Source localization of focal ventricular arrhythmias using linear estimation, correlation, and back propagation networks
Abstract Catheter-based approaches used in the localization and treatment of the source of heart rhythm disturbances (arrhythmias) have become popular, because they do not require highly invasive and risky open-chest operations. In most of the existing approaches, mapping of the outer surface (epica...
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description | Abstract Catheter-based approaches used in the localization and treatment of the source of heart rhythm disturbances (arrhythmias) have become popular, because they do not require highly invasive and risky open-chest operations. In most of the existing approaches, mapping of the outer surface (epicardium) is not possible even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this study, we report a feasibility study of a novel mapping approach targeting the epicardium which is based on the measurements of multielectrode catheters placed in the coronary veins. We investigated three methods in determining the most probable region of early activation, i.e., the region that contains the source of the abnormal activation on the heart, using only a set of sparse venous catheter recordings. The methods we proposed here were the linear estimation, correlation, and the back propagation networks. The linear estimation technique hypothesized the relationship between venous catheter measurements and unmeasured epicardial sites based on a previously recorded training data set. The correlation method included a comparative analysis between test and training epicardial activation time maps based on the measured values from the venous sites. In the back propagation method, the input layer consisted of the source data in the form of 42 nodes which were the activation time values from the intravenous catheter leads. We used two hidden layers with 600 and 500 nodes, respectively. The output layer consisted of 28 nodes in the output layer that corresponded to the manually selected early activation regions on the epicardium. The results of the linear estimation and the correlation methods showed that they could be used as a good predictor for the region of early activation, and thus, these approaches may be employed to direct a subsequent more focused electrophysiological study and curative radiofrequency (RF) ablation. The back propagation network approach performed relatively well for the right ventricularly paced beats and the results demonstrated its potential as a supporting technique to the estimation and correlation methods. The results of this study encourage further investigation and provides evidence that an epicardial mapping approach based on the venous catheter recordings is feasible and can provide adequate accuracy for clinical applications. |
doi_str_mv | 10.1016/j.compbiomed.2007.01.008 |
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In most of the existing approaches, mapping of the outer surface (epicardium) is not possible even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this study, we report a feasibility study of a novel mapping approach targeting the epicardium which is based on the measurements of multielectrode catheters placed in the coronary veins. We investigated three methods in determining the most probable region of early activation, i.e., the region that contains the source of the abnormal activation on the heart, using only a set of sparse venous catheter recordings. The methods we proposed here were the linear estimation, correlation, and the back propagation networks. The linear estimation technique hypothesized the relationship between venous catheter measurements and unmeasured epicardial sites based on a previously recorded training data set. The correlation method included a comparative analysis between test and training epicardial activation time maps based on the measured values from the venous sites. In the back propagation method, the input layer consisted of the source data in the form of 42 nodes which were the activation time values from the intravenous catheter leads. We used two hidden layers with 600 and 500 nodes, respectively. The output layer consisted of 28 nodes in the output layer that corresponded to the manually selected early activation regions on the epicardium. The results of the linear estimation and the correlation methods showed that they could be used as a good predictor for the region of early activation, and thus, these approaches may be employed to direct a subsequent more focused electrophysiological study and curative radiofrequency (RF) ablation. The back propagation network approach performed relatively well for the right ventricularly paced beats and the results demonstrated its potential as a supporting technique to the estimation and correlation methods. The results of this study encourage further investigation and provides evidence that an epicardial mapping approach based on the venous catheter recordings is feasible and can provide adequate accuracy for clinical applications.