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.

[1]  P. Savard,et al.  Extracellular Measurement of Anisotropic Bidomain Myocardial Conductivities. I. Theoretical Analysis , 2001, Annals of Biomedical Engineering.

[2]  A. van Oosterom,et al.  Intramural resistivity of cardiac tissue , 2006, Medical and Biological Engineering and Computing.

[3]  Barbara M. Johnston,et al.  Six Conductivity Values to Use in the Bidomain Model of Cardiac Tissue , 2016, IEEE Transactions on Biomedical Engineering.

[4]  A. M. Scher,et al.  Influence of Cardiac Fiber Orientation on Wavefront Voltage, Conduction Velocity, and Tissue Resistivity in the Dog , 1979, Circulation research.

[5]  R. Barr,et al.  Feasibility of cardiac microimpedance measurement using multisite interstitial stimulation. , 2004, American journal of physiology. Heart and circulatory physiology.

[6]  Barbara M. Johnston,et al.  Analysis of Electrode Configurations for Measuring Cardiac Tissue Conductivities and Fibre Rotation , 2006, Annals of Biomedical Engineering.

[7]  S. Luther,et al.  Modelling far field pacing for terminating spiral waves pinned to ischaemic heterogeneities in cardiac tissue , 2017, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  P. Savard,et al.  Electrical alignment of a cardiac impedance probe , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[9]  Peter R Johnston,et al.  A sensitivity study of conductivity values in the passive bidomain equation. , 2011, Mathematical biosciences.

[10]  B. Taccardi,et al.  Spread of Excitation in a Myocardial Volume: , 1993, Journal of cardiovascular electrophysiology.

[11]  Barbara M. Johnston,et al.  Determining six cardiac conductivities from realistically large datasets. , 2015, Mathematical biosciences.

[12]  Wanda Krassowska,et al.  Theoretical versus experimental estimates of the effective conductivities of cardiac muscle , 1992, Proceedings Computers in Cardiology.

[13]  Naum Zuselevich Shor,et al.  Minimization Methods for Non-Differentiable Functions , 1985, Springer Series in Computational Mathematics.

[14]  P. Steendijk,et al.  The four-electrode resistivity technique in anisotropic media: theoretical analysis and application on myocardial tissue in vivo , 1993, IEEE Transactions on Biomedical Engineering.

[15]  K. L. Boon,et al.  Electrical conductivity of skeletal muscle tissue: Experimental results from different musclesin vivo , 1984, Medical and Biological Engineering and Computing.

[16]  Roger C Barr,et al.  A biophysical model for cardiac microimpedance measurements. , 2010, American journal of physiology. Heart and circulatory physiology.

[17]  A. Moreno,et al.  A Model of Electrical Conduction in Cardiac Tissue Including Fibroblasts , 2009, Annals of Biomedical Engineering.

[18]  Yves Coudière,et al.  The modified bidomain model with periodic diffusive inclusions , 2014, Computing in Cardiology 2014.

[19]  Peter R. Johnston,et al.  The effect of conductivity values on ST segment shift in subendocardial ischaemia , 2003, IEEE Transactions on Biomedical Engineering.

[20]  Karl A. Tomlinson,et al.  Cardiac Microstructure: Implications for Electrical Propagation and Defibrillation in the Heart , 2002, Circulation research.

[21]  Peter R. Johnston,et al.  The importance of anisotropy in modeling ST segment shift in subendocardial ischaemia , 2001, IEEE Transactions on Biomedical Engineering.

[22]  Barbara M. Johnston,et al.  A multi-electrode array and inversion technique for retrieving six conductivities from heart potential measurements , 2013, Medical & Biological Engineering & Computing.

[23]  R. Coronel,et al.  The effect of lesion size and tissue remodeling on ST deviation in partial-thickness ischemia. , 2007, Heart rhythm.

[24]  B Sanchez,et al.  New electrical impedance methods for the in situ measurement of the complex permittivity of anisotropic biological tissues. , 2017, Physics in medicine and biology.

[25]  Barbara M. Johnston,et al.  A new approach to the determination of cardiac potential distributions: application to the analysis of electrode configurations. , 2006, Mathematical biosciences.

[26]  P. Johnston A nondimensional formulation of the passive bidomain equation. , 2011, Journal of electrocardiology.

