CESLabTechSpecs.htmlTEXTR*chô◊Cµ◊L/µ◊L/ÅÅ| CESLab 3.0 Technical Specifications

CESLab 3.0
Technical Specifications

Mark Stevans
Chieh Chou
CESI
http://www.cesinst.com

September 2, 2000

1. Introduction

This document contains the technical specifications for the CESLab 3.0 cardiac simulation system. It describes the internal components of the system and their interactions, and details the algorithms used in CESLab electrophysiological simulation.

2. Shape Modeling

2.1. Extrusion and Interpolation

The CESLab 3-D shape modeler employs the extrusion paradigm, in which one axis (typically the vertical, Y axis) is distinguished. Any number of 2-D planar slices at differing altitudes along the Y axis may be described explicitly by the user: each is considered an exact representation of the projection of the total 3-D geometry onto that plane. To construct intervening planes along the distinguished axis, the shape modeler interpolates the geometry on the surrounding explicit slices. The collection of these explicit and implicit slices form the totality of the 3-D geometry.

2.2. Geometric Entities

3. Tissue Modeling

3.1. Overview

Subtissues exist to capture commonality between particular contiguous sets of cells. Commonality between subtissues is captured by tissue types.

3.2. Tissue Types

Each tissue type has the following attributes:

3.3. Subtissues

3.3.1. General Subtissue Parameters

All subtissues contain the following data:

3.3.2. Tissue Walls

Tissue walls are a variety of subtissue used to represent biological tissue walls (e.g. cardiac free walls and septi). Each tissue wall has a predetermined "inside" and "outside" surface, which are used to compute a fractional depth to each component cell, which is then used to compute fiber orientations and control intramural repolarization gradients.

In general, tissue walls possess the following characteristics:

3.3.3. Fibers

Simulated fibers are represented by a connected set of cells arranged along a particular CompoundShape curve. The thickness is uniform (currently, always one cell).

3.3.4. Tissue Nodes

Tissue nodes are represented by one or more cells centered around a particular locus.

4. Cell Modeling

4.1. Cell Geometry

In CESLab, the heart is represented by a set of finite elements, called by the term cells for simplicity. Since their size is obviously much larger than that of actual biological cells, these simulated cells effectively represent small cubic groups of biological cells, and must capture their macroscopic behavior rather than the microscopic behavior of individual cells. Microscopic inter/intracellular interactions (such as ionic flow across membrane boundaries) are thus not modeled in CESLab.

4.2. Cell Parameters

Each cell is associated with a particular subtissue, and inherits many of its characteristics from that subtissue. In addition, each simulated cell possesses the following parameters:

5. Torso Modeling

5.1. Overview

In CESLab, the biological torso is modeled by a set of closed polyhedra, each composed of triangular facets. These represent the boundaries between regions of differing (and uniform) conductivity. These regions typically include:

5.2. Polyhedra

Polyhedra acquire their geometry directly from compound shapes and so can be modified by the user via the shape editor. Polyhedra are used to define the body surface, skeletal muscle layer, lungs, and intracavitary masses.

5.3. Facets

Each facet includes the following fields, used for conducting the procedure of Gelernter and Swihart:

5.4 Surface Potential Transfer Coefficient Computation

Surface Potential Transfer Coefficients are computed using the procedure of Gelernter and Swihart. While this approach is less accurate than matrix inversion (which solves the simultaneous equations describing the quasi-static state), the advantages of this method are:

5.5. Electrode Set

To compute twelve-lead ECG's in the standard CESLab torso model, a set of nine electrodes is used, where each acquires its potential from a polyhedral facet of the same name (usually found on the outer torso polyhedron). Facets acquire their names from the associated compound shapes: the facet whose centroid is nearest a given locus acquires the name of the locus.

The electrodes are grouped as follows:

5.6. ECG Lead System

The standard twelve ECG lead potentials are computed from the electrode potentials as follows:

5.7. VCG Lead Systems

Both Frank and McFee lead systems are supported, using the lead potential computation formulae quoted by Mailloux and Gulrajani.

5.7.1. McFee VCG Lead Potentials

For the McFee VCG system, the lead potentials are computed as:

VX = 1/2 (V5 + V6) - V7
VY = V8 - V9
VZ = V4 - 1/3 (V1 + V2 + V3)

5.7.2. Frank VCG Lead Potentials

For the Frank VCG system, the lead potentials are:

VX = 0.610VA + 0.171VC - 0.781VI
VY = 0.655VF + 0.345VM - 1.000VH
VZ = 0.736VM + 0.133VA - 0.264VI -0.374VE - 0.231VC

6. Activation/Recovery Modeling

6.1. Temporal Quantization

Like most finite-element models, CESLab enforces strict quantization of simulated time. Simulated time is quantized at intervals of approximately 15.26 microseconds (2-16 seconds, to be exact), and is not configurable by the user.

