Independent component analysis and decision trees for ECG holter recording de-noising.

We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standar...

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Autores principales:	Jakub Kuzilek, Vaclav Kremen, Filip Soucek, Lenka Lhotska
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/204945ac48f54642b99721b7df9e0392
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id	oai:doaj.org-article:204945ac48f54642b99721b7df9e0392
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spelling	oai:doaj.org-article:204945ac48f54642b99721b7df9e03922021-11-18T08:16:44ZIndependent component analysis and decision trees for ECG holter recording de-noising.1932-620310.1371/journal.pone.0098450https://doaj.org/article/204945ac48f54642b99721b7df9e03922014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24905359/?tool=EBIhttps://doaj.org/toc/1932-6203We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.Jakub KuzilekVaclav KremenFilip SoucekLenka LhotskaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e98450 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jakub Kuzilek Vaclav Kremen Filip Soucek Lenka Lhotska Independent component analysis and decision trees for ECG holter recording de-noising.
description	We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.
format	article
author	Jakub Kuzilek Vaclav Kremen Filip Soucek Lenka Lhotska
author_facet	Jakub Kuzilek Vaclav Kremen Filip Soucek Lenka Lhotska
author_sort	Jakub Kuzilek
title	Independent component analysis and decision trees for ECG holter recording de-noising.
title_short	Independent component analysis and decision trees for ECG holter recording de-noising.
title_full	Independent component analysis and decision trees for ECG holter recording de-noising.
title_fullStr	Independent component analysis and decision trees for ECG holter recording de-noising.
title_full_unstemmed	Independent component analysis and decision trees for ECG holter recording de-noising.
title_sort	independent component analysis and decision trees for ecg holter recording de-noising.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/204945ac48f54642b99721b7df9e0392
work_keys_str_mv	AT jakubkuzilek independentcomponentanalysisanddecisiontreesforecgholterrecordingdenoising AT vaclavkremen independentcomponentanalysisanddecisiontreesforecgholterrecordingdenoising AT filipsoucek independentcomponentanalysisanddecisiontreesforecgholterrecordingdenoising AT lenkalhotska independentcomponentanalysisanddecisiontreesforecgholterrecordingdenoising
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Independent component analysis and decision trees for ECG holter recording de-noising.

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