A biophysical model for cardiac microimpedance measurements

Approaches for determining cardiac bidomain conductivity values: progress and challenges

Modelling the electrical activity of the heart is an important tool for understanding electrical function in various diseases and conduction disorders. Clearly, for model results to be useful, it is necessary to have accurate inputs for the models, in particular the commonly used bidomain model. However, there are only three sets of four experimentally determined conductivity values for cardiac ventricular tissue and these are inconsistent, were measured around 40 years ago, often produce different results in simulations and do not fully represent the three-dimensional anisotropic nature of cardiac tissue. Despite efforts in the intervening years, difficulties associated with making the measurements and also determining the conductivities from the experimental data have not yet been overcome. In this review, we summarise what is known about the conductivity values, as well as progress to date in meeting the challenges associated with both the mathematical modelling and the experimental techniques. Graphical abstract Epicardial potential distributions, arising from a subendocardial ischaemic region, modelled using conductivity data from the indicated studies.

A brief history of tissue models for cardiac electrophysiology

The last four decades have produced a number of significant advances in the developments of computer models to simulate and investigate the electrical activity of cardiac tissue. The tissue descriptions that underlie these simulations have been built from a combination of clever insight and careful comparison with measured data at multiple scales. Tissue models have not only led to greater insights into the mechanisms of life-threatening arrhythmias but have been used to engineer new therapies to treat the consequences of cardiac disease. This paper is a look back at the early years in the cardiac modeling and the challenges facing the field as models move toward the clinic.

Sensor spacing affects the tissue impedance spectra of rabbit ventricular epicardium

This study was designed to test the hypothesis that a complex composite impedance spectra develops when stimulation and recording of cardiac muscle with sufficiently fine spatial resolution in a four-electrode configuration is used. With traditional (millimeter scale) separations, the ratio between the recorded interstitial central potential difference and total supplied interstitial current is constant at all frequencies. This occurs because the fraction of supplied current that redistributes to the intracellular compartment depends on effective membrane resistance between electrodes, which is low, to a much greater extent than effective membrane capacitance. The spectra should therefore change with finer separations at which effective membrane resistance increases, as supplied current will remain primarily interstitial at lower frequencies and redistribute between compartments at higher frequencies. To test this hypothesis, we built arrays with sensors separated (d) by 804 μm, 452 μm, and 252 μm; positioned those arrays across myocyte axes on rabbit ventricular epicardium; and resolved spectra in terms of resistivity (ρt) and reactivity (χt) over the 10 Hz to 4,000 Hz range. With all separations, we measured comparable spectra with predictions from passive membrane simulations that used a three-dimensional structural framework in which intracellular, interstitial, and membrane properties were prescribed based on the limited data available from the literature. At the finest separation, we found mean ρt at 100 Hz and 4,000 Hz that lowered from 395 Ω-cm to 236 Ω-cm, respectively, with maximal mean χt of 160 Ω-cm. This experimental confirmation of spectra development in whole heart experiments is important because such development is central to achieve measurements of intracellular and interstitial passive electrical properties in cardiac electrophysiological experiments using only interstitial access.

A structural framework for interpretation of four-electrode microimpedance spectra in cardiac tissue

Renewed interest in the four-electrode method for identification of passive electrical properties in cardiac tissue has been sparked by a recognition that measurements made with sensors in close proximity are frequency dependent. Therefore, resolution of four-electrode microimpedance spectra (4EMS) may provide an opportunity for routine identification of passive electrical properties for the interstitial and intracellular compartments using only interstitial access. The present study documents a structural framework in which the tissue resistivity (ρt) and reactivity (xt) that comprise spectra are computed using interstitial and intracellular microimpedance distributions that account for differences in compartment size, anisotropic electrical properties in each compartment and electrode separations. We used this framework to consider 4EMS development with relatively wide (d=1 mm) and fine (d=250 μm) electrode separations and sensors oriented along myocyte axes, across myocyte axes and intermediate between those axes.

