Stevan Kun

Algorithm for tissue ischemia estimation based on electrical impedance spectroscopy

The purpose of this paper is to present an algorithm developed for real-time estimation of skeletal muscle ischemia, based on parameters extracted from in vivo obtained electrical impedance spectra. A custom impedance spectrometer was used to acquire data sets: complex impedance spectra measured at 27 frequencies in the range of 100 Hz-1 MHz, and tissue pH. Twenty-nine in vivo animal studies on rabbit anterior tibialis muscle were performed to gather data on the behavior of tissue impedance during ischemia. An artificial neural network (ANN) was used to quantitatively describe the relationship between the parameters of complex tissue impedance spectra and tissue ischemia via pH. The ANN was trained on 1249, and tested on 946 ischemic tissue impedance data sets. A correlation of 94.5% and a standard deviation of 0.15 pH units was achieved between the ANN estimated pH and measured tissue pH values.

Real-time extraction of tissue impedance model parameters for electrical impedance spectrometer

AbstractThis paper presents a new algorithm for real-time extraction of tissue electrical impedance model parameters from in vivo electrical impedance spectroscopic measurements. This algorithm was developed as a part of a system for muscle tissue ischemia measurements using electrical impedance spectroscopy. An iterative least square fitting method, biased with a priori knowledge of the impedance model was developed. It simultaneously uses both the real and imaginary impedance spectra to calculate tissue parameters R0, R∞, α and τ. The algorithm was tested with simulated data, and during real-time in vivo ischemia experiments. Experimental results were achieved with standard deviations of

Muscle tissue ischemia monitoring using impedance spectroscopy: quantitative results of animal studies

\sigma _{R_0 } = 0.80\% , \sigma _{R_\infty } = 0.84\%

Development of an impedance spectrometer for tissue ischemia monitoring: application of synchronous sampling principle

, σα=0.72%, and στ=1.26%. On a Pentium II based PC, the algorithm converges to within 0.1% of the results in 17 ms. The results show that the algorithm possesses excellent parameter extraction capabilities, repeatability, speed and noise rejection.

Muscle tissue ischemia monitoring using impedance spectroscopy: preliminary results

Intraventricular Impedance Imaging (III) applies an innovative technique for estimation of cardiac catheter position. It is based on electrical impedance measurements and uses a multi-electrode probe mounted at a tip of a cardiac catheter. After analyzing the previous versions of the III system, a new system hardware realization was proposed, built, and tested. The signal acquisition module is based on AD7850-a highly integrated electronic component. A special protocol was developed for optimal high speed communication between the 32 channel III system and the IBM PC controller. This protocol also significantly reduces the required electronic component count. The results obtained by the prototype evaluation showed that: the proposed realization is working according to expectations, the signal generation provides sufficiently accurate signals, and the system requires 3 ms to generate a set of data-sufficient for real-time 60 Hz determination of catheter position and instantaneous ventricular volume.

Development of an impedance spectrometer for tissue ischemia monitoring: instrument realization and performance

Impedance spectroscopy is a promising method for tissue ischemia measurement, because it is non-invasive and suitable for long-term continuous monitoring. Ischemia causes biochemical and physiological changes in tissue which influence the tissue impedance. These changes can be detected by impedance spectroscopic measurements. To fully develop this method, the authors first constructed a dedicated instrument impedance spectrometer, and then used it to experimentally investigate the relationships between ischemia and tissue impedance. Results from in-vivo animal experiments indicate that the relationships between tissue impedance and ischemia is complex and nonlinear. In order to quantitatively describe this relationship, the authors used artificial neural networks to develop an algorithm for predicting tissue pH based on measured complex tissue impedance (tissue pH is the best known quantitative estimator of the ischemia levels). Preliminary experimental results show a good correlation (/spl ap/95%) between the predicted and actual ischemia levels (expressed in pH). The achieved correlation level is not yet sufficient to provide for accurate measurements of tissue pH.

Muscle tissue ischemia monitoring using impedance spectroscopy: preliminary animal studies

We have developed a noninvasive blood glucose measuring instrument, based on application of the Optical Bridge/sup TM/ in the near-infrared region. This exploratory research is an endeavor to evaluate the possibility of increasing the performance of the noninvasive glucose monitor by employing Artificial Neural Networks (ANN). The objective of this research is to design an ANN to interpret the instruments outputs as well as the system parameters, and correlate them with blood glucose levels. The main hypothesis of this project is that such an ANN can be designed to improve the performance of this instrument.

Determination of a relationship between bacteria levels and tissue pH in wounds: animal studies

The objective of our research Is to develop an impedance spectrometer which will be used for tissue ischemia monitoring. After analyzing the standard methods for complex impedance measurements, a new system realization was proposed. The approach is based on the four electrode impedance measurement method, using synchronous sampling demodulation with one analog channel. Realization of this method, based on the newest generation of highly integrated electronic components for generating sinusoidal signals of arbitrary frequencies, is simple and provides accurate measurements. This approach enables complete microprocessor control of measurements. It has the potential to be fully integrated into a hand-held instrument.