DocumentCode :
307720
Title :
A neural network reconstruction algorithm for Intraventricular Impedance Imaging
Author :
Walker, Gregory W. ; Kun, Stevan ; Peura, Robert A.
Author_Institution :
Dept. of Biomed. Eng., Worcester Polytech. Inst., MA, USA
Volume :
1
fYear :
1995
fDate :
20-25 Sep 1995
Firstpage :
809
Abstract :
An Intraventricular Impedance Imaging (III) system, that will be used for assessing electrical and mechanical cardiac properties via an intraventricular catheter, is presently under development. One of the major problems to be solved is the determination of the intraventricular catheter position within the ventricle. Existing methods for determining catheter position within a cardiac ventricle, including X-ray, fluoroscopy and computer tomography, are accurate but cumbersome, expensive, and unable to ascertain the continuous real-time intraventricular catheter position. The purpose of this work was to develop a reconstruction algorithm, based on Artificial Neural Networks (ANN), which will be used to process the electrical information from the catheter to ascertain the continuous, real-time intraventricular catheter position. A back-propagation neural network was trained using results from computer simulations of the III system. The neural network predicted the desired output variables with errors ranging from 0.05% to 1.8% and correlation coefficients (r) ranging from 83% to 99% The RMS error of the output variables was 4.9% These results indicate that ANN have great potential as a tool in determining the continuous real-time intraventricular catheter position
Keywords :
backpropagation; cardiology; electric impedance imaging; electrocardiography; image reconstruction; medical image processing; multilayer perceptrons; ANN; Artificial Neural Networks; Intraventricular Impedance Imaging; RMS error; back-propagation neural network; catheter position; computer simulations; continuous real-time intraventricular catheter position; correlation coefficients; electrical properties; errors; four layer back propagation ANN; intraventricular catheter; mechanical cardiac properties; neural network reconstruction algorithm; output variables; ventricle; Artificial neural networks; Biomedical electrodes; Biomedical imaging; Catheters; Geometry; Impedance; Neural networks; Pressure measurement; Reconstruction algorithms; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-2475-7
Type :
conf
DOI :
10.1109/IEMBS.1995.575374
Filename :
575374
Link To Document :
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