A new approach for resolution of complex tissue impedance spectra in hearts

This study was designed to test the feasibility of using sinusoidal approximation in combination with a new instrumentation approach to resolve complex impedance (uCI) spectra from heart preparations. To assess that feasibility, we applied stimuli in the 10-4000 Hz range and recorded potential differences (uPDs) in a four-electrode configuration that allowed identification of probe constants (Kp) during calibration that were in turn used to measure total tissue resistivity ρt from rabbit ventricular epicardium. Simultaneous acquisition of a signal proportional to the supplied current (Vstim) with uPD allowed identification of the V- I ratio needed for ρt measurement, as well as the phase shift from Vstim to uPD needed for uCI spectra resolution. Performance with components integrated to reduce noise in cardiac electrophysiologic experiments, in particular, and provide accurate electrometer-based measurements, in general, was first characterized in tests using passive loads. Load tests showed accurate uCI recovery with mean uPD SNRs between 10 (1) and 10 (3) measured with supplied currents as low as 10 nA. Comparable performance characteristics were identified during calibration of nine arrays built with 250 μm Ag/AgCl electrodes, with uCIs that matched analytic predictions and no apparent effect of frequency ( F = 0.12, P = 0.99). The potential ability of parasitic capacitance in the presence of the electrode-electrolyte interface associated with the small sensors to influence the uCI spectra was therefore limited by the instrumentation. Resolution of uCI spectra in rabbit ventricle allowed measurement of ρt = 134 ± 53 Ω· cm. The rapid identification available with this strategy provides an opportunity for new interpretations of the uCI spectra to improve quantification of disease-, region-, tissue-, and species-dependent intercellular uncoupling in hearts.

Uncertainty Visualization in Forward and Inverse Cardiac Models

Quantification and visualization of uncertainty in cardiac forward and inverse problems with complex geometries is subject to various challenges. Specific to visualization is the observation that occlusion and clutter obscure important regions of interest, making visual assessment difficult. In order to overcome these limitations in uncertainty visualization, we have developed and implemented a collection of novel approaches. To highlight the utility of these techniques, we evaluated the uncertainty associated with two examples of modeling myocardial activity. In one case we studied cardiac potentials during the repolarization phase as a function of variability in tissue conductivities of the ischemic heart (forward case). In a second case, we evaluated uncertainty in reconstructed activation times on the epicardium resulting from variation in the control parameter of Tikhonov regularization (inverse case). To overcome difficulties associated with uncertainty visualization, we implemented linked-view windows and interactive animation to the two respective cases. Through dimensionality reduction and superimposed mean and standard deviation measures over time, we were able to display key features in large ensembles of data and highlight regions of interest where larger uncertainties exist.

Bayesian quantitative electrophysiology and its multiple applications in bioengineering

Bayesian interpretation of observations began in the early 1700s, and scientific electrophysiology began in the late 1700s. For two centuries these two fields developed mostly separately. In part that was because quantitative Bayesian interpretation, in principle a powerful method of relating measurements to their underlying sources, often required too many steps to be feasible with hand calculation in real applications. As computer power became widespread in the later 1900s, Bayesian models and interpretation moved rapidly but unevenly from the domain of mathematical statistics into applications. Use of Bayesian models now is growing rapidly in electrophysiology. Bayesian models are well suited to the electrophysiological environment, allowing a direct and natural way to express what is known (and unknown) and to evaluate which one of many alternatives is most likely the source of the observations, and the closely related receiver operating characteristic (ROC) curve is a powerful tool in making decisions. Yet, in general, many people would ask what such models are for, in electrophysiology, and what particular advantages such models provide. So to examine this question in particular, this review identifies a number of electrophysiological papers in bioengineering arising from questions in several organ systems to see where Bayesian electrophysiological models or ROC curves were important to the results that were achieved.