Estimating electrical conductivity tensors of biological tissues using microelectrode arrays

Gilboa, Elad; Rosa, Patricio S. La; Nehorai, Arye

doi:10.1109/embc.2012.6346112

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…These are not the only examples of data assimilation in biomedical applications. We mention, for instance, the work in electrocardiology for the setup of patient-specific models in [19], and for estimating cardiac conductivities [32,37,74]. Other applications can be found, e.g., in [17,29].…”

Section: Some Applications Of Data Assimilation In Hemodynamics Problemsmentioning

confidence: 99%

Data Assimilation in Cardiovascular Fluid–Structure Interaction Problems: An Introduction

Bertagna

D’Elia

Perego

et al. 2014

Fluid-Structure Interaction and Biomedical Applications

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Numerical methods for incompressible fluid dynamics have recently received a strong impulse from the applications to the cardiovascular system. In particular, fluid-structure interaction methods have been extensively investigated in view of an accurate and possibly fast simulation of blood flow in arteries and veins. This has been strongly motivated by the progressive interest in using numerical tools not only for understanding the general physiology and pathology of the vascular system. The opportunity offered by medical images properly preprocessed and elaborated to simulate blood flow in real patients highlighted the potential impact of scientific computing on the clinical practice. Therefore, in silico experiments are currently extensively used in bioengineering for completing (and sometimes driving) more traditional in vivo and in vitro investigations. Parallel to the development of numerical models, the need for quantitative analysis for diagnostic purposes has strongly stimulated the design of new methods and instruments for measurements and imaging. Thanks to these developments, a huge amount of data is nowadays available. Data Assimilation is the accurate merging of measures (including images) and numerical simulations for a mathematically sound integration of different sources of information. The outcome of this process includes both the patient-specific measures and the general principles underlying the development of mathematical models. In this way, simulations are adapted to the availability of individual data and are therefore supposed to be more reliable; measures are correspondingly filtered by the mathematical models assumed to describe the underlying phenomena, resulting in a (hopefully) significant reduction of the noise.This chapter provides an introduction to methods for data assimilation, mostly developed in fields like meteorology, applied to computational hemodynamics. We focus mainly on two of them: methods based on stochastic arguments (Kalman filtering) and variational methods. We also address some examples that have been approached with different techniques, in particular the estimation of vascular compliance from displacement measures.

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Section: Some Applications Of Data Assimilation In Hemodynamics Problemsmentioning

confidence: 99%

Data Assimilation in Cardiovascular Fluid–Structure Interaction Problems: An Introduction

Bertagna

D’Elia

Perego

et al. 2014

Fluid-Structure Interaction and Biomedical Applications

View full text Add to dashboard Cite

show abstract

“…The inverse problem of inferring conductivities from measurements of electric potential is highly ill-posed and computationally intensive [7]. Recent work in this area includes a proposed method that maps the electrical activation of cardiac tissue and then uses a least squares and singular value decomposition approach to obtain the optimum set of cardiac parameters [8].…”

Section: Introductionmentioning

confidence: 99%

“…Recent work in this area includes a proposed method that maps the electrical activation of cardiac tissue and then uses a least squares and singular value decomposition approach to obtain the optimum set of cardiac parameters [8]. A new suggested approach [7], applied to a 2D monodomain, is to simplify the problem into a set of computationally tractable problems via a single-step approximation. Other recent work in this area includes an investigation into the sensitivity of the forward problem of electrocardiology to the tissue conductivity parameters associated with the various organs in the human thorax [6].…”

Section: Introductionmentioning

confidence: 99%

A multi-electrode array and inversion technique for retrieving six conductivities from heart potential measurements

Johnston

2013

Med Biol Eng Comput

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A method for accurately finding cardiac bidomain conductivity parameters is a crucial part of efforts to study and understand the electrical functioning of the heart. The bidomain model considers current flowing along (longitudinal) and across (transverse) sheets of cardiac fibres, as well as between these sheets (normal), in both the extracellular and intracellular domains, which leads to six conductivity values. To match experimental studies, such a method must be able to determine these six conductivity values, not just the four where it is assumed that the transverse and normal conductivities are equal. This study presents a mathematical model, solution technique, multi-electrode array and two-pass inversion method, which can be used to retrieve all six conductivities from measurements of electrical potential made on the array. Simulated measurements of potential, to which noise is added, are used to demonstrate the ability of the method to retrieve the conductivity values. It is found that not only is it possible to accurately retrieve all six conductivity values, as well as a value for fibre rotation angle, but that the accuracy of such retrievals is comparable to the accuracy found in a previous study when only four conductivities (and fibre rotation) were retrieved.

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“…More recent extensions of these measuring techniques often involve arrays of electrodes, where the design of the array is based on the four electrode technique [17,2,20,21,22,9,13,12,14,6]. An example of one of these designs is an array of plunge electrodes [9,10,7,3] that demonstrated that porcine ventricular tissue is electrically orthotropic, with three distinct propagation directions.…”

Section: Introductionmentioning

confidence: 99%

Design of a multi-electrode array to measure cardiac conductivities

Johnston

2013

ANZIAMJ

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Accurate determination of cardiac tissue parameters is essential in bidomain models that simulate the electrical activity of the heart and thereby contribute to understanding cardiovascular disease. Recent experimental work indicated the need for six parameters, which measure electrical conductivity in two domains (extracellular and intracellular), along and across the cardiac fibres within a sheet and also between sheets. This is in contrast to the available experimentally determined conductivities, which are sets of four values, where it is assumed that conductivities across the fibres within a sheet and between the fibre sheets are equal. This study presents a mathematical model that incorporates six bidomain conductivities. It also discusses the design of a multi-electrode array and inversion method to retrieve these

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Estimating electrical conductivity tensors of biological tissues using microelectrode arrays

Cited by 6 publications

References 6 publications

Data Assimilation in Cardiovascular Fluid–Structure Interaction Problems: An Introduction

Data Assimilation in Cardiovascular Fluid–Structure Interaction Problems: An Introduction

A multi-electrode array and inversion technique for retrieving six conductivities from heart potential measurements

Design of a multi-electrode array to measure cardiac conductivities

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