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2007.01.008</identifier><identifier>PMID: 17346691</identifier><identifier>CODEN: CBMDAW</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Algorithms ; Animals ; Arrhythmias, Cardiac - physiopathology ; Artificial neural networks ; Back propagation networks ; Cardiac Catheterization ; Catheter ; Dogs ; Electrophysiology ; Epicardial mapping ; Heart Ventricles - physiopathology ; Humans ; Internal Medicine ; Linear Models ; Models, Cardiovascular ; Neural Networks (Computer) ; Other ; Pericardium - physiopathology ; Statistical estimation</subject><ispartof>Computers in biology and medicine, 2007-10, Vol.37 (10), p.1437-1445</ispartof><rights>Elsevier Ltd</rights><rights>2007 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c517t-e62f9681614a98610bb5ae194dfd07647efaad6f0e7a00cda0e3ed51a118f9033</citedby><cites>FETCH-LOGICAL-c517t-e62f9681614a98610bb5ae194dfd07647efaad6f0e7a00cda0e3ed51a118f9033</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1033001405?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,64361,64363,64365,65309,72215</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17346691$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yilmaz, Bülent</creatorcontrib><creatorcontrib>Cunedioğlu, Uğur</creatorcontrib><title>Source localization of focal ventricular arrhythmias using linear estimation, correlation, and back propagation networks</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Abstract Catheter-based approaches used in the localization and treatment of the source of heart rhythm disturbances (arrhythmias) have become popular, because they do not require highly invasive and risky open-chest operations. In most of the existing approaches, mapping of the outer surface (epicardium) is not possible even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this study, we report a feasibility study of a novel mapping approach targeting the epicardium which is based on the measurements of multielectrode catheters placed in the coronary veins. We investigated three methods in determining the most probable region of early activation, i.e., the region that contains the source of the abnormal activation on the heart, using only a set of sparse venous catheter recordings. The methods we proposed here were the linear estimation, correlation, and the back propagation networks. The linear estimation technique hypothesized the relationship between venous catheter measurements and unmeasured epicardial sites based on a previously recorded training data set. The correlation method included a comparative analysis between test and training epicardial activation time maps based on the measured values from the venous sites. In the back propagation method, the input layer consisted of the source data in the form of 42 nodes which were the activation time values from the intravenous catheter leads. We used two hidden layers with 600 and 500 nodes, respectively. The output layer consisted of 28 nodes in the output layer that corresponded to the manually selected early activation regions on the epicardium. The results of the linear estimation and the correlation methods showed that they could be used as a good predictor for the region of early activation, and thus, these approaches may be employed to direct a subsequent more focused electrophysiological study and curative radiofrequency (RF) ablation. The back propagation network approach performed relatively well for the right ventricularly paced beats and the results demonstrated its potential as a supporting technique to the estimation and correlation methods. The results of this study encourage further investigation and provides evidence that an epicardial mapping approach based on the venous catheter recordings is feasible and can provide adequate accuracy for clinical applications.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Arrhythmias, Cardiac - physiopathology</subject><subject>Artificial neural networks</subject><subject>Back propagation networks</subject><subject>Cardiac Catheterization</subject><subject>Catheter</subject><subject>Dogs</subject><subject>Electrophysiology</subject><subject>Epicardial mapping</subject><subject>Heart Ventricles - physiopathology</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Linear Models</subject><subject>Models, Cardiovascular</subject><subject>Neural Networks (Computer)</subject><subject>Other</subject><subject>Pericardium - physiopathology</subject><subject>Statistical estimation</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkk1v1DAQhi0EosvCX0CWkDiRME4cO7kg0YovqRKHwtlynEnr3Wy82Elh-fVM2BWVeqEna-R3vt5nGOMCcgFCvd3kLuz2rQ877PICQOcgcoD6EVuJWjcZVKV8zFYAAjJZF9UZe5bSBgAklPCUnQldSqUasWK_rsIcHfIhODv433byYeSh5_0S81scp-jdPNjIbYw3h-lm523ic_LjNR_8iPSBafK7v4lvuAsx4nAK7Njx1rot38ewt9fH2iNOP0PcpufsSW-HhC9O75p9__jh28Xn7PLrpy8X7y8zVwk9ZaiKvlG1UELaplYC2rayKBrZ9R1oJTX21naqB9QWwHUWsMSuElaIum-gLNfs9bEuDfFjplnNzieHw2BHDHMyVLuSlSr-KyxAF0DmPkBYilKCJuGre8INmT3StkbQZERHEqg1q48qF0NKEXuzj-RnPJDILLTNxtzRNgttA8IQbUp9eWowt8vfv8QTXhKcHwVIDt96jCY5j6PDzkd0k-mCf0iXd_eKOCLv6T62eMB0t5NJhQFztVzdcnSg6eCIVPkHqnHXAQ</recordid><startdate>20071001</startdate><enddate>20071001</enddate><creator>Yilmaz, Bülent</creator><creator>Cunedioğlu, Uğur</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7QO</scope><scope>7X8</scope></search><sort><creationdate>20071001</creationdate><title>Source