The maximal conduction speeds generally found in biological neuromuscular tissue are on the order of 3 m/s. In a high-resolution preparation at 1 mm per cell, this produces a minimal intercell propagation delay of over 20 time quanta, which yields a high degree of temporal resolution even in this worst case scenario.

6.2. Dipole Region Assignment

To more efficiently collect instantaneous current dipoles during electrophysiological simulation, CESLab assigns compact, contiguous groups of simulated cells into a fixed set of regional dipoles. The minimum spacing to be used for the dipole centroids is specified by the user, but the exact number of dipole regions generated and their locations are dependent on the geometry of the simulated subtissues.

6.3. Electrophysiological Connectedness

Electrophysiological connectedness may be defined as the set of rules that establish which cells are considered by be directly connected to a given one for the purposes of electrophysiological simulation, such that myocardial activation may be directly propagated between them. These rules establish what is called a neighborhood for each cell. For a given cell, any other cells located within its neighborhood are called neighbors. Note that, unless a cell happens to be completely inside a subtissue and totally surrounded by other cells, its number of neighbors does not equal the number of neighboring locations in quantized three-space.

In CESLab, the cubic cells are arranged in a 3-D rectangular grid, so each cell has six locations with which it shares a common cubic face (monaxial neighbors), twelve locations with which it shares only an edge (diaxial neighbors), and eight locations with which is shares only a corner vertex (triaxial neighbors).

The neighborhood size for a given preparation is configurable by the user from the following choices:

6.4. Activation Phases and Subphases

Like other finite-element models, CESLab simulates complex macroscopic behaviour by simulating a large collection of simpler elements, here known as cells. Though the macroscopic electrophysiological effects to be simulated are quite sophisticated, the electrophysiological behaviour of any given cell can be made quite straightforward. At any point in time, each cell is considered to be in one of a small, predefined set of simple activation phases.

Each activation phase may contain an arbitrary number of subphases, each of which represents a linear segment of the action potential waveform. By adding enough subphases to the phases above, waveforms of any degree of complexity can be modelled as closely as desired.

6.4.1. Phase 4 (Resting)

At the start of a trial, all simulated cells are in the resting activation phase.

Cells remain in the resting state until they are activated by a neighbor (or, for automatic cells, their automatic period elapses).

6.4.2. Phase 0 (Action Potential Upstroke)

Upon activation (either due to inherent automaticity or activation of a suitable neighbor), cells move from activation phase 3 RRP or 4 to phase 0. The square root of the upstroke potential slew rate over phase 0 is multiplied into the conduction speed factors of associated subtissues to yield conduction speeds.

Cells remain in phase 0 for a fixed time period, and then enter phase 1.

6.4.3. Phase 1 (Action Potential Downstroke to Plateau)

Phase 1 is typically of short duration, and represents the transition to the phase 2 plateau potential.

Cells remain in phase 1 for a fixed time period, after which they enter phase 0.

6.4.4. Phase 2 (Action Potential Plateau)

Cells remain in phase 2 for a temporal duration dependent on the base action potential waveform specified for the tissue type, multiplied by any prevailing interval/duration factors, and intramural or vertical phase 2 duration gradients, and then transition to phase 3 ARP.

6.4.5. Phase 3 ARP (Absolute Refractory Period)

Cells in phase 3 ARP are not yet capable of reactivation, though they are capable of activating neighboring cells if the neighbor's excitation threshold is low enough.

Cells remain in phase 3 ARP for a time duration dependent on the base action potential waveform specified for the tissue type, multiplied by any prevailing interval/duration factors, after which they enter phase 3 RRP.

6.4.6. Phase 3 (Relative Refractory Period)

Cells in phase 3 RRP can be activated by neighboring cells, if the neighbor's stimulus exceeds our excitation threshold. Note that the the elevation of the excitation threshold during phase 3 RRP is not directly modelled by CESLab. Slow conduction caused by such elevation is modelled via the decremental conduction curves; complete loss of conduction due strictly to elevation of excitation threshold during phase 3 RRP is assumed to be negligible.

Cells remain in phase 3 RRP until reactivated, or until a specific time duration elapses (the phase 3 RRP duration from the action potential waveform specified for the associated tissue type, multiplied by the interval/duration factor), after which they reenter phase 4.

