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Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography

Bibliographic Details
Main Author: Almeida, Martim Manuel Oliveira e Silva Ferreira de
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10451/53853
Summary: Tese de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2022
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spelling Uterine Contractions Features: Evolution Throughout Pregnancy Using ElectrohysterographyEHGExtração de Características de SinalModelos Lineares MistosAnálise de SensibilidadeImputação MúltiplaTeses de mestrado - 2022Departamento de Estatística e Investigação OperacionalTese de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2022Uterine Electrohysterography (EHG) is a method used to detect uterine electrical activity which pro vokes uterine contractions. This technique has been studied to detect uterine contractions, and it has been reported as an overall non-invasive better alternative compared with the gold standard IUPC (In trauterine Pressure Catheter) and the TOCOgram. The study of EHG, specifically EHG signal features, has gained more interest in preterm prediction and uterine contraction detection. Some studies also com pare features such as duration, entropies, or frequency features in different pregnancy scenarios. With the use of the longitudinal 16 electrode Iceland Database, which comprises EHG recordings from 45 women, this work aims to analyze a set of 15 of the most common EHG extracted features over the gestational period and including the effect of pregnancy and woman-specific factors, such as Age, BMI or Placental Position. Linear Mixed Models with random intercept are used to analyze this data, and given the high degree of missing values, a sensitivity analysis using multiple imputation is performed to assess the robustness of the models in order to validate the results. It was observed that some features, such as Log Energy, Log Shannon Entropy, or Log Peak Amplitude, increase towards labor. BMI ap pears to exert a low-pass filter on some features such as Log Peak Amplitude, Log Mean, and Median Frequency. Age of the woman also showed to have some effect on the features Log Sample Entropy and Crest factor. More interestingly, Placental Position was observed to significantly influence all entropy features, except for Log Shannon Entropy and Lyapunov Exponent. Some of the obtained results are consistent with the knowledge of the respective EHG features in the literature. Additionally, the sensi tivity analysis showed robust results for most of the models, confirming that Linear Mixed Models can be considered a robust technique to model correlated data, even with high levels of missingness.Nunes, Maria Helena Mouriño Silva, 1969-Batista, ArnaldoRepositório da Universidade de LisboaAlmeida, Martim Manuel Oliveira e Silva Ferreira de2024-12-30T01:30:34Z202220212022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/53853TID:203200594enginfo: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:RCAAP2025-03-17T14:48:11Zoai:repositorio.ulisboa.pt:10451/53853Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T03:25:11.557989Repositórios 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 Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
title Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
spellingShingle Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
Almeida, Martim Manuel Oliveira e Silva Ferreira de
EHG
Extração de Características de Sinal
Modelos Lineares Mistos
Análise de Sensibilidade
Imputação Múltipla
Teses de mestrado - 2022
Departamento de Estatística e Investigação Operacional
title_short Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
title_full Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
title_fullStr Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
title_full_unstemmed Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
title_sort Uterine Contractions Features: Evolution Throughout Pregnancy Using Electrohysterography
author Almeida, Martim Manuel Oliveira e Silva Ferreira de
author_facet Almeida, Martim Manuel Oliveira e Silva Ferreira de
author_role author
dc.contributor.none.fl_str_mv Nunes, Maria Helena Mouriño Silva, 1969-
Batista, Arnaldo
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Almeida, Martim Manuel Oliveira e Silva Ferreira de
dc.subject.por.fl_str_mv EHG
Extração de Características de Sinal
Modelos Lineares Mistos
Análise de Sensibilidade
Imputação Múltipla
Teses de mestrado - 2022
Departamento de Estatística e Investigação Operacional
topic EHG
Extração de Características de Sinal
Modelos Lineares Mistos
Análise de Sensibilidade
Imputação Múltipla
Teses de mestrado - 2022
Departamento de Estatística e Investigação Operacional
description Tese de mestrado, Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2022
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022-01-01T00:00:00Z
2024-12-30T01:30:34Z
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/10451/53853
TID:203200594
url http://hdl.handle.net/10451/53853
identifier_str_mv TID:203200594
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.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
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