Electrical Impedance Tomography for Biomedical Application
Journal: IPASJ International Journal of Electronics & Communication (IIJEC) (Vol.3, No. 5)Publication Date: 2015-06-03
Authors : D. K. Kamat; Sunil M. Shinde; P. M. Patil;
Page : 15-20
Keywords : Keywords: Electrodes; Analog multiplexer; ARM processor; Impedance converter; EIDORS.;
Abstract
ABSTRACT In this paper design and implementation of Electrical Impedance Tomography (EIT) system has been discussed. EIT is a method that constructs the electrical impedance distribution image of the cross section of a body based on current excitation and voltage measurement from electrode array. The EIT image provides the significant physiological and pathological information according to the electrical property of the tissue inside the human body. Since the image quality heavily depends on the performance of the applied current, such as frequency, current accuracy and stability, the design of a steady and highly accurate signal source is of great significance. In EIT measurement, multi-frequency system can provide more useful information about the body tissue, so the current source of the EIT system should supply multi-frequency signal for measurement. Both real part and imaginary part of the signal from the EIT measurements contain abundant physiological and pathological information about human body. Hence, a signal source that provides accurate excitation signal for measurement and reference signal is quite important in EIT system. The impedance data is then exported to MATLAB for position correlation and post processing. In medical applications, due to the differences in bioelectrical properties between tissues, the conductivity distribution can show the structural and functional properties of the subject. Different organs of human body show a contrast in bioimpedance imaging. Further, physiological variations, such as increased blood volume in lungs, cause bioelectrical property changes which can be imaged as a varying conductivity distribution.
Other Latest Articles
- Analysis & Performance Evolution of IRIS Recognition SVD, KLV and EBP Algorithms using Neural Network Classifier
- Predicting Protein-Protein Interactions through Associative Classification Technique
- An Encapsulated Approach for Microarray Sample Classification using Supervised Attribute clustering and Fuzzy Classification Algorithm
- A Model For Secure Data Sharing Using Attribute Based Encryption
- An advancement to the security level through the Galois field in the existing password based approach of hiding classified information in images
Last modified: 2015-06-05 14:45:31