[27]  W. Cascio,et al.  The Ib phase of ventricular arrhythmias in ischemic in situ porcine heart is related to changes in cell-to-cell electrical coupling. Experimental Cardiology Group, University of North Carolina. , 1995, Circulation.

[28]  William E Louch,et al.  Species-Dependent Mechanisms of Cardiac Arrhythmia: A Cellular Focus , 2017, Clinical Medicine Insights. Cardiology.

[29]  Bruce H Smaill,et al.  Laminar Arrangement of Ventricular Myocytes Influences Electrical Behavior of the Heart , 2007, Circulation research.

[30]  P. Johnston,et al.  The effect of ischaemic region shape on st potentials using a half-ellipsoid model of the left ventricle , 2012, 2012 Computing in Cardiology.

[31]  Joakim Sundnes,et al.  Simulation of ST segment changes during subendocardial ischemia using a realistic 3-D cardiac geometry , 2005, IEEE Transactions on Biomedical Engineering.

[32]  Peter R Johnston,et al.  A finite volume method solution for the bidomain equations and their application to modelling cardiac ischaemia , 2010, Computer methods in biomechanics and biomedical engineering.

[33]  P. Hunter,et al.  Laminar structure of the heart: a mathematical model. , 1997, The American journal of physiology.

[34]  Alessandro Veneziani,et al.  Efficient Estimation of Cardiac Conductivities via POD-DEIM Model Order Reduction , 2016, 1603.05722.

[35]  A. Kleber,et al.  Animal models of cardiac arrhythmias. , 1998, Cardiovascular research.

[36]  B. Roth Nonsustained Reentry Following Successive Stimulation of Cardiac Tissue Through a Unipolar Electrode , 1997, Journal of cardiovascular electrophysiology.

[37]  Barbara M. Johnston,et al.  Design of a multi-electrode array to measure cardiac conductivities , 2013 .

[38]  H Zhang,et al.  Models of cardiac tissue electrophysiology: progress, challenges and open questions. , 2011, Progress in biophysics and molecular biology.

[39]  S. Rush,et al.  Resistivity of Body Tissues at Low Frequencies , 1963, Circulation research.

[40]  R. Barr,et al.  Sensor spacing affects the tissue impedance spectra of rabbit ventricular epicardium. , 2014, American journal of physiology. Heart and circulatory physiology.

[41]  Barbara M. Johnston,et al.  Sensitivity analysis of ST-segment epicardial potentials arising from changes in ischaemic region conductivities in early and late stage ischaemia , 2018, Comput. Biol. Medicine.

[42]  P. Johnston The effect of simplifying assumptions in the bidomain model of cardiac tissue: application to ST segment shifts during partial ischaemia. , 2005, Mathematical biosciences.

[43]  Darren A Hooks,et al.  Myocardial segment-specific model generation for simulating the electrical action of the heart , 2007, Biomedical engineering online.

[44]  K. Meijer,et al.  Attractive Gait Training: Applying Dynamical Systems Theory to the Improvement of Locomotor Performance Across the Lifespan , 2019, Front. Physiol..

[45]  R. M. Arthur,et al.  Effect of inhomogeneities on the apparent location and magnitude of a cardiac current dipole source. , 1970, IEEE transactions on bio-medical engineering.

[46]  Barbara M. Johnston,et al.  The Sensitivity of the Passive Bidomain Equation to Variations in Six Conductivity Values , 2013, BioMed 2013.

[47]  David Kilpatrick,et al.  Estimation of the Bidomain Conductivity Parameters of Cardiac Tissue From Extracellular Potential Distributions Initiated by Point Stimulation , 2010, Annals of Biomedical Engineering.

[48]  K. Foster,et al.  Dielectric properties of tissues and biological materials: a critical review. , 1989, Critical reviews in biomedical engineering.

[49]  R. Gulrajani Bioelectricity and biomagnetism , 1998 .

[50]  Olaf Dössel,et al.  Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs , 2010, IEEE Transactions on Biomedical Engineering.

[51]  B Sanchez,et al.  New electrical impedance methods for the in situ measurement of the complex permittivity of anisotropic skeletal muscle using multipolar needles , 2019, Scientific Reports.

[52]  Caroline Mendonça Costa,et al.  Automatic parameterization strategy for cardiac electrophysiology simulations , 2013, Computing in Cardiology 2013.

[53]  Felipe Aguel,et al.  Computer simulations of cardiac defibrillation: a look inside the heart , 2002 .

