Fetal ECG extraction using independent component analysis by Jade approach

Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. T...

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Autores:
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/8958
Acceso en línea:
https://hdl.handle.net/20.500.12585/8958
Palabra clave:
Bioengineering
Fetal ECG
Independent component analysis
Signal processing
Bioinformatics
Electrocardiography
High pass filters
Independent component analysis
Signal processing
Silicate minerals
Systems science
Abnormal conditions
Computational costs
Fetal ECG
Fetal ecg extractions
Joint approximate diagonalization of eigen-matrices
Low-Frequency Noise
Biomedical signal processing
Rights
restrictedAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/8958
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.none.fl_str_mv Fetal ECG extraction using independent component analysis by Jade approach
title Fetal ECG extraction using independent component analysis by Jade approach
spellingShingle Fetal ECG extraction using independent component analysis by Jade approach
Bioengineering
Fetal ECG
Independent component analysis
Signal processing
Bioinformatics
Electrocardiography
High pass filters
Independent component analysis
Signal processing
Silicate minerals
Systems science
Abnormal conditions
Computational costs
Fetal ECG
Fetal ecg extractions
Joint approximate diagonalization of eigen-matrices
Low-Frequency Noise
Biomedical signal processing
title_short Fetal ECG extraction using independent component analysis by Jade approach
title_full Fetal ECG extraction using independent component analysis by Jade approach
title_fullStr Fetal ECG extraction using independent component analysis by Jade approach
title_full_unstemmed Fetal ECG extraction using independent component analysis by Jade approach
title_sort Fetal ECG extraction using independent component analysis by Jade approach
dc.contributor.editor.none.fl_str_mv Lepore N.
Brieva J.
Garcia J.D.
Romero E.
dc.subject.keywords.none.fl_str_mv Bioengineering
Fetal ECG
Independent component analysis
Signal processing
Bioinformatics
Electrocardiography
High pass filters
Independent component analysis
Signal processing
Silicate minerals
Systems science
Abnormal conditions
Computational costs
Fetal ECG
Fetal ecg extractions
Joint approximate diagonalization of eigen-matrices
Low-Frequency Noise
Biomedical signal processing
topic Bioengineering
Fetal ECG
Independent component analysis
Signal processing
Bioinformatics
Electrocardiography
High pass filters
Independent component analysis
Signal processing
Silicate minerals
Systems science
Abnormal conditions
Computational costs
Fetal ECG
Fetal ecg extractions
Joint approximate diagonalization of eigen-matrices
Low-Frequency Noise
Biomedical signal processing
description Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance. © 2017 SPIE.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-03-26T16:32:39Z
dc.date.available.none.fl_str_mv 2020-03-26T16:32:39Z
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.spa.none.fl_str_mv Conferencia
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10572
dc.identifier.isbn.none.fl_str_mv 9781510616332
dc.identifier.issn.none.fl_str_mv 0277786X
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/8958
dc.identifier.doi.none.fl_str_mv 10.1117/12.2285962
dc.identifier.instname.none.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.none.fl_str_mv Repositorio UTB
dc.identifier.orcid.none.fl_str_mv 56520286300
57210822856
57199861860
55985160800
identifier_str_mv Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10572
9781510616332
0277786X
10.1117/12.2285962
Universidad Tecnológica de Bolívar
Repositorio UTB
56520286300
57210822856
57199861860
55985160800
url https://hdl.handle.net/20.500.12585/8958
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.conferencedate.none.fl_str_mv 5 October 2017 through 7 October 2017
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
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dc.rights.cc.none.fl_str_mv Atribución-NoComercial 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución-NoComercial 4.0 Internacional
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dc.format.medium.none.fl_str_mv Recurso electrónico
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dc.publisher.none.fl_str_mv SPIE
publisher.none.fl_str_mv SPIE
