner.junior@unioeste.br) on 2020-11-19T16:29:14Z No. of bitstreams: 2 Lucas_Guilherme_Hubner_2020.pdf: 4746760 bytes, checksum: c5c06b8ad8c8bc56c9baa47d4f4740a7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2020-11-19T16:29:14Z (GMT). No. of bitstreams: 2 Lucas_Guilherme_Hubner_2020.pdf: 4746760 bytes, checksum: c5c06b8ad8c8bc56c9baa47d4f4740a7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2020-02-10application/pdfpor8774263440366006536500Universidade Estadual do Oeste do ParanáFoz do IguaçuPrograma de Pós-Graduação em Engenharia Elétrica e ComputaçãoUNIOESTEBrasilCentro de Engenharias e Ciências Exatashttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessEletrocardiogramaProcessamento de sinaisArritmiasMonitoraçãoElectrocardiogramSignal processingArrhythmiaMonitoringBIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOSClassificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadasReal time heart rythm classification applying embedded technologiesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis-1040084669565072649600600600-7734402124082146922-1017255114638026215reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALLucas_Guilherme_Hubner_2020.pdfLucas_Guilherme_Hubner_2020.pdfapplication/pdf4746760http://tede.unioeste.br:8080/tede/bitstream/tede/5114/5/Lucas_Guilherme_Hubner_2020.pdfc5c06b8ad8c8bc56c9baa47d4f4740a7MD55CC-LICENSElicense_urllicense_urltext/plain; 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dc.title.por.fl_str_mv |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
dc.title.alternative.eng.fl_str_mv |
Real time heart rythm classification applying embedded technologies |
title |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
spellingShingle |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas Hübner, Lucas Guilherme Eletrocardiograma Processamento de sinais Arritmias Monitoração Electrocardiogram Signal processing Arrhythmia Monitoring BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS |
title_short |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
title_full |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
title_fullStr |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
title_full_unstemmed |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
title_sort |
Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas |
author |
Hübner, Lucas Guilherme |
author_facet |
Hübner, Lucas Guilherme |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Kauati, Adriana Tokuhashi |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4808167232091229 |
dc.contributor.referee1.fl_str_mv |
Campos, Marcello Luiz Rodrigues de |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/2402401592333107 |
dc.contributor.referee2.fl_str_mv |
Machado, Renato Bobsin |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/8407723021436270 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3165569301590377 |
dc.contributor.author.fl_str_mv |
Hübner, Lucas Guilherme |
contributor_str_mv |
Kauati, Adriana Tokuhashi Campos, Marcello Luiz Rodrigues de Machado, Renato Bobsin |
dc.subject.por.fl_str_mv |
Eletrocardiograma Processamento de sinais Arritmias Monitoração |
topic |
Eletrocardiograma Processamento de sinais Arritmias Monitoração Electrocardiogram Signal processing Arrhythmia Monitoring BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS |
dc.subject.eng.fl_str_mv |
Electrocardiogram Signal processing Arrhythmia Monitoring |
dc.subject.cnpq.fl_str_mv |
BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS |
description |
The aging process makes changes in the cardiovascular structure, increasing the risk of cardiovascular diseases and making people more dependent and vulnerable. The National Institute of Social Security points out that every year around 250 thousand new cases of stroke occur in Brazil, with approximately 40 % of retirement requests because of strokes and heart attacks. Heart disease is one of the most prominent causes of death in the world. Most cases of sudden death occur without previous symptoms, while some non-lethal arrhythmias such as ventricular extrasystoles precede others directly related to sudden death. In this sense, it is advisable to monitor individuals at high risk on a daily basis who are not hospitalized. In addition, considering the aging of the population and the increase in the number of people living alone, it is important that remote monitoring systems for various types of biomedical signals. For this reason a Monitoring System of the Human Body, to work in real time, is being developed at UNIOESTE to helps the monitoring of elderly patients or with cardiac risk. This work is part of the Monitoring System of the Human Body and The main objective is to implement a method for classifying QRS complexes and send alarms to a hospital, doctor and / or guardian. The classifier was chosen comparing 3 methods using the same database. To choose the classifier, we used several approaches, including entropy, fractal dimension and statistical measurements using the same database comparing the performance obtained on each approach. The algorithms tested were: J48, Multilayer Perceptron and Random Forests. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-11-19T16:29:14Z |
dc.date.issued.fl_str_mv |
2020-02-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
HÜBNER, Lucas Guilherme. Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas. 2020. 172 f. Dissertação (Mestrado em Engenharia Elétrica e Computação) - Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, 2020. |
dc.identifier.uri.fl_str_mv |
http://tede.unioeste.br/handle/tede/5114 |
identifier_str_mv |
HÜBNER, Lucas Guilherme. Classificação de ritmos cardíacos em tempo real aplicando tecnologias embarcadas. 2020. 172 f. Dissertação (Mestrado em Engenharia Elétrica e Computação) - Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, 2020. |
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http://tede.unioeste.br/handle/tede/5114 |
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por |
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600 600 600 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Programa de Pós-Graduação em Engenharia Elétrica e Computação |
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UNIOESTE |
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Brasil |
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Centro de Engenharias e Ciências Exatas |
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Universidade Estadual do Oeste do Paraná Foz do Iguaçu |
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