Modelling passive cardiac conductivity during ischaemia

Stinstra, J.G.; Shome, Shibaji; Hopenfeld, Bruce; MacLeod, Robert S.

doi:10.1007/bf02430957

Cited by 34 publications

(41 citation statements)

References 26 publications

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“…The geometrical model that underlies the simulation was based on previous work that focused on computing passive electrical characteristics of myocardial tissue such as the conductivity tensor for a bidomain [2,6]. We created a computer algorithm for filling up a space with realistically shaped myocytes that fit together like pieces of a jigsaw puzzle of which the average length and cross section could be altered to fit histological data.…”

Section: Methodsmentioning

confidence: 99%

“…The latter could, for instance, reflect the collapse of the interstitial space during the onset of acute ischemia [2]. The extracellular space was carved out on the lateral sides of the myocytes (perpendicular to the fiber direction) and the amount of extracellular space was regulated by altering the thickness of the sheet of interstitial space that surrounds the myocytes.…”

Section: Methodsmentioning

confidence: 99%

“…One of the reasons for including more details into the model is to be able to simulate pathologies like ischemia based on their physiological origins rather than by assigning parameters to fit experiments. For instance, during an episode of]ischemia the amount of extracellular space changes with time [2] altering the way a depolarization front propagates along the fiber [3].…”

Section: Introductionmentioning

confidence: 99%

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A model for estimating the anisotropy of the conduction velocity in cardiac tissue based on the tissue morphology

Stinstra

Poelzing

MacLeod

et al. 2007

2007 Computers in Cardiology

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IntroductionIn the quest to better understand the mechanisms underlying cardiac arrhythmias, the use of computer simulations is a common tool. Computer simulations have the advantage of being able to resolve potentials and currents with a finer level of detail than is possible with measurements. However, in order to be useful, simulations need to reproduce observations, in this case from electrophysiology, and hence they must produce results against which to compare with experimental findings.The most common approach to the simulation of the propagation of depolarization fronts inside the myocardium is the use of the "bidomain model" [1]. The bidomain model is based on the notion that the myocardium can be separated into intracellular and extracellular spaces, which are joined to each other by a membrane that acts both as a current source and a pathway allowing current to flow between both spaces. More specifically, the bidomain model assumes that the intracellular space, the extracellular space and the membrane all coexist at each point in space. Hence at each location the myocardium can be characterized by a conductivity tensor for the intracellular space, a conductivity tensor for the extracellular space, and an ionic model that describes the current flowing through the membrane.The two conductivity tensors describing the electrical properties of the tissue represent the homogenized conductivity of either the intracellular or the extracellular space. The most common way of estimating these parameters is to choose them so that simulated cardiac propagation speeds fit experimentally observed values. Although this is a valid way of setting these parameters, it fails to relate them to the underlying tissue structure and composition.In this paper, we describe a model that simulates cardiac propagation based on a more detailed description of the microscopic tissue morphology than that provided by the standard bidomain. One of the reasons for including more details into the model is to be able to simulate pathologies like ischemia based on their physiological origins rather than by assigning parameters to fit experiments. For instance, during an episode of]ischemia the amount of extracellular space changes with time [2] altering the way a depolarization front propagates along the fiber [3].In order to create such a model, we built on the models of Spach et al. [4], who created a 2D model of cardiac tissue that consisted of several hundred realistically shaped myocytes that were coupled by gap junctions. Their model was limited by the fact that it did not include an extracellular space. Two-dimensional models are intrinsically limited because the intracellular and extracellular current paths cannot cross each other, thereby limiting the amount of available extracellular and intracellular pathways.To avoid these limitations of a 2D model, we created a full 3D model of cardiac tissue that included realistically shaped myocytes coupled by gap junctions and embedded in an extracellular matrix. We present here pr...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A model for estimating the anisotropy of the conduction velocity in cardiac tissue based on the tissue morphology