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dc.source.event.none.fl_str_mv 13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017
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spelling Lepore N.Brieva J.Garcia J.D.Romero E.Giraldo-Guzmán J.Contreras Ortiz, Sonia HelenaLasprilla G.I.B.Kotas, Marian2020-03-26T16:32:39Z2020-03-26T16:32:39Z2017Proceedings of SPIE - The International Society for Optical Engineering; Vol. 1057297815106163320277786Xhttps://hdl.handle.net/20.500.12585/895810.1117/12.2285962Universidad Tecnológica de BolívarRepositorio UTB56520286300572108228565719986186055985160800Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance. © 2017 SPIE.Medical Image Computing and Computer Assisted Intervention (MICCAI);SIPAIM Foundation;Universidad Nacional de Colombia;Universidad Nacional de Colombia, Direccion de Relaciones ExterioresRecurso electrónicoapplication/pdfengSPIEhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85038431554&doi=10.1117%2f12.2285962&partnerID=40&md5=135787b2482c707280503ebee7013e49Scopus2-s2.0-8503843155413th International Conference on Medical Information Processing and Analysis, SIPAIM 2017Fetal ECG extraction using independent component analysis by Jade approachinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fBioengineeringFetal ECGIndependent component analysisSignal processingBioinformaticsElectrocardiographyHigh pass filtersIndependent component analysisSignal processingSilicate mineralsSystems scienceAbnormal conditionsComputational costsFetal ECGFetal ecg extractionsJoint approximate diagonalization of eigen-matricesLow-Frequency NoiseBiomedical signal processing5 October 2017 through 7 October 2017Fruhman, G., Gavard, J.A., McCormick, K., Wilson-Grin, J., Amon, E., Gross, G.A., Standard external doppler fetal heart tracings versus external fetal electrocardiogram in very preterm gestation: A pilot study (2016) American Journal of Perinatology Reports, 6 (4), pp. e378-e383Kanjilal, P.P., Palit, S., Saha, G., Fetal ecg extraction from single-channel maternal ecg using singular value decomposition (1997) IEEE Transactions on Biomedical Engineering, 44 (1), pp. 51-59Khamene, A., Negahdaripour, S., A new method for the extraction of fetal ecg from the composite abdominal signal (2000) IEEE Transactions on Biomedical Engineering, 47 (4), pp. 507-516Martens, S.M., Rabotti, C., Mischi, M., Sluijter, R.J., A robust fetal ecg detection method for abdominal recordings (2007) Physiological Measurement, 28 (4), p. 373Cliord, G., Sameni, R., Ward, J., Robinson, J., Wolfberg, A.J., Clinically accurate fetal ecg param-eters acquired from maternal abdominal sensors (2011) American Journal of Obstetrics and Gynecology, 205 (1), p. 47e1Kotas, M., Combined application of independent component analysis and projective filtering to fetal ecg extraction (2008) Biocybernetics and Biomedical Engineering, 28 (1), p. 75Kotas, M., Jezewski, J., Matonia, A., Kupka, T., Towards noise immune detection of fetal qrs com-plexes (2010) Computer Methods and Programs in Biomedicine, 97 (3), pp. 241-256Kotas, M., Jezewski, J., Horoba, K., Matonia, A., Application of spatio-temporal filtering to fetal electrocardiogram enhancement (2011) Computer Methods and Programs in Biomedicine, 104 (1), pp. 1-9Ahmadieh, H., Asl, B.M., Fetal ecg extraction via type-2 adaptive neuro-fuzzy inference systems (2017) Computer Methods and Programs in Biomedicine, 142, pp. 101-108Da Poian, G., Bernardini, R., Rinaldo, R., Separation and analysis of fetal-ecg signals from compressed sensed abdominal ecg recordings (2016) IEEE Transactions on Biomedical Engineering, 63 (6), pp. 1269-1279De Moor, B., De Gersem, P., De Schutter, B., Favoreel, W., Daisy: A database for identification of systems (1997) JOURNAL A, 38, pp. 4-5Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E., The great time series classification bake o-: A review and experimental evaluation of recent algorithmic advances (2016) Data Mining and Knowledge Discovery Online FirstRangayyan, R.M., (2015) Biomedical Signal Analysis, 33. , John Wiley & SonsRutledge, D.N., Bouveresse, D.J.-R., Independent components analysis with the jade algorithm (2013) TrAC Trends in Analytical Chemistry, 50, pp. 22-32http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8958/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8958oai:repositorio.utb.edu.co:20.500.12585/89582023-05-25 15:53:21.956Repositorio Institucional UTBrepositorioutb@utb.edu.co

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