Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/43669 |
Resumo: | Background: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. Methods: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. Results: the discrimination results ranged between 73% and 100%, the between 68% and 100%, and the between 0.42 and 1. Conclusions: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT. |
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Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysisECG signalsCardiovascular diseasesMachine learning modelsDiscrete wavelet transformNon-linear analysisDiscriminationBackground: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. Methods: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. Results: the discrimination results ranged between 73% and 100%, the between 68% and 100%, and the between 0.42 and 1. Conclusions: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.Veritati - Repositório Institucional da Universidade Católica PortuguesaRibeiro, PedroSá, JoanaPaiva, DanielaRodrigues, Pedro Miguel2024-01-17T17:01:37Z2024-01-072024-01-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/43669eng2306-535410.3390/bioengineering110100588518316712138247935001148836000001info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-20T01:32:33Zoai:repositorio.ucp.pt:10400.14/43669Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:56:31.144420Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
title |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
spellingShingle |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis Ribeiro, Pedro ECG signals Cardiovascular diseases Machine learning models Discrete wavelet transform Non-linear analysis Discrimination |
title_short |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
title_full |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
title_fullStr |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
title_full_unstemmed |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
title_sort |
Cardiovascular diseases diagnosis using an ECG multi-band non-linear machine learning framework analysis |
author |
Ribeiro, Pedro |
author_facet |
Ribeiro, Pedro Sá, Joana Paiva, Daniela Rodrigues, Pedro Miguel |
author_role |
author |
author2 |
Sá, Joana Paiva, Daniela Rodrigues, Pedro Miguel |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Ribeiro, Pedro Sá, Joana Paiva, Daniela Rodrigues, Pedro Miguel |
dc.subject.por.fl_str_mv |
ECG signals Cardiovascular diseases Machine learning models Discrete wavelet transform Non-linear analysis Discrimination |
topic |
ECG signals Cardiovascular diseases Machine learning models Discrete wavelet transform Non-linear analysis Discrimination |
description |
Background: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. Methods: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. Results: the discrimination results ranged between 73% and 100%, the between 68% and 100%, and the between 0.42 and 1. Conclusions: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-17T17:01:37Z 2024-01-07 2024-01-07T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/43669 |
url |
http://hdl.handle.net/10400.14/43669 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2306-5354 10.3390/bioengineering11010058 85183167121 38247935 001148836000001 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137053502341120 |