Missing data imputation techniques for wireless continuous vital signs monitoring
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10362/158434 |
Resumo: | Publisher Copyright: © 2023, The Author(s). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. |
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7160 |
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Missing data imputation techniques for wireless continuous vital signs monitoringImputationMissing dataPhysiological monitoringTelemonitoringVital signsHealth InformaticsCritical Care and Intensive Care MedicineAnesthesiology and Pain MedicinePublisher Copyright: © 2023, The Author(s). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5–60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window’s slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9–2.6 beats/min, respiratory rate: 0.8–1.8 breaths/min, temperature: 0.04–0.17 °C, oxygen saturation: 0.3–0.7% for 5–60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1–8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted.DF – Departamento de FísicaRUNvan Rossum, Mathilde C.da Silva, Pedro M. AlvesWang, YingKouwenhoven, Ewout A.Hermens, Hermie J.2023-09-28T22:20:41Z2023-102023-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/158434eng1387-1307PURE: 72616959https://doi.org/10.1007/s10877-023-00975-winfo: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:RCAAP2024-03-11T05:40:49Zoai:run.unl.pt:10362/158434Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:08.288624Repositório 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 |
Missing data imputation techniques for wireless continuous vital signs monitoring |
title |
Missing data imputation techniques for wireless continuous vital signs monitoring |
spellingShingle |
Missing data imputation techniques for wireless continuous vital signs monitoring van Rossum, Mathilde C. Imputation Missing data Physiological monitoring Telemonitoring Vital signs Health Informatics Critical Care and Intensive Care Medicine Anesthesiology and Pain Medicine |
title_short |
Missing data imputation techniques for wireless continuous vital signs monitoring |
title_full |
Missing data imputation techniques for wireless continuous vital signs monitoring |
title_fullStr |
Missing data imputation techniques for wireless continuous vital signs monitoring |
title_full_unstemmed |
Missing data imputation techniques for wireless continuous vital signs monitoring |
title_sort |
Missing data imputation techniques for wireless continuous vital signs monitoring |
author |
van Rossum, Mathilde C. |
author_facet |
van Rossum, Mathilde C. da Silva, Pedro M. Alves Wang, Ying Kouwenhoven, Ewout A. Hermens, Hermie J. |
author_role |
author |
author2 |
da Silva, Pedro M. Alves Wang, Ying Kouwenhoven, Ewout A. Hermens, Hermie J. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
DF – Departamento de Física RUN |
dc.contributor.author.fl_str_mv |
van Rossum, Mathilde C. da Silva, Pedro M. Alves Wang, Ying Kouwenhoven, Ewout A. Hermens, Hermie J. |
dc.subject.por.fl_str_mv |
Imputation Missing data Physiological monitoring Telemonitoring Vital signs Health Informatics Critical Care and Intensive Care Medicine Anesthesiology and Pain Medicine |
topic |
Imputation Missing data Physiological monitoring Telemonitoring Vital signs Health Informatics Critical Care and Intensive Care Medicine Anesthesiology and Pain Medicine |
description |
Publisher Copyright: © 2023, The Author(s). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-28T22:20:41Z 2023-10 2023-10-01T00:00:00Z |
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/10362/158434 |
url |
http://hdl.handle.net/10362/158434 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1387-1307 PURE: 72616959 https://doi.org/10.1007/s10877-023-00975-w |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
14 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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|
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1799138154524966912 |