Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFS |
Texto Completo: | https://ri.ufs.br/jspui/handle/riufs/19469 |
Resumo: | Electrocardiography is a frequently used examination technique for heart disease diagnosis. Represented by the test called electrocardiogram (ECG), electrocardiography is essential in the clinical evaluation of athletes, risk patients who need surgery, and also those who have heart disease. Through electrocardiography, doctors can identify whether the cardiac muscle dysfunctions presented by the patient are of inflammatory or degenerative origin and early diagnose serious diseases that primarily affect the blood vessels and the brain. Thus, the objective of this project is to develop a prototype capable of capturing, analyzing, and classifying a patient’s electrocardiogram signals for the detection and prevention of cardiac arrhythmia in clinical patients. Our ECG signal classification model obtained an accuracy of 98.12% and an F1-score of 99.72% in the classification of ventricular ectopic beats (V). Our ECG acquisition board circuit tested gain output is 28.8V/V and the frequency cut is 40Hz. |
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Souza, Igor LopesDantas, Daniel Oliveira2024-07-05T19:04:17Z2024-07-05T19:04:17Z2023-12-19SOUZA, Igor Lopes. Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks. 2023. 52 f. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Sergipe, São Cristóvão, 2023.https://ri.ufs.br/jspui/handle/riufs/19469Electrocardiography is a frequently used examination technique for heart disease diagnosis. Represented by the test called electrocardiogram (ECG), electrocardiography is essential in the clinical evaluation of athletes, risk patients who need surgery, and also those who have heart disease. Through electrocardiography, doctors can identify whether the cardiac muscle dysfunctions presented by the patient are of inflammatory or degenerative origin and early diagnose serious diseases that primarily affect the blood vessels and the brain. Thus, the objective of this project is to develop a prototype capable of capturing, analyzing, and classifying a patient’s electrocardiogram signals for the detection and prevention of cardiac arrhythmia in clinical patients. Our ECG signal classification model obtained an accuracy of 98.12% and an F1-score of 99.72% in the classification of ventricular ectopic beats (V). Our ECG acquisition board circuit tested gain output is 28.8V/V and the frequency cut is 40Hz.Fundação de Apoio a Pesquisa e à Inovação Tecnológica do Estado de Sergipe - FAPITEC/SESão CristóvãoporEletrocardiografia (ECG)Coração (doenças diagnóstico)Redes neuraisElectrocardiographyAcquisitionClassificationCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOAcquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisPós-Graduação em Ciência da ComputaçãoUniversidade Federal de Sergipe (UFS)reponame:Repositório Institucional da UFSinstname:Universidade Federal de Sergipe (UFS)instacron:UFSinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81475https://ri.ufs.br/jspui/bitstream/riufs/19469/1/license.txt098cbbf65c2c15e1fb2e49c5d306a44cMD51ORIGINALIGOR_LOPES_SOUZA.pdfIGOR_LOPES_SOUZA.pdfapplication/pdf1615450https://ri.ufs.br/jspui/bitstream/riufs/19469/2/IGOR_LOPES_SOUZA.pdfc219518ce529bc8363274f5bb8b7e40cMD52riufs/194692024-07-05 16:04:22.697oai:oai:ri.ufs.br:repo_01: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Repositório InstitucionalPUBhttps://ri.ufs.br/oai/requestrepositorio@academico.ufs.bropendoar:2024-07-05T19:04:22Repositório Institucional da UFS - Universidade Federal de Sergipe (UFS)false |
dc.title.pt_BR.fl_str_mv |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
title |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
spellingShingle |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks Souza, Igor Lopes Eletrocardiografia (ECG) Coração (doenças diagnóstico) Redes neurais Electrocardiography Acquisition Classification CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
title_full |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
title_fullStr |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
title_full_unstemmed |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
title_sort |
Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks |
author |
Souza, Igor Lopes |
author_facet |
Souza, Igor Lopes |
author_role |
author |
dc.contributor.author.fl_str_mv |
Souza, Igor Lopes |
dc.contributor.advisor1.fl_str_mv |
Dantas, Daniel Oliveira |
contributor_str_mv |
Dantas, Daniel Oliveira |
dc.subject.por.fl_str_mv |
Eletrocardiografia (ECG) Coração (doenças diagnóstico) Redes neurais |
topic |
Eletrocardiografia (ECG) Coração (doenças diagnóstico) Redes neurais Electrocardiography Acquisition Classification CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Electrocardiography Acquisition Classification |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
Electrocardiography is a frequently used examination technique for heart disease diagnosis. Represented by the test called electrocardiogram (ECG), electrocardiography is essential in the clinical evaluation of athletes, risk patients who need surgery, and also those who have heart disease. Through electrocardiography, doctors can identify whether the cardiac muscle dysfunctions presented by the patient are of inflammatory or degenerative origin and early diagnose serious diseases that primarily affect the blood vessels and the brain. Thus, the objective of this project is to develop a prototype capable of capturing, analyzing, and classifying a patient’s electrocardiogram signals for the detection and prevention of cardiac arrhythmia in clinical patients. Our ECG signal classification model obtained an accuracy of 98.12% and an F1-score of 99.72% in the classification of ventricular ectopic beats (V). Our ECG acquisition board circuit tested gain output is 28.8V/V and the frequency cut is 40Hz. |
publishDate |
2023 |
dc.date.issued.fl_str_mv |
2023-12-19 |
dc.date.accessioned.fl_str_mv |
2024-07-05T19:04:17Z |
dc.date.available.fl_str_mv |
2024-07-05T19:04:17Z |
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 |
SOUZA, Igor Lopes. Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks. 2023. 52 f. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Sergipe, São Cristóvão, 2023. |
dc.identifier.uri.fl_str_mv |
https://ri.ufs.br/jspui/handle/riufs/19469 |
identifier_str_mv |
SOUZA, Igor Lopes. Acquisition of electrocardiogram signals and cardiac arrhythmia detection using neural networks. 2023. 52 f. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Sergipe, São Cristóvão, 2023. |
url |
https://ri.ufs.br/jspui/handle/riufs/19469 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.program.fl_str_mv |
Pós-Graduação em Ciência da Computação |
dc.publisher.initials.fl_str_mv |
Universidade Federal de Sergipe (UFS) |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFS instname:Universidade Federal de Sergipe (UFS) instacron:UFS |
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Universidade Federal de Sergipe (UFS) |
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UFS |
institution |
UFS |
reponame_str |
Repositório Institucional da UFS |
collection |
Repositório Institucional da UFS |
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https://ri.ufs.br/jspui/bitstream/riufs/19469/1/license.txt https://ri.ufs.br/jspui/bitstream/riufs/19469/2/IGOR_LOPES_SOUZA.pdf |
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Repositório Institucional da UFS - Universidade Federal de Sergipe (UFS) |
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repositorio@academico.ufs.br |
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