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https://ir.swu.ac.th/jspui/handle/123456789/14915
Title: | Investigation of catheter curvature and genetic algorithms in conductance catheter optimization |
Authors: | Thaijiam C. Gale T.J. |
Keywords: | Blood Electrodes Finite element method Genetic algorithms Optimization Blood volume measurement Catheter curvature Conductance catheter optimization Electrode configurations Catheters algorithm article biological model catheterization electric conductivity electrode electrophysiology equipment equipment design finite element analysis heart volume human impedance cardiography methodology signal processing statistical analysis statistical model theoretical model Algorithms Cardiac Volume Cardiography, Impedance Catheterization Data Interpretation, Statistical Electric Conductivity Electrodes Electrophysiology Equipment Design Finite Element Analysis Humans Models, Genetic Models, Statistical Models, Theoretical Signal Processing, Computer-Assisted |
Issue Date: | 2007 |
Abstract: | Catheter curvature affects accuracy of intraventricular blood volume measurement when using conductance catheter techniques, especially with irregular geometries, such as in the right ventricle. To investigate this effect, we present results from using different curved catheter configurations and different numbers of electrodes in a simple Finite Element model. It was found that there is an apparent increase in accuracy with curvature, due to greater linearity in the field in the region of the measurement electrodes, which are located farther from the source electrodes as curvature increases. Also, optimization using Genetic Algorithms is presented as a method to find the optimal distribution of measurement electrodes. We plan to extend these results to develop improved electrode configurations for using in blood volume measurement in the right ventricle. © 2007 IEEE. |
URI: | https://ir.swu.ac.th/jspui/handle/123456789/14915 https://www.scopus.com/inward/record.uri?eid=2-s2.0-57649236206&doi=10.1109%2fIEMBS.2007.4352759&partnerID=40&md5=2b49533e37803949f75a26b2035ffb7f |
ISSN: | 5891019 |
Appears in Collections: | Scopus 1983-2021 |
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