Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning
A method for electrocardiogram (ECG) pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal featu...
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| Veröffentlicht in: | Control theory and technology 2014-11, Vol.12 (4), p.333-344 |
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| creator | Dong, Xunde Wang, Cong Hu, Junmin Ou, Shanxing |
| description | A method for electrocardiogram (ECG) pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases: the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach. |
| doi_str_mv | 10.1007/s11768-014-4056-4 |
| format | Article |
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Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases: the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach.</description><identifier>ISSN: 2095-6983</identifier><identifier>EISSN: 2198-0942</identifier><identifier>DOI: 10.1007/s11768-014-4056-4</identifier><language>eng</language><publisher>Heidelberg: South China University of Technology and Academy of Mathematics and Systems Science, CAS</publisher><subject>Complexity ; Computational Intelligence ; Constants ; Control ; Control and Systems Theory ; Dynamics ; Engineering ; Learning ; Learning theory ; Mechatronics ; Modelling ; Optimization ; Pattern recognition ; Recognition ; Robotics ; Systems Theory</subject><ispartof>Control theory and technology, 2014-11, Vol.12 (4), p.333-344</ispartof><rights>South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2014</rights><rights>Copyright © Wanfang Data Co. 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Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases: the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach.</description><subject>Complexity</subject><subject>Computational Intelligence</subject><subject>Constants</subject><subject>Control</subject><subject>Control and Systems Theory</subject><subject>Dynamics</subject><subject>Engineering</subject><subject>Learning</subject><subject>Learning theory</subject><subject>Mechatronics</subject><subject>Modelling</subject><subject>Optimization</subject><subject>Pattern recognition</subject><subject>Recognition</subject><subject>Robotics</subject><subject>Systems Theory</subject><issn>2095-6983</issn><issn>2198-0942</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LxDAQxYMouOh-AG89rkI1kz9Ne5RlXYUFD-49ZNO0ZG2TNekq9dObpYInTzMMvzcz7yF0A_geMBYPEUAUZY6B5QzzImdnaEagSpOKkfPU44rnRVXSSzSPcY9xIkFQWs7Q26ozegheq1Bb3wbVZ4vVcn2bHdQwmOCy3tems67NlKuzYLRvnR2sd9mnVVltEtNbZ-NgddYZFVxCr9FFo7po5r_1Cm2fVtvlc755Xb8sHze5JgIgV0zDTgvFhVHEFEB0TXVBa76rRQEN5ZQpzgQhFS1ZA0XJCbAm8XRXiKaiV-huWvulXKNcK_f-GFw6KN-_u24cR2lIMopZspvgxQQfgv84mjjI3kZtuk45449RQolLAM44SShMqA4-xmAaeQi2V2GUgOUpbznlLdNyecpbsqQhkyYm1rUm_D3zv-gHVquB6Q</recordid><startdate>20141101</startdate><enddate>20141101</enddate><creator>Dong, Xunde</creator><creator>Wang, Cong</creator><creator>Hu, Junmin</creator><creator>Ou, Shanxing</creator><general>South China University of Technology and Academy of Mathematics and Systems Science, CAS</general><general>School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China%Department of Radiology, General Hospital of Guangzhou Military Command, Guangzhou Guangdong 510010, China</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20141101</creationdate><title>Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning</title><author>Dong, Xunde ; Wang, Cong ; Hu, Junmin ; Ou, Shanxing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2711-a4c1bc7a57ea2e612cd3c63d5bd761f3534a547229384f1685214f7a53b67f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Complexity</topic><topic>Computational Intelligence</topic><topic>Constants</topic><topic>Control</topic><topic>Control and Systems Theory</topic><topic>Dynamics</topic><topic>Engineering</topic><topic>Learning</topic><topic>Learning theory</topic><topic>Mechatronics</topic><topic>Modelling</topic><topic>Optimization</topic><topic>Pattern recognition</topic><topic>Recognition</topic><topic>Robotics</topic><topic>Systems Theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dong, Xunde</creatorcontrib><creatorcontrib>Wang, Cong</creatorcontrib><creatorcontrib>Hu, Junmin</creatorcontrib><creatorcontrib>Ou, Shanxing</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Control theory and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dong, Xunde</au><au>Wang, Cong</au><au>Hu, Junmin</au><au>Ou, Shanxing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning</atitle><jtitle>Control theory and technology</jtitle><stitle>Control Theory Technol</stitle><date>2014-11-01</date><risdate>2014</risdate><volume>12</volume><issue>4</issue><spage>333</spage><epage>344</epage><pages>333-344</pages><issn>2095-6983</issn><eissn>2198-0942</eissn><abstract>A method for electrocardiogram (ECG) pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases: the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach.</abstract><cop>Heidelberg</cop><pub>South China University of Technology and Academy of Mathematics and Systems Science, CAS</pub><doi>10.1007/s11768-014-4056-4</doi><tpages>12</tpages></addata></record> |
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| source | SpringerNature Journals; Alma/SFX Local Collection |
| subjects | Complexity Computational Intelligence Constants Control Control and Systems Theory Dynamics Engineering Learning Learning theory Mechatronics Modelling Optimization Pattern recognition Recognition Robotics Systems Theory |
| title | Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning |
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