Algorithms for time series clustering applied to biomedical signals

Detalhes bibliográficos
Autor(a) principal: Nunes, Neuza Filipa Martins
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/5666
Resumo: Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering
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spelling Algorithms for time series clustering applied to biomedical signalsBiosignalsAlgorithmsSignal-ProcessingAlignment techniquesClusteringThesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical EngineeringThe increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification.Faculdade de Ciências e TecnologiaGamboa, HugoRUNNunes, Neuza Filipa Martins2011-05-25T15:59:52Z20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/5666enginfo: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:RCAAP2024-03-11T03:36:24Zoai:run.unl.pt:10362/5666Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:16:25.928932Repositório 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 time series clustering applied to biomedical signals
title Algorithms for time series clustering applied to biomedical signals
spellingShingle Algorithms for time series clustering applied to biomedical signals
Nunes, Neuza Filipa Martins
Biosignals
Algorithms
Signal-Processing
Alignment techniques
Clustering
title_short Algorithms for time series clustering applied to biomedical signals
title_full Algorithms for time series clustering applied to biomedical signals
title_fullStr Algorithms for time series clustering applied to biomedical signals
title_full_unstemmed Algorithms for time series clustering applied to biomedical signals
title_sort Algorithms for time series clustering applied to biomedical signals
author Nunes, Neuza Filipa Martins
author_facet Nunes, Neuza Filipa Martins
author_role author
dc.contributor.none.fl_str_mv Gamboa, Hugo
RUN
dc.contributor.author.fl_str_mv Nunes, Neuza Filipa Martins
dc.subject.por.fl_str_mv Biosignals
Algorithms
Signal-Processing
Alignment techniques
Clustering
topic Biosignals
Algorithms
Signal-Processing
Alignment techniques
Clustering
description Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering
publishDate 2011
dc.date.none.fl_str_mv 2011-05-25T15:59:52Z
2011
2011-01-01T00:00:00Z
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/10362/5666
url http://hdl.handle.net/10362/5666
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
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