Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix

Detalhes bibliográficos
Autor(a) principal: Rodrigues, João
Data de Publicação: 2022
Outros Autores: Liu, Hui, Folgado, Duarte, Belo, David, Schultz, Tanja, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150491
Resumo: The APC was funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen. Hanse Wissenschaftskolleg - Institute for Advanced Study: BRAIN Program. Publisher Copyright: © 2022 by the authors.
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spelling Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity MatrixFocus on Automatic Segmentationautomatic segmentationbiosignal processingclusteringdata mininghuman activity recognitioninformation retrievalnovelty functionself-similarity matrixunsupervised segmentationAnalytical ChemistryBiotechnologyBiomedical EngineeringInstrumentationEngineering (miscellaneous)Clinical BiochemistryThe APC was funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen. Hanse Wissenschaftskolleg - Institute for Advanced Study: BRAIN Program. Publisher Copyright: © 2022 by the authors.Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.LIBPhys-UNLRUNRodrigues, JoãoLiu, HuiFolgado, DuarteBelo, DavidSchultz, TanjaGamboa, Hugo2023-03-13T22:27:27Z2022-12-192022-12-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article34application/pdfhttp://hdl.handle.net/10362/150491eng2079-6374PURE: 55552568https://doi.org/10.3390/bios12121182info: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:RCAAP2023-07-10T16:12:46ZPortal AgregadorONG
dc.title.none.fl_str_mv Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
Focus on Automatic Segmentation
title Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
spellingShingle Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
Rodrigues, João
automatic segmentation
biosignal processing
clustering
data mining
human activity recognition
information retrieval
novelty function
self-similarity matrix
unsupervised segmentation
Analytical Chemistry
Biotechnology
Biomedical Engineering
Instrumentation
Engineering (miscellaneous)
Clinical Biochemistry
title_short Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
title_full Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
title_fullStr Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
title_full_unstemmed Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
title_sort Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
author Rodrigues, João
author_facet Rodrigues, João
Liu, Hui
Folgado, Duarte
Belo, David
Schultz, Tanja
Gamboa, Hugo
author_role author
author2 Liu, Hui
Folgado, Duarte
Belo, David
Schultz, Tanja
Gamboa, Hugo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Rodrigues, João
Liu, Hui
Folgado, Duarte
Belo, David
Schultz, Tanja
Gamboa, Hugo
dc.subject.por.fl_str_mv automatic segmentation
biosignal processing
clustering
data mining
human activity recognition
information retrieval
novelty function
self-similarity matrix
unsupervised segmentation
Analytical Chemistry
Biotechnology
Biomedical Engineering
Instrumentation
Engineering (miscellaneous)
Clinical Biochemistry
topic automatic segmentation
biosignal processing
clustering
data mining
human activity recognition
information retrieval
novelty function
self-similarity matrix
unsupervised segmentation
Analytical Chemistry
Biotechnology
Biomedical Engineering
Instrumentation
Engineering (miscellaneous)
Clinical Biochemistry
description The APC was funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen. Hanse Wissenschaftskolleg - Institute for Advanced Study: BRAIN Program. Publisher Copyright: © 2022 by the authors.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-19
2022-12-19T00:00:00Z
2023-03-13T22:27:27Z
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 http://hdl.handle.net/10362/150491
url http://hdl.handle.net/10362/150491
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2079-6374
PURE: 55552568
https://doi.org/10.3390/bios12121182
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 34
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)
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repository.mail.fl_str_mv
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