New detection method based on ECG signal features to determine localization and extent of myocardial infarction using Body Surface Potential Map data
In this study, a method for determining the location and extent of myocardial infarction using BSPM data that was obtained from PhysioNet challenge 2007 database has been suggested. This data is related to the four patients with MI that we used from two patients as training set to determine rules, a...
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description | In this study, a method for determining the location and extent of myocardial infarction using BSPM data that was obtained from PhysioNet challenge 2007 database has been suggested. This data is related to the four patients with MI that we used from two patients as training set to determine rules, and from two other patients for testing set and the conclusion of the proposed model. At first, T-wave amplitude, R-wave amplitude and integration of T-wave as three features of ECG signals were extracted. Then with definition and applying several rules and threshold levels for those features, areas that are with MI and these extents were diagnosed. In this study to determine the precise location of MI, 17-segments standard model of left ventricle (LV) was used. Finally, overall accuracy of this method that expressed with SO parameter and EPD parameter for two patients in test set was obtained to 0.94 and 5.37, respectively. The main advantages of this method were its simplicity and high accuracy. |
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J. ; Attarodi, G.</creator><creatorcontrib>Safdarian, N. ; Dabanloo, N. J. ; Attarodi, G.</creatorcontrib><description>In this study, a method for determining the location and extent of myocardial infarction using BSPM data that was obtained from PhysioNet challenge 2007 database has been suggested. This data is related to the four patients with MI that we used from two patients as training set to determine rules, and from two other patients for testing set and the conclusion of the proposed model. At first, T-wave amplitude, R-wave amplitude and integration of T-wave as three features of ECG signals were extracted. Then with definition and applying several rules and threshold levels for those features, areas that are with MI and these extents were diagnosed. In this study to determine the precise location of MI, 17-segments standard model of left ventricle (LV) was used. Finally, overall accuracy of this method that expressed with SO parameter and EPD parameter for two patients in test set was obtained to 0.94 and 5.37, respectively. 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In this study to determine the precise location of MI, 17-segments standard model of left ventricle (LV) was used. Finally, overall accuracy of this method that expressed with SO parameter and EPD parameter for two patients in test set was obtained to 0.94 and 5.37, respectively. The main advantages of this method were its simplicity and high accuracy.</description><subject>Electrocardiography</subject><subject>Feature extraction</subject><subject>Mathematical model</subject><subject>Myocardium</subject><subject>Torso</subject><subject>Training</subject><issn>0276-6574</issn><issn>2325-8853</issn><isbn>1467320765</isbn><isbn>9781467320764</isbn><isbn>9781467320757</isbn><isbn>9781467320771</isbn><isbn>1467320757</isbn><isbn>1467320773</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1jUlOw0AQRZtJIgk5AZu6gKUe3fYSojBIYZDIPiq7y6GRh6jdEYR7cF-cBFalr_ffrxM2zW0mdGqV5NbYUzaSSpoky4w6Y-N_kJpzNuLSpklqrL5k477_4FzkuTUj9vNMn-AoUhl910JD8b1zUGBPDoY8n91D79ct1lARxm2gHmJ3EELjW4K6K7H233iwsXVAX5HaCF0FzW5gwfnB9W2F4fhh2_t2Dbed28HbNlRYErx2e2Xfe8INOIx4xS4qrHua_t0JW97Nl7OHZPFy_zi7WSReWBMThcIJ7oSTKCUVnHRRulIVaWZQaCFLpXM0yhWoChJGV3lmhU25KgauSjVh18dZT0SrTfANht0q1ZJrLdQvtqpnPw</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Safdarian, N.</creator><creator>Dabanloo, N. 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J.</creatorcontrib><creatorcontrib>Attarodi, G.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore Digital Library</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Safdarian, N.</au><au>Dabanloo, N. J.</au><au>Attarodi, G.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>New detection method based on ECG signal features to determine localization and extent of myocardial infarction using Body Surface Potential Map data</atitle><btitle>2012 Computing in Cardiology</btitle><stitle>CiC</stitle><date>2012-09</date><risdate>2012</risdate><spage>505</spage><epage>508</epage><pages>505-508</pages><issn>0276-6574</issn><eissn>2325-8853</eissn><isbn>1467320765</isbn><isbn>9781467320764</isbn><eisbn>9781467320757</eisbn><eisbn>9781467320771</eisbn><eisbn>1467320757</eisbn><eisbn>1467320773</eisbn><abstract>In this study, a method for determining the location and extent of myocardial infarction using BSPM data that was obtained from PhysioNet challenge 2007 database has been suggested. This data is related to the four patients with MI that we used from two patients as training set to determine rules, and from two other patients for testing set and the conclusion of the proposed model. At first, T-wave amplitude, R-wave amplitude and integration of T-wave as three features of ECG signals were extracted. Then with definition and applying several rules and threshold levels for those features, areas that are with MI and these extents were diagnosed. In this study to determine the precise location of MI, 17-segments standard model of left ventricle (LV) was used. Finally, overall accuracy of this method that expressed with SO parameter and EPD parameter for two patients in test set was obtained to 0.94 and 5.37, respectively. The main advantages of this method were its simplicity and high accuracy.</abstract><pub>IEEE</pub><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Electrocardiography Feature extraction Mathematical model Myocardium Torso Training |
title | New detection method based on ECG signal features to determine localization and extent of myocardial infarction using Body Surface Potential Map data |
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