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

Bibliographic Details
Main Author: Abreu, Rodolfo Telo Martins de
Publication Date: 2012
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/8250
Summary: Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:11:52Zoai:run.unl.pt:10362/8250Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:42:56.544878Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596135935049728

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