6.5. Propagation of Excitation

When a cell is activated, CESLab examines each neighboring cell to determine if activation should be propagated to it. To be activated, the neighboring cell must be excitable, and possess an excitation threshold that is surpassed by the action potential waveform of the cell being activated.

The delay to be employed before propagation of activation to a neighboring cell is dependent upon the following:

For some small number of cells in subtissues that have oriented fibers, a fiber orientation may not be computable (e.g. at the extreme apex of the heart); for such cells, the isotropic conduction speed that would activate a sphere of volume equal to that of the ellipsoid activated by the specified axial and transverse conduction speeds is employed (equal to the cube root of the axial conduction speed times the square of the transverse).

6.6. Dipole Source Generation

During electrophysiological simulation, dipoles are generated at the interfaces between cells, and collected in the appropriate regional dipole. At fixed simulated time intervals (dipole sampling intervals), the accumulated dipole vector is presented to all extant instruments, and then cleared.

6.7. Cycle Length Dependency Modelling

In biological preparations, certain observable characteristics (particularly the action potential waveform and the conduction speed) produced by the activation of a given neuron are dependent to some degree upon the recent activation history of the cell, especially the temporal interval between the activation and the one immediately preceding it. In CESLab, this is modelled by three types of user- definable curves: interval/duration, interval/potential, and decremental conduction.

For each of the three modelled cycle-length effects, the user may specify a single non-tissue type-specific curve and/or tissue type- specific curves for any/all tissue types. For activation of a given cell, if a tissue type-specific curve exists for the tissue type associated with the cell, it is employed; otherwise, the non-tissue type-specific curve is employed.

Each curve yields a scalar factor that is multiplied into the conduction speed, action potential (relative to the resting potential), or action duration to express the desired effect.

6.7.1. Interval/Duration Curves

An interval/duration curve maps the interactivation interval to a numeric factor that is used to linearly rescale the temporal durations of phases 2 and 3 of the action potential waveform. The durations of phases 0 and 1 are not affected by this curve. This curve thus affects the instantaneous dipole generated by the cell, as well as its ability to excite other cells. For the first activation of a given cell within a trial, the default interactivation interval for the preparation is used to look up the appropriate points on the relevant curves, but any succeeding activations within the trial will use the actual time elapsed between the activation and the one immediately preceding it.

6.7.2. Interval/Potential Curves

An interval/potential curve maps the interactivation interval to a numeric factor that is applied to the entire action potential waveform to linearly rescale the potentials with respect to the resting potential. This curve affects the dipole generated by the cell, as well as its ability to excite other cells.

For the first activation of a given cell within a trial, the default interactivation interval for the preparation is used to look up the appropriate points on the relevant curves, but any succeeding activations within the trial will use the actual time elapsed between the activation and the one immediately preceding it.

6.7.3. Decremental Conduction Curves

A decremental conduction curve maps the temporal duration between the last entry of a cell into phase 3 RRP and the current time to a scalar factor that is multiplied into the conduction speed of the cell in order to adjust the speed at which excitation is propagated to neighboring cells. The action potential waveform of the cell is not altered.

For the first activation of a given cell within a trial, a decremental conduction factor of unity is always employed.

7. Electropharmacological Modelling

7.1. Modulator Effect Modalities

In CESLab, the effects of Modulators (drugs or electrolytes) are modelled by specifying a set of curves, each of which maps a given level of a simulated pharmacological agent to a linear rescaling factor. The rescaling factor is applied in one of a fixed set of Modulator Effect Modalities, to adjust a particular characteristic of the preparation. Any given simulated pharmacological agent can operate in any or all of these modalities:

7.2. Tissue Type Specificity of Modulator Effects

For any given Modulator and relevant Effect Modality, the user may specify a non-tissue type-specific curve which affects the entire preparation and/or any number of tissue type-specific curves, which affect only subtissues associated with the tissue type. Where a tissue type-specific curve exists for a given Modulator and Effect Modality, it takes precedence over the non-tissue type-specific curve.

7.2. Simultaneous Simulation of Multiple Modulators

If more than one Modulator is employed simultaneously, their scaling factors are multiplied together to achieve the net scaling factor. This assumes that the Modulators operate independently; more complex interactions between multiple agents must be modelled by defining Modulators that each represent a mixture of agents in given ratios, e.g. two parts agent A to one part agent B.

