Selective MMIE training of hidden Markov models for cardiac arrhythmia classification

Detalhes bibliográficos
Autor(a) principal: Lima, C. S.
Data de Publicação: 2007
Outros Autores: Cardoso, Manuel J.
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/17511
Resumo: Centre Algoritmi
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spelling Selective MMIE training of hidden Markov models for cardiac arrhythmia classificationHidden Markov modelsMMI trainingCardiac arrhythmiaPattern recognitionCentre AlgoritmiThis paper is concerned to the cardiac arrhythmia classification by using Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, and two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF) and atrial flutter (AFL). The approach followed in this paper is based on the supposition that atrial fibrillation, atrial flutter and normal beats are morphologically similar except that the former does not exhibit the P wave, while the later exhibits several P waves following the QRS. Regarding to the HMM modelling this can mean that these three classes can be modelled by HMM’s of similar topology and sharing some similar parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMI (Maximum Mutual Information) training, and saving computational resources at run-time decoding. This paper also shows that the similarities among normal, atrial fibrillation and atrial flutter beats, which main difference is the lack or repetitions of the P wave, can be taken into consideration to improve the classifier performance by using MMI training, in a single model/triple class framework, which is similar of having three different models sharing several parameters. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where one different HMM models each class when using MLE (Maximum Likelihood Estimation) training alone.Centre AlgoritmiWorld Association for Chinese Biomedical Engineers (WACBE)Universidade do MinhoLima, C. S.Cardoso, Manuel J.2007-072007-07-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/17511enginfo: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:RCAAP2024-05-11T04:33:45Zoai:repositorium.sdum.uminho.pt:1822/17511Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T04:33:45Repositório 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 Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
title Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
spellingShingle Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
Lima, C. S.
Hidden Markov models
MMI training
Cardiac arrhythmia
Pattern recognition
title_short Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
title_full Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
title_fullStr Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
title_full_unstemmed Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
title_sort Selective MMIE training of hidden Markov models for cardiac arrhythmia classification
author Lima, C. S.
author_facet Lima, C. S.
Cardoso, Manuel J.
author_role author
author2 Cardoso, Manuel J.
author2_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Lima, C. S.
Cardoso, Manuel J.
dc.subject.por.fl_str_mv Hidden Markov models
MMI training
Cardiac arrhythmia
Pattern recognition
topic Hidden Markov models
MMI training
Cardiac arrhythmia
Pattern recognition
description Centre Algoritmi
publishDate 2007
dc.date.none.fl_str_mv 2007-07
2007-07-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/17511
url http://hdl.handle.net/1822/17511
dc.language.iso.fl_str_mv eng
language eng
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.publisher.none.fl_str_mv World Association for Chinese Biomedical Engineers (WACBE)
publisher.none.fl_str_mv World Association for Chinese Biomedical Engineers (WACBE)
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 mluisa.alvim@gmail.com
_version_ 1817544349893787648

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