Comparison of machine learning methods for the arterial hypertension diagnostics

Detalhes bibliográficos
Autor(a) principal: Kublanov, Vladimir S.
Data de Publicação: 2017
Outros Autores: Dolganov, Anton Yu, Belo, David, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1155/2017/5985479
Resumo: Act 211 Government of the Russian Federation (02.A03.21.0006) FCT (AHA CMUP-ERI/HCI/0046/2013)
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spelling Comparison of machine learning methods for the arterial hypertension diagnosticsEMPIRICAL MODE DECOMPOSITIONOBSTRUCTIVE SLEEP-APNEAHEART-RATE-VARIABILITYECGDYNAMICSSYSTEMHEALTHBiotechnologyMedicine (miscellaneous)BioengineeringBiomedical EngineeringAct 211 Government of the Russian Federation (02.A03.21.0006) FCT (AHA CMUP-ERI/HCI/0046/2013)The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.DF – Departamento de FísicaCeFITec – Centro de Física e Investigação TecnológicaRUNKublanov, Vladimir S.Dolganov, Anton YuBelo, DavidGamboa, Hugo2018-11-30T23:25:27Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.1155/2017/5985479eng1176-2322PURE: 3794256http://www.scopus.com/inward/record.url?scp=85031328575&partnerID=8YFLogxKhttps://doi.org/10.1155/2017/5985479info: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:RCAAP2024-03-11T04:26:23Zoai:run.unl.pt:10362/53325Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:32:38.794560Repositório 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 Comparison of machine learning methods for the arterial hypertension diagnostics
title Comparison of machine learning methods for the arterial hypertension diagnostics
spellingShingle Comparison of machine learning methods for the arterial hypertension diagnostics
Kublanov, Vladimir S.
EMPIRICAL MODE DECOMPOSITION
OBSTRUCTIVE SLEEP-APNEA
HEART-RATE-VARIABILITY
ECG
DYNAMICS
SYSTEM
HEALTH
Biotechnology
Medicine (miscellaneous)
Bioengineering
Biomedical Engineering
title_short Comparison of machine learning methods for the arterial hypertension diagnostics
title_full Comparison of machine learning methods for the arterial hypertension diagnostics
title_fullStr Comparison of machine learning methods for the arterial hypertension diagnostics
title_full_unstemmed Comparison of machine learning methods for the arterial hypertension diagnostics
title_sort Comparison of machine learning methods for the arterial hypertension diagnostics
author Kublanov, Vladimir S.
author_facet Kublanov, Vladimir S.
Dolganov, Anton Yu
Belo, David
Gamboa, Hugo
author_role author
author2 Dolganov, Anton Yu
Belo, David
Gamboa, Hugo
author2_role author
author
author
dc.contributor.none.fl_str_mv DF – Departamento de Física
CeFITec – Centro de Física e Investigação Tecnológica
RUN
dc.contributor.author.fl_str_mv Kublanov, Vladimir S.
Dolganov, Anton Yu
Belo, David
Gamboa, Hugo
dc.subject.por.fl_str_mv EMPIRICAL MODE DECOMPOSITION
OBSTRUCTIVE SLEEP-APNEA
HEART-RATE-VARIABILITY
ECG
DYNAMICS
SYSTEM
HEALTH
Biotechnology
Medicine (miscellaneous)
Bioengineering
Biomedical Engineering
topic EMPIRICAL MODE DECOMPOSITION
OBSTRUCTIVE SLEEP-APNEA
HEART-RATE-VARIABILITY
ECG
DYNAMICS
SYSTEM
HEALTH
Biotechnology
Medicine (miscellaneous)
Bioengineering
Biomedical Engineering
description Act 211 Government of the Russian Federation (02.A03.21.0006) FCT (AHA CMUP-ERI/HCI/0046/2013)
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2018-11-30T23:25:27Z
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 https://doi.org/10.1155/2017/5985479
url https://doi.org/10.1155/2017/5985479
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1176-2322
PURE: 3794256
http://www.scopus.com/inward/record.url?scp=85031328575&partnerID=8YFLogxK
https://doi.org/10.1155/2017/5985479
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection 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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