Stinstra

Poelzing

MacLeod

et al. 2007

2007 Computers in Cardiology

View full text Add to dashboard Cite

show abstract

“…Furthermore, recent research supports the notion that small blood vessels are relatively nonconducting (Stinstra et. al.,[14]) in comparison with interstitial fluids. They attribute low conductivity to the insulating nature of the capillary wall and the tendency for non-conductive blood cells to greatly increase the path length for charge carriers.…”

Section: Relation Of Eddy Current Measurements To Tissue Microstructurementioning

confidence: 99%

Auto-Tuned Induction Coil Conductivity Sensor for In-Vivo Human Tissue Measurements

Heller¹,

Feldkamp²

2009

Measurement Science Review

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Auto-tuned induction coil technology, based upon phase-locked loop circuitry (PLL), was developed and shown to be an effective tool for in-vivo measurement of electrical conductivity of human tissues. Because electrical contact is not required, several disadvantages of the electrode method for conductivity determination are avoided, such as electrode polarization and variable conductivity associated with the stratum corneum of the epidermis. Fixed frequency excitation is supplied to a parallel tuned RLC circuit, or "sensor", while bias applied to a varactor diode is automatically adjusted via PLL circuitry to maintain the RLC sensor at resonance. Since resonant impedance of a coil positioned near a conductive object is known to be frequency dependent, such an arrangement permits precise calibration of the sensor against a set of standard Potassium Chloride solutions. In our experiments, a two-layer spiral coil is used with upper and lower spiral arms staggered so as to reduce inter-winding coil capacitance. Preliminary in-vivo testing was done on the forearms of a single male subject as a prelude to more extensive use in a clinical setting. In that instance, electrical conductivity at the proximal volar forearm location was shown to depend on forearm elevation. Clinical studies using our prototype, as well as further consideration of the "elevation effect", are discussed in a companion paper.

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“…In order to provide a framework for our interpretation of electrical conductivity values, we have proposed using a simple human tissue model -a porous medium consisting of two phases. One of these phases consists of a relatively non-conducting microvasculature [4] that includes arterioles, capillaries, venules and lymph vessels, while the second phase consists of highly conductive interstitial fluids. Redistribution of fluids between the two phases, especially due to elevation changes in extremities, is expected to cause measurable changes in electrical conductivity.…”

Section: Introductionmentioning

confidence: 99%

Effects of Extremity Elevation and Health Factors On Soft Tissue Electrical Conductivity

Feldkamp¹,

Heller²

2009

Measurement Science Review

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Two clinical studies were completed using an auto-tuned induction coil conductivity sensor (ICCS) to determine the effects of a variety of factors on the electrical conductivity of soft tissue. In addition to fifteen "subject variables" such as blood pressure and others, we have specifically focused on considering the role of such factors as gender, age, BMI, smoking and elevation of extremities. Measurements were made at seven sites on either side of the body for a total of fourteen. Higher conductivities were obtained for women than men at all sites. At five sites, where age was a significant factor, conductivity was found to decline with increased age. Interestingly, smokers as a group tended to have reduced conductivity, suggesting that aging and smoking have similar effects on the microvasculature of soft tissue. Generally speaking, electrical conductivity is observed to increase in response to increased elevation at sites located on extremities. Considering just healthy adults, a definite pattern of elevation-induced electrical conductivity displacement emerges when subjects are flagged according to high, low or moderate blood pressure. We suggest that violations of this pattern may provide a method for identifying those individuals in an early stage of peripheral vascular disease.

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Modelling passive cardiac conductivity during ischaemia

Cited by 34 publications

References 26 publications

A model for estimating the anisotropy of the conduction velocity in cardiac tissue based on the tissue morphology

A model for estimating the anisotropy of the conduction velocity in cardiac tissue based on the tissue morphology

Auto-Tuned Induction Coil Conductivity Sensor for In-Vivo Human Tissue Measurements

Effects of Extremity Elevation and Health Factors On Soft Tissue Electrical Conductivity

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