NDLI: A neural network reconstruction algorithm for Intraventricular Impedance Imaging

Content Provider	IEEE Xplore Digital Library
Author	Walker, G.W. Kun, S. Peura, R.A.
Copyright Year	1995
Description	Author affiliation: Dept. of Biomed. Eng., Worcester Polytech. Inst., MA, USA (Walker, G.W.)
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.
Starting Page	809
Ending Page	810
File Size	374104
Page Count	2
File Format	PDF
ISBN	0780324757
DOI	10.1109/IEMBS.1995.575374
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-09-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Reconstruction algorithms Impedance Catheters Artificial neural networks Geometry Pressure measurement Biomedical imaging Biomedical electrodes Voltage
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Utilization of neural networks for catheter tip position determination in intraventricular impedance imaging

Comparative sensitivity analysis between spherical and cylindrical intraventricular impedance imaging catheters

RBF neural network image reconstruction for electrical impedance tomography

Development of a testing model for an intraventricular impedance imaging system

Estimating intraventricular Catheter Position Via Impedance Measurements

Real Time Measurement Of Intraventricular Catheter Position

Reconstruction Algorithms Based on Artificial Neural Network for Electrical Capacitance Tomography

The application of artificial neural networks to intraventricular impedance imaging

Electrical impedance tomography reconstruction algorithm using extended Kalman filter

A neural network reconstruction algorithm for Intraventricular Impedance Imaging

Similar Documents

Utilization of neural networks for catheter tip position determination in intraventricular impedance imaging

Comparative sensitivity analysis between spherical and cylindrical intraventricular impedance imaging catheters

RBF neural network image reconstruction for electrical impedance tomography

Development of a testing model for an intraventricular impedance imaging system

Estimating intraventricular Catheter Position Via Impedance Measurements

Real Time Measurement Of Intraventricular Catheter Position

Reconstruction Algorithms Based on Artificial Neural Network for Electrical Capacitance Tomography

The application of artificial neural networks to intraventricular impedance imaging

Electrical impedance tomography reconstruction algorithm using extended Kalman filter

A neural network reconstruction algorithm for Intraventricular Impedance Imaging