8. Arithmetic Precision

Within CESLab, for maximum accuracy, all floating-point computations are conducted using 64-bit double-precision arithmetic. All floating-point quantities are stored in 64-bit double-precision variables except the following, which are stored in 32-bit single-precision variable for compactness:

9. References

Adam D (1991). Propagation of depolarization processes in the myocardium: An anisotropic model. IEEE Trans Biomed Eng 38: 133- 41.

Aoki M, Okamoto Y, Musha T, Harumi KI (1987). Three-dimensional simulation of the ventricular depolarization and repolarization process and body surface potentials. Normal heart and bundle branch block. IEEE Trans Biomed Eng 36: 454-62.

Boineau JP, Schuessler RB, Mooney CR, Wylds AC, Miller CB, Hudson RD, Borremans JM, Brockus CW (1978). Multicentric origin of the atrial depolarization wave: The pacemaker complex. Relation to dynamics of atrial conduction, P-wave changes and heart rate control. Circulation 58(6): 1036-48.

Corbin LV II, Scher AM (1977). The canine heart as an electrocardiographic generator. Dependence on cardiac cell orientation. Circ Res 41(1): 58-67.

Durrer D, van Dam RT, Freud GE, Janse MJ, Miejler FL, Arzbaecher RC (1970). Total excitation of the isolated human heart. Circulation 41: 899-912.

Fujino T (1968). On genesis of RBBB pattern in electro- and vectorcardiogram as studied by simulation of ventricular propagation process and reconstruction of QRS patterns. Jap Circ J 32: 1533-41.

Gelernter HL, Swihart JC (1964). A mathematical-physical model of the genesis of the electrocardiogram. Biophys J 4: 285-301.

Goodman D, van der Steen ABM, van Dam RT (1971). Endocardial and epicardial activation pathways of the canine right atrium. Am J Physiol 220(1): 1-11.

Gulrajani RM, Mailloux GE (1983). A simulation study of the effects of torso inhomogeneities on electrocardiographic potentials, using realistic heart and torso models. Circ Res 52: 45-56.

Gulrajani RM, Pham-Huy H, Nadeau RA, Savard P, de Guise J, Primeau RE, Roberge FA (1984). Application of the single moving dipole inverse solution to the study of the Wolff-Parkinson-White syndrome in man. J Electrocardiol 17(3): 271-88.

Gulrajani RM (1988). Models of the electrical activity of the heart and computer simulation of the electrocardiogram. CRC Crit Rev Biomed Eng 16(1): 1-66.

Hunter PJ, McNaughton PA, Noble D (1975). Analytical models of propagation in excitable cells. Prog Biophys Mol Bio 30:99-144.

Killman R, Wach P, Dienstl F (1991). Three-dimensional computer model of the entire human heart for simulation of reentry and tachycardia: Gap phenomenon and Wolff-Parkinson-White syndrome. Bas Res Cardiol 86: 485-501.

Leon LJ, Horacek BM (1991). Computer model of excitation and recovery in the anisotropic myocardium. J Electrocardiol 24: 1-41.

Lorange M, Gulrajani R (1993). A computer heart model incorporating anisotropic propagation. J Electrocardiol 26(4): 245- 77.

Mailloux GE, Gulrajani RM (1982). Theoretical evaluation of the McFee and Frank vectorcardiographic lead systems using a numerical inhomogeneous torso model. IEEE Trans Biomed Eng 29: 322-32.

Massing GK, James TN (1976). Anatomical configuration of the His bundle and bundle branches in the human heart. Circulation 53(4): 609-21.

McFee R, Rush S (1968). Qualitative effects of thoracic resistivity variations on the interpretation of electrocardiograms: the low resistance surface layer. Am Heart J 76: 48-61.

Miller WT, Geselowitz DB (1978). Simulation studies of the electrocardiogram. Circ Res 43: 301-23.

Okajima M, Fujino T, Kobayashi T, Yamada K (1968). Computer simulation of the propagation process in excitation of the ventricles. Circ Res 23: 203-11.

Pilkington TC, Plonsey R (1982). Engineering Contributions to Biophysical Electrocardiography. IEEE Press, New York.

Plonsey R, Barr RC (1988). Bioelectricity: a Quantitative Approach. Plenum Press, New York.

Roberts DE, Hersh LT, Scher AM (1979). Influence of cardiac fiber orientation on wavefront voltage, conduction velocity, and tissue resistivity in the dog. Circ Res 44: 701-12.

Streeter DD Jr, Spotnitz HM, Patel DP, Ross J Jr., Sonnenblick EH (1969). Fiber orientation in the canine left ventricle during diastole and systole. Circ Res 24: 339-47. 2 Mark Stevans2;}¶\N2STR ø„ˇˇ;}¶|