Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
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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oai:run.unl.pt:10362/150491 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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:RCAAP2024-03-11T05:32:28Zoai:run.unl.pt:10362/150491Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:08.932839Repositório 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 |
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) |
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 |
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1799138131148013568 |