[54]  Roger C. Barr,et al.  A New Approach for Resolution of Complex Tissue Impedance Spectra in Hearts , 2013, IEEE Transactions on Biomedical Engineering.

[55]  Jeroen G Stinstra,et al.  Mechanism for ST Depression Associated with Contiguous Subendocardial Ischemia , 2004, Journal of cardiovascular electrophysiology.

[56]  Roger C Barr,et al.  Cardiac microimpedance measurement in two-dimensional models using multisite interstitial stimulation. , 2006, American journal of physiology. Heart and circulatory physiology.

[57]  A Study of the Dynamics of Cardiac Ischemia using Experimental and Modeling Approaches , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[58]  J. Stinstra,et al.  The Effect of Conductivity on ST-Segment Epicardial Potentials Arising from Subendocardial Ischemia , 2005, Annals of Biomedical Engineering.

[59]  Barbara M. Johnston,et al.  Using a sensitivity study to facilitate the design of a multi-electrode array to measure six cardiac conductivity values. , 2013, Mathematical biosciences.

[60]  F. J. Claydon,et al.  Membrane polarization induced in the myocardium by defibrillation fields: an idealized 3-D finite element bidomain/monodomain torso model , 1999, IEEE Transactions on Biomedical Engineering.

[61]  Pierre Savard,et al.  Measurement of myocardial conductivities with an eight-electrode technique in the frequency domain , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[62]  Barbara M. Johnston,et al.  Quantifying the effect of uncertainty in input parameters in a simplified bidomain model of partial thickness ischaemia , 2017, Medical & Biological Engineering & Computing.

[63]  F. Sachse,et al.  Confocal Microscopy-Based Estimation of Parameters for Computational Modeling of Electrical Conduction in the Normal and Infarcted Heart , 2018, Front. Physiol..

[64]  Peter R. Johnston,et al.  Cardiac conductivity values — A challenge for experimentalists? , 2011, 2011 8th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the 2011 8th International Conference on Bioelectromagnetism.

[65]  Barbara M. Johnston,et al.  Exploiting GPUs to investigate an inversion method that retrieves cardiac conductivities from potential measurements , 2014 .

[66]  Enrico G. Caiani,et al.  Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients , 2017, Medical & Biological Engineering & Computing.

[67]  R. Cardinal,et al.  ST elevation or depression in subendocardial ischemia? , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[68]  Robert Plonsey,et al.  The Four-Electrode Resistivity Technique as Applied to Cardiac Muscle , 1982, IEEE Transactions on Biomedical Engineering.

[69]  A. M. Scher,et al.  Effect of Tissue Anisotropy on Extracellular Potential Fields in Canine Myocardium in Situ , 1982, Circulation research.

[70]  B. Taccardi,et al.  Modeling ventricular excitation: axial and orthotropic anisotropy effects on wavefronts and potentials. , 2004, Mathematical biosciences.

[71]  Hervé Delingette,et al.  Human Atlas of the Cardiac Fiber Architecture: Study on a Healthy Population , 2012, IEEE Transactions on Medical Imaging.

[72]  F. Fenton,et al.  Numerical sensitivity analysis of a variational data assimilation procedure for cardiac conductivities. , 2017, Chaos.

[73]  J. Stinstra,et al.  On the Passive Cardiac Conductivity , 2005, Annals of Biomedical Engineering.

[74]  F B Sachse,et al.  Estimating Intracellular Conductivity Tensors from Confocal Microscopy of Rabbit Ventricular Tissue , 2013, Biomedizinische Technik. Biomedical engineering.

[75]  Barbara M. Johnston,et al.  The effect of conductivity values on activation times and defibrillation thresholds , 2016, 2016 Computing in Cardiology Conference (CinC).

[76]  Tanmay A. Gokhale,et al.  Modeling dynamics in diseased cardiac tissue: Impact of model choice. , 2017, Chaos.

[77]  Bruce H Smaill,et al.  Three Distinct Directions of Intramural Activation Reveal Nonuniform Side-to-Side Electrical Coupling of Ventricular Myocytes , 2009, Circulation. Arrhythmia and electrophysiology.

[78]  Piero Colli Franzone,et al.  DYNAMICAL EFFECTS OF MYOCARDIAL ISCHEMIA IN ANISOTROPIC CARDIAC MODELS IN THREE DIMENSIONS , 2007 .

