Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60386 |
Resumo: | Premature Ventricular Contractions (PVC) arrhythmias can be associated with sudden death and acute myocardial infarction, occurring in 50% of the population for Holter monitoring. PVC patterns are very hard to be recognized since their waveforms can be confused with other heartbeats, such as Right and Left Bundle Branch Blocks. This work proposes a new approach for PVC recognition, based on Gaussian Naive Bayes algorithm and AMUSE (Algorithm for Multiple Unknown Signal Extraction), which is a method for the blind source separation problem. This approach provides a set of attributes that are combined by Linear Discriminant Analysis, allowing the training of an ensemble learning. The Analytic Hierarchy Process weights each learned model according to its importance, obtained from the performance metrics. This approach has some advantages over baseline methods since it does not use a pre-processing stage and employs a simple machine learning model trained using only two parameters for each feature. Using a standard dataset for training and test phases, the proposed approach achieves 98.75% accuracy, 90.65% sensitivity, and 99.46% specificity. The best performance was 99.57% accuracy, 98.64% sensitivity, and 99.65% specificity for other datasets. In general, the proposed approach is better than 66% of the state-of-the-art methods concerning accuracy |
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Acta scientiarum. Technology (Online) |
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Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy processPremature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy processArrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in healthArrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in healthPremature Ventricular Contractions (PVC) arrhythmias can be associated with sudden death and acute myocardial infarction, occurring in 50% of the population for Holter monitoring. PVC patterns are very hard to be recognized since their waveforms can be confused with other heartbeats, such as Right and Left Bundle Branch Blocks. This work proposes a new approach for PVC recognition, based on Gaussian Naive Bayes algorithm and AMUSE (Algorithm for Multiple Unknown Signal Extraction), which is a method for the blind source separation problem. This approach provides a set of attributes that are combined by Linear Discriminant Analysis, allowing the training of an ensemble learning. The Analytic Hierarchy Process weights each learned model according to its importance, obtained from the performance metrics. This approach has some advantages over baseline methods since it does not use a pre-processing stage and employs a simple machine learning model trained using only two parameters for each feature. Using a standard dataset for training and test phases, the proposed approach achieves 98.75% accuracy, 90.65% sensitivity, and 99.46% specificity. The best performance was 99.57% accuracy, 98.64% sensitivity, and 99.65% specificity for other datasets. In general, the proposed approach is better than 66% of the state-of-the-art methods concerning accuracyPremature Ventricular Contractions (PVC) arrhythmias can be associated with sudden death and acute myocardial infarction, occurring in 50% of the population for Holter monitoring. PVC patterns are very hard to be recognized since their waveforms can be confused with other heartbeats, such as Right and Left Bundle Branch Blocks. This work proposes a new approach for PVC recognition, based on Gaussian Naive Bayes algorithm and AMUSE (Algorithm for Multiple Unknown Signal Extraction), which is a method for the blind source separation problem. This approach provides a set of attributes that are combined by Linear Discriminant Analysis, allowing the training of an ensemble learning. The Analytic Hierarchy Process weights each learned model according to its importance, obtained from the performance metrics. This approach has some advantages over baseline methods since it does not use a pre-processing stage and employs a simple machine learning model trained using only two parameters for each feature. Using a standard dataset for training and test phases, the proposed approach achieves 98.75% accuracy, 90.65% sensitivity, and 99.46% specificity. The best performance was 99.57% accuracy, 98.64% sensitivity, and 99.65% specificity for other datasets. In general, the proposed approach is better than 66% of the state-of-the-art methods concerning accuracyUniversidade Estadual De Maringá2022-07-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/6038610.4025/actascitechnol.v44i1.60386Acta Scientiarum. Technology; Vol 44 (2022): Publicação contínua; e60386Acta Scientiarum. Technology; v. 44 (2022): Publicação contínua; e603861806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60386/751375154619Copyright (c) 2022 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessOliveira, Bruno Rodrigues de Duarte, Marco Aparecido Queiroz Vieira Filho, Jozue2022-08-22T17:18:39Zoai:periodicos.uem.br/ojs:article/60386Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2022-08-22T17:18:39Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
title |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
spellingShingle |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process Oliveira, Bruno Rodrigues de Arrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in health Arrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in health |
title_short |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
title_full |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
title_fullStr |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
title_full_unstemmed |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
title_sort |
Premature ventricular contraction recognition using blind source separation and ensemble gaussian naive bayes weighted by analytic hierarchy process |
author |
Oliveira, Bruno Rodrigues de |
author_facet |
Oliveira, Bruno Rodrigues de Duarte, Marco Aparecido Queiroz Vieira Filho, Jozue |
author_role |
author |
author2 |
Duarte, Marco Aparecido Queiroz Vieira Filho, Jozue |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Oliveira, Bruno Rodrigues de Duarte, Marco Aparecido Queiroz Vieira Filho, Jozue |
dc.subject.por.fl_str_mv |
Arrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in health Arrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in health |
topic |
Arrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in health Arrhythmia recognition; ensemble learning; machine learning; heart diseases; decision making in health |
description |
Premature Ventricular Contractions (PVC) arrhythmias can be associated with sudden death and acute myocardial infarction, occurring in 50% of the population for Holter monitoring. PVC patterns are very hard to be recognized since their waveforms can be confused with other heartbeats, such as Right and Left Bundle Branch Blocks. This work proposes a new approach for PVC recognition, based on Gaussian Naive Bayes algorithm and AMUSE (Algorithm for Multiple Unknown Signal Extraction), which is a method for the blind source separation problem. This approach provides a set of attributes that are combined by Linear Discriminant Analysis, allowing the training of an ensemble learning. The Analytic Hierarchy Process weights each learned model according to its importance, obtained from the performance metrics. This approach has some advantages over baseline methods since it does not use a pre-processing stage and employs a simple machine learning model trained using only two parameters for each feature. Using a standard dataset for training and test phases, the proposed approach achieves 98.75% accuracy, 90.65% sensitivity, and 99.46% specificity. The best performance was 99.57% accuracy, 98.64% sensitivity, and 99.65% specificity for other datasets. In general, the proposed approach is better than 66% of the state-of-the-art methods concerning accuracy |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-28 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60386 10.4025/actascitechnol.v44i1.60386 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60386 |
identifier_str_mv |
10.4025/actascitechnol.v44i1.60386 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60386/751375154619 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 44 (2022): Publicação contínua; e60386 Acta Scientiarum. Technology; v. 44 (2022): Publicação contínua; e60386 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315338045685760 |