Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Detalhes bibliográficos
Autor(a) principal: Abreu, Rodolfo Telo Martins de
Data de Publicação: 2012
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/8250
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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spelling Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniquesBiosignalsWavesEvents detectionFeatures extractionPattern recognitionk-meansDissertação para obtenção do Grau de Mestre em Engenharia BiomédicaOne of the biggest challenges when analysing data is to extract information from it, especially if we dealing with very large sized data, which brings a new set of barriers to be overcome. The extracted information can be used to aid physicians in their diagnosis since biosignals often carry vital information on the subjects. In this research work, we present a signal-independent algorithm with two main goals: perform events detection in biosignals and, with those events, extract information using a set of distance measures which will be used as input to a parallel version of the k-means clustering algorithm. The first goal is achieved by using two different approaches. Events can be found based on peaks detection through an adaptive threshold defined as the signal’s root mean square (RMS) or by morphological analysis through the computation of the signal’s meanwave. The final goal is achieved by dividing the distance measures into n parts and by performing k-means individually. In order to improve speed performance, parallel computing techniques were applied. For this study, a set of different types of signals was acquired and annotated by our algorithm. By visual inspection, the L1 and L2 Minkowski distances returned an output that allowed clustering signals’ cycles with an efficiency of 97:5% and 97:3%, respectively. Using the meanwave distance, our algorithm achieved an accuracy of 97:4%. For the downloaded ECGs from the Physionet databases, the developed algorithm detected 638 out of 644 manually annotated events provided by physicians. The fact that this algorithm can be applied to long-term raw biosignals and without requiring any prior information about them makes it an important contribution in biosignals’ information extraction and annotation.Faculdade de Ciências e TecnologiaGamboa, HugoRUNAbreu, Rodolfo Telo Martins de2012-11-30T14:35:47Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/8250enginfo: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:RCAAP2024-03-11T03:40:47Zoai:run.unl.pt:10362/8250Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:18:05.195200Repositório 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 Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
title Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
spellingShingle Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
Abreu, Rodolfo Telo Martins de
Biosignals
Waves
Events detection
Features extraction
Pattern recognition
k-means
title_short Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
title_full Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
title_fullStr Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
title_full_unstemmed Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
title_sort Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
author Abreu, Rodolfo Telo Martins de
author_facet Abreu, Rodolfo Telo Martins de
author_role author
dc.contributor.none.fl_str_mv Gamboa, Hugo
RUN
dc.contributor.author.fl_str_mv Abreu, Rodolfo Telo Martins de
dc.subject.por.fl_str_mv Biosignals
Waves
Events detection
Features extraction
Pattern recognition
k-means
topic Biosignals
Waves
Events detection
Features extraction
Pattern recognition
k-means
description Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
publishDate 2012
dc.date.none.fl_str_mv 2012-11-30T14:35:47Z
2012
2012-01-01T00:00:00Z
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.uri.fl_str_mv http://hdl.handle.net/10362/8250
url http://hdl.handle.net/10362/8250
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 Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
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
_version_ 1799137827145908224

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