Real-time algorithm for changes detection in depth of anesthesia signals

Detalhes bibliográficos
Autor(a) principal: Sebastião, Raquel
Data de Publicação: 2013
Outros Autores: Silva, Margarida M., Rabiço, Rui, Gama, João, Mendonça, Teresa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/18968
Resumo: This paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A Page-Hinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their "age" so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a two-fold approach. First, the algorithm was run offline in depth of anesthesia (DoA) signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69 % of the changes were classified as true positives, 26 % as false positives, and 5 % as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice. © 2012 Springer-Verlag.
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spelling Real-time algorithm for changes detection in depth of anesthesia signalsAdaptive systemsChange detection algorithmsDynamic behaviorChange detection algorithmsThis paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A Page-Hinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their "age" so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a two-fold approach. First, the algorithm was run offline in depth of anesthesia (DoA) signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69 % of the changes were classified as true positives, 26 % as false positives, and 5 % as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice. © 2012 Springer-Verlag.Springer Verlag2017-11-24T14:19:36Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/18968eng1868-647810.1007/s12530-012-9063-4Sebastião, RaquelSilva, Margarida M.Rabiço, RuiGama, JoãoMendonça, Teresainfo: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:RCAAP2024-02-22T11:35:05Zoai:ria.ua.pt:10773/18968Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:53:13.471976Repositório 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 Real-time algorithm for changes detection in depth of anesthesia signals
title Real-time algorithm for changes detection in depth of anesthesia signals
spellingShingle Real-time algorithm for changes detection in depth of anesthesia signals
Sebastião, Raquel
Adaptive systems
Change detection algorithms
Dynamic behavior
Change detection algorithms
title_short Real-time algorithm for changes detection in depth of anesthesia signals
title_full Real-time algorithm for changes detection in depth of anesthesia signals
title_fullStr Real-time algorithm for changes detection in depth of anesthesia signals
title_full_unstemmed Real-time algorithm for changes detection in depth of anesthesia signals
title_sort Real-time algorithm for changes detection in depth of anesthesia signals
author Sebastião, Raquel
author_facet Sebastião, Raquel
Silva, Margarida M.
Rabiço, Rui
Gama, João
Mendonça, Teresa
author_role author
author2 Silva, Margarida M.
Rabiço, Rui
Gama, João
Mendonça, Teresa
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Sebastião, Raquel
Silva, Margarida M.
Rabiço, Rui
Gama, João
Mendonça, Teresa
dc.subject.por.fl_str_mv Adaptive systems
Change detection algorithms
Dynamic behavior
Change detection algorithms
topic Adaptive systems
Change detection algorithms
Dynamic behavior
Change detection algorithms
description This paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A Page-Hinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their "age" so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a two-fold approach. First, the algorithm was run offline in depth of anesthesia (DoA) signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69 % of the changes were classified as true positives, 26 % as false positives, and 5 % as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice. © 2012 Springer-Verlag.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2017-11-24T14:19:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/18968
url http://hdl.handle.net/10773/18968
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1868-6478
10.1007/s12530-012-9063-4
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dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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