[79]  Gunnar Seemann,et al.  Quantitative Analysis of Cardiac Tissue Including Fibroblasts Using Three-Dimensional Confocal Microscopy and Image Reconstruction: Towards a Basis for Electrophysiological Modeling , 2013, IEEE Transactions on Medical Imaging.

[80]  Alessio Gizzi,et al.  Experimental validation of a variational data assimilation procedure for estimating space-dependent cardiac conductivities , 2020 .

[81]  J. Ross,et al.  Fiber Orientation in the Canine Left Ventricle during Diastole and Systole , 1969, Circulation research.

[82]  C. Henriquez,et al.  Cardiac propagation simulation. , 1992, Critical reviews in biomedical engineering.

[83]  Transmyocardial ST potential distributions in ischaemic heart disease , 2005 .

[84]  L. Clerc Directional differences of impulse spread in trabecular muscle from mammalian heart. , 1976, The Journal of physiology.

[85]  P. C. Franzone,et al.  Spreading of excitation in 3-D models of the anisotropic cardiac tissue. I. Validation of the eikonal model. , 1993, Mathematical biosciences.

[86]  John A. Board,et al.  A modular simulation system for the bidomain equations , 1999 .

[87]  Barbara M. Johnston,et al.  Differences between models of partial thickness and subendocardial ischaemia in terms of sensitivity analyses of ST-segment epicardial potential distributions. , 2019, Mathematical biosciences.

[88]  A. Kléber,et al.  Electrical constants of arterially perfused rabbit papillary muscle. , 1987, The Journal of physiology.

[89]  Craig S. Henriquez,et al.  A Brief History of Tissue Models For Cardiac Electrophysiology , 2014, IEEE Transactions on Biomedical Engineering.

[90]  Boyce E. Griffith,et al.  Deriving Macroscopic Myocardial Conductivities by Homogenization of Microscopic Models , 2009, Bulletin of mathematical biology.

[91]  William M. Smith,et al.  Linear Electrode Arrays for Stimulation and Recording Within Cardiac Tissue Space Constants , 2008, IEEE Transactions on Biomedical Engineering.

[92]  C. Henriquez,et al.  Estimation of Cardiac Bidomain Parameters from Extracellular Measurement: Two Dimensional Study , 2006, Annals of Biomedical Engineering.

[93]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[94]  A. Veneziani,et al.  Estimation of cardiac conductivities in ventricular tissue by a variational approach , 2015 .

[95]  S. Weidmann Electrical constants of trabecular muscle from mammalian heart , 1970, The Journal of physiology.

[96]  Gregory B. Sands,et al.  Experiment-specific models of ventricular electrical activation: Construction and application , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[97]  P. Hunter,et al.  Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. , 1995, The American journal of physiology.

[98]  Andrew J. Pullan,et al.  Solving the cardiac bidomain equations for discontinuous conductivities , 2006, IEEE Transactions on Biomedical Engineering.

[99]  Barbara M. Johnston,et al.  Determining the most significant input parameters in models of subendocardial ischaemia and their effect on ST segment epicardial potential distributions , 2018, Comput. Biol. Medicine.

[100]  Bradley J. Roth,et al.  The electrical potential produced by a strand of cardiac muscle: A bidomain analysis , 2006, Annals of Biomedical Engineering.

[101]  Robert Plonsey,et al.  Electrode systems for measuring cardiac impedances using optical transmembrane potential sensors and interstitial electrodes-theoretical design , 2003, IEEE Transactions on Biomedical Engineering.

[102]  B. Roth Electrical conductivity values used with the bidomain model of cardiac tissue , 1997, IEEE Transactions on Biomedical Engineering.

[103]  J. Papp,et al.  Ionic currents and action potentials in rabbit, rat, and guinea pig ventricular myocytes , 1993, Basic Research in Cardiology.

[104]  The effect of ischaemic region shape on epicardial potential distributions in transient models of cardiac tissue , 2012 .

[105]  Akil Narayan,et al.  A Comparison of Methods for Examining the Effect of Uncertainty in the Conductivities in a Model of Partial Thickness Ischaemia , 2019, 2019 Computing in Cardiology (CinC).

[106]  Roger C. Barr,et al.  A structural framework for interpretation of four-electrode microimpedance spectra in cardiac tissue , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.