localization of focal ventricular arrhythmias using linear estimation, correlation, and back propagation networks</title><author>Yilmaz, Bülent ; Cunedioğlu, Uğur</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c517t-e62f9681614a98610bb5ae194dfd07647efaad6f0e7a00cda0e3ed51a118f9033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Arrhythmias, Cardiac - physiopathology</topic><topic>Artificial neural networks</topic><topic>Back propagation networks</topic><topic>Cardiac Catheterization</topic><topic>Catheter</topic><topic>Dogs</topic><topic>Electrophysiology</topic><topic>Epicardial mapping</topic><topic>Heart Ventricles - physiopathology</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>Linear Models</topic><topic>Models, Cardiovascular</topic><topic>Neural Networks (Computer)</topic><topic>Other</topic><topic>Pericardium - physiopathology</topic><topic>Statistical estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yilmaz, Bülent</creatorcontrib><creatorcontrib>Cunedioğlu, Uğur</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Biotechnology Research Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yilmaz, Bülent</au><au>Cunedioğlu, Uğur</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Source localization of focal ventricular arrhythmias using linear estimation, correlation, and back propagation networks</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2007-10-01</date><risdate>2007</risdate><volume>37</volume><issue>10</issue><spage>1437</spage><epage>1445</epage><pages>1437-1445</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><coden>CBMDAW</coden><abstract>Abstract Catheter-based approaches used in the localization and treatment of the source of heart rhythm disturbances (arrhythmias) have become popular, because they do not require highly invasive and risky open-chest operations. In most of the existing approaches, mapping of the outer surface (epicardium) is not possible even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this study, we report a feasibility study of a novel mapping approach targeting the epicardium which is based on the measurements of multielectrode catheters placed in the coronary veins. We investigated three methods in determining the most probable region of early activation, i.e., the region that contains the source of the abnormal activation on the heart, using only a set of sparse venous catheter recordings. The methods we proposed here were the linear estimation, correlation, and the back propagation networks. The linear estimation technique hypothesized the relationship between venous catheter measurements and unmeasured epicardial sites based on a previously recorded training data set. The correlation method included a comparative analysis between test and training epicardial activation time maps based on the measured values from the venous sites. In the back propagation method, the input layer consisted of the source data in the form of 42 nodes which were the activation time values from the intravenous catheter leads. We used two hidden layers with 600 and 500 nodes, respectively. The output layer consisted of 28 nodes in the output layer that corresponded to the manually selected early activation regions on the epicardium. The results of the linear estimation and the correlation methods showed that they could be used as a good predictor for the region of early activation, and thus, these approaches may be employed to direct a subsequent more focused electrophysiological study and curative radiofrequency (RF) ablation. The back propagation network approach performed relatively well for the right ventricularly paced beats and the results demonstrated its potential as a supporting technique to the estimation and correlation methods. The results of this study encourage further investigation and provides evidence that an epicardial mapping approach based on the venous catheter recordings is feasible and can provide adequate accuracy for clinical applications.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>17346691</pmid><doi>10.1016/j.compbiomed.2007.01.008</doi><tpages>9</tpages></addata></record> |
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subjects | Algorithms Animals Arrhythmias, Cardiac - physiopathology Artificial neural networks Back propagation networks Cardiac Catheterization Catheter Dogs Electrophysiology Epicardial mapping Heart Ventricles - physiopathology Humans Internal Medicine Linear Models Models, Cardiovascular Neural Networks (Computer) Other Pericardium - physiopathology Statistical estimation |
title | Source localization of focal ventricular arrhythmias using linear estimation, correlation, and back propagation networks |
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