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Artifacts and noise removal in electrocardiograms using independent component analysis
Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove arti...
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Published in: | International journal of cardiology 2008-09, Vol.129 (2), p.278-281 |
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container_title | International journal of cardiology |
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creator | CHAWLA, M. P. S VERMA, H. K KUMAR, Vinod |
description | Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms. |
doi_str_mv | 10.1016/j.ijcard.2007.06.037 |
format | article |
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In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.</description><subject>Algorithms</subject><subject>Biological and medical sciences</subject><subject>Cardiology. Vascular system</subject><subject>Cardiovascular Diseases - diagnosis</subject><subject>Cardiovascular Diseases - physiopathology</subject><subject>Electrocardiography - methods</subject><subject>Humans</subject><subject>Medical sciences</subject><subject>Models, Statistical</subject><subject>Signal Processing, Computer-Assisted</subject><issn>0167-5273</issn><issn>1874-1754</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNo9j09LAzEQxYMotla_gUguHned_Nskx1KsCgUv6rVMs2lJ2c0uyVbot3eL1cubecxvZniE3DMoGbDqaV-GvcNUlxxAl1CVIPQFmTKjZcG0kpdkOmK6UFyLCbnJeQ8A0lpzTSZMV8ZqJqfka56GsEU3ZIqxprEL2dPk2-4bGxoi9Y13Q-pOj0K3S9hmesgh7sZZ7Xs_Shyo69q-i6cOIzbHHPItudpik_3duc7I5_L5Y_FarN5f3hbzVdFzMEMhJQjL0Ukja-ksMgdOcokb5ZRSnKEXFa8Bjd2MlssatVCnHa21AMPEjDz83u0Pm9bX6z6FFtNx_RdwBB7PAGaHzTZhdCH_cxwqbZk14gdohWHO</recordid><startdate>20080926</startdate><enddate>20080926</enddate><creator>CHAWLA, M. P. S</creator><creator>VERMA, H. 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Vascular system</topic><topic>Cardiovascular Diseases - diagnosis</topic><topic>Cardiovascular Diseases - physiopathology</topic><topic>Electrocardiography - methods</topic><topic>Humans</topic><topic>Medical sciences</topic><topic>Models, Statistical</topic><topic>Signal Processing, Computer-Assisted</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>CHAWLA, M. P. S</creatorcontrib><creatorcontrib>VERMA, H. K</creatorcontrib><creatorcontrib>KUMAR, Vinod</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><jtitle>International journal of cardiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>CHAWLA, M. P. S</au><au>VERMA, H. K</au><au>KUMAR, Vinod</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artifacts and noise removal in electrocardiograms using independent component analysis</atitle><jtitle>International journal of cardiology</jtitle><addtitle>Int J Cardiol</addtitle><date>2008-09-26</date><risdate>2008</risdate><volume>129</volume><issue>2</issue><spage>278</spage><epage>281</epage><pages>278-281</pages><issn>0167-5273</issn><eissn>1874-1754</eissn><coden>IJCDD5</coden><abstract>Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.</abstract><cop>Shannon</cop><pub>Elsevier Science</pub><pmid>17689714</pmid><doi>10.1016/j.ijcard.2007.06.037</doi><tpages>4</tpages></addata></record> |
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subjects | Algorithms Biological and medical sciences Cardiology. Vascular system Cardiovascular Diseases - diagnosis Cardiovascular Diseases - physiopathology Electrocardiography - methods Humans Medical sciences Models, Statistical Signal Processing, Computer-Assisted |
title | Artifacts and noise removal in electrocardiograms using independent component analysis |
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