Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram
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/10400.17/4480 |
Resumo: | The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (p-value = 1.31 × 10-12). |
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Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine ElectromyogramAlgorithmsElectromyographyFemaleInfant, NewbornPregnancyPremature Birth*RespirationSignal Processing, Computer-Assisted*Uterus / physiologyMAC OBSThe electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (p-value = 1.31 × 10-12).Multidisciplinary Digital Publishing Institute (MDPI)Repositório do Centro Hospitalar Universitário de Lisboa Central, EPEMartins, DBatista, AMouriño, HRusso, SEsgalhado, Fdos Reis, CSerrano, FOrtigueira, M2023-04-12T14:26:18Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.17/4480engSensors (Basel) . 2022 Oct 9;22(19):763810.3390/s22197638info: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:RCAAP2023-04-16T05:45:32Zoai:repositorio.chlc.min-saude.pt:10400.17/4480Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:49:36.111646Repositório 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 |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
title |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
spellingShingle |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram Martins, D Algorithms Electromyography Female Infant, Newborn Pregnancy Premature Birth* Respiration Signal Processing, Computer-Assisted* Uterus / physiology MAC OBS |
title_short |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
title_full |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
title_fullStr |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
title_full_unstemmed |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
title_sort |
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram |
author |
Martins, D |
author_facet |
Martins, D Batista, A Mouriño, H Russo, S Esgalhado, F dos Reis, C Serrano, F Ortigueira, M |
author_role |
author |
author2 |
Batista, A Mouriño, H Russo, S Esgalhado, F dos Reis, C Serrano, F Ortigueira, M |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório do Centro Hospitalar Universitário de Lisboa Central, EPE |
dc.contributor.author.fl_str_mv |
Martins, D Batista, A Mouriño, H Russo, S Esgalhado, F dos Reis, C Serrano, F Ortigueira, M |
dc.subject.por.fl_str_mv |
Algorithms Electromyography Female Infant, Newborn Pregnancy Premature Birth* Respiration Signal Processing, Computer-Assisted* Uterus / physiology MAC OBS |
topic |
Algorithms Electromyography Female Infant, Newborn Pregnancy Premature Birth* Respiration Signal Processing, Computer-Assisted* Uterus / physiology MAC OBS |
description |
The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (p-value = 1.31 × 10-12). |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-04-12T14:26:18Z |
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/10400.17/4480 |
url |
http://hdl.handle.net/10400.17/4480 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sensors (Basel) . 2022 Oct 9;22(19):7638 10.3390/s22197638 |
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.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
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 |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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