Ambiguities of epicardial mapping

Ambiguities of epicardial mapping

Ambiguities of Epicardial Mapping Edward J. Berbari, PhD,* Paul Lander, PhD*, Benjamin J. Scherlag, PhD,* Ralph Lazzara, MD,* and David B. Geselowitz...

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Ambiguities of Epicardial Mapping

Edward J. Berbari, PhD,* Paul Lander, PhD*, Benjamin J. Scherlag, PhD,* Ralph Lazzara, MD,* and David B. Geselowitz, PhDt

Two dimensional maps of cardiac activation within and surrounding infarct regions of the canine heart have been instrumental in understanding the mechanisms of ventricular tachycardia.':" Some controversies have arisen in characterizing these activation maps as either depicting very slow conduction velocities" or conduction block. I Of immediate concern in resolving such controversies is evaluation of the methodologies used to generate these activation maps and their practical limitations. There are two steps in generating an activation map: (I) identifying a set of activation times from each electrode and (2) interpolating between sample points to create the isochronous lines. Epicardial electrograms recorded from the thin border zone of surviving tissue overlying an infarct in the canine ventricles are of particular interest. In interpreting epicardial electrograms with unipolar electrodes it is assumed that each electrode overlies viable tissue. The most common criterion for activation time at an electrode site is the instant of largest negative derivative in the waveform. The determination of activation time in the region overlying an infarct is complicated because surviving cells in this region do not have a uniform conduction velocity (both speed and direction) and the electrogram often appears "fractionated" with multiple deflections.":" Fractionation can occur when multiple sites of entry exist in the thin border zone of surviving tissue or in the presence of patchy necrosis. Model studies show that such fractionation can also occur when there are abrupt changes in conduction ve-

locity. in which case neither amplitude nor derivative criteria are adequate for identifying a distinct activation time." In addition, it is possible that some electrodes are over nonviable tissue leading to a discontinuity in the spatial distribution of the activation times. Most mapping procedures rely on triangulation methods for the interpolation that is necessary to generate isochronous lines.":" This assumes that the surface to be mapped is continuous and that it is locally autocorrelated. For example, the continuous nature of activation does not apply if wavefronts break through from below or terminate abruptly due to an infarct. Local autocorrelation implies that activation everywhere on the epicardial surface can be found by interpolating between a measurement point and its nearest neighbor. In general, these conditions do not always hold. Further complications are the effects of electrode density. size. and position and the state of the underlying tissue. Initial studies examining errors in maps have been published by Ideker et a1. 10 Given these shortcomings in electrogram analysis and map generation, one can continue to examine new approaches to solve these problems. A biophysical model of thin layer sources with varying conduction velocities has been developed II to better understand electrogram analysis. Additionally. newer methods of map generation, namely gridding and krigging, have been used to estimate the inherent error in the map generation process. 12.1 3 The goals of this study were to record late potentials from the epicardial surface and to define their activation sequence. Towards this end it was necessary to accurately define activation times in individual electrograms and to generate two dimensional isochronous maps. Ambiguities encountered in electrogram analysis and map generation are the focus of this short report.

* From the Division of Cardiovascular Diseases. University of Oklahoma Health Sciences Center. Oklahoma City. Oklahoma. tFrom the Department of Biomedical Engineering. Penn State University. State College. Pennsylvania. Funded in part by a grant from the National Institutes of Health (HL-}6625 and HL-44695).

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Ambiguities of Epicardial Mapping •

Methods All data were obtained from ane sthetized dogs who had the left anterior descending coronary artery ligated 4 days prior to the study. The two- stage Harris procedure was used. All protocols were approved by the Institutional Animal Care and Use Committee and performed in an AAALAC accredited facility. This 4-day infarct model has been used extensively to study re-entry arrhythmias"? and has been shown to be an excellent late potential generator'" The mapping system used has been previously described 15 and has 128 channels. The electrode array of 124 sites covers a region of 6 em in diameter. The central part of the array is a lu x 10 grid with 4 mm spacing. Six electrodes are placed in a triangular pattern on each side of this square grid and account for the total of 124 recordings . All electro grams were recorded in the unipolar mode with the right leg used as a reference . The electrograms were sampled at l.0 KHz at a bandwidth of 0.1- 300 Hz. Interchannel skew time was 2 j.1S for a total of 256 j.1s latency between channel I and channel 128. The array of unipolar recordings could be converted to bipolar pairs by subtracting adjacent electrograms in either the horizontal direction (dV/dx) or the vertical direction (dv/dyj.!" In addition, the time derivative (dV/dt) was calculated by a two point central difference algorithm. Several methods of isochronous map generation and analysis were investigated . These included the traditional approach of triangulation and the more contemporary methods of gridding and krigging. 12 . 1 3 A systematic review of the methods for generating a map is beyond the scope of this report.

Results Figure I shows six unipolar electrogram recordings in the left column. The number represents the electrode site within the scheme of the array . Each recording is vertically adjacent within the array electrode . Of interest in these recordings are the late potentials clearly observed within the ST-segrnents. following the QR deflection which represent distant ventricular activation. Electrograms 48 and 84 both show double deflection late potentials. It is not evident which point to choose as the time of late potential activation. One should note that in electrogram 48 the first late potential deflection coincides with the late potential deflection at site 37 and the

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Fig. 1. Examples of electrograms overlying an infarct region and their temporal (dv/dt) and spatial derivatives (dV/dy and dv/dx).

second late potential deflection coincides with the late potential deflection at site 59. The time derivative (dV/dt) of each waveform is shown in the second column. The largest negative derivative in the late potential region results from the first late potential deflection at site 48. The vertical (dV/dy) and horizontal (dV/dx) spatial derivatives mimic bipolar recordings . In electrogram 48 the largest deflection in dV/dy is the second deflection while in dV/dx it is the first deflection. Note that in both cases the initial late potential is quite similar. Electrogram 84 which also has multiple late potential deflections exhibits similar ambiguities in the various derivatives. Figure 2 presents model OUtput data from a slab of tissue with a zone of slow conduction. The results are quite similar to those observed by Lesh with a different biophysical model of conduction. The data presented here are the bidomain model previously described by Geselowitz et aI. I 1.16 The lower part of the figure depicts normal conducting tissue (0.6 Mis) on each side of the shaded, slower conducting region. The dot in the middle represents the electrode recording site. The 12 simulated outputs are shown in the top of the figure with the number representing the conduction velocity. The center of each simulated

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cause the grid matrix nodes roughly correspond to the original measurement points. The infarct border zone is encountered about halfway across the map. and conduction in this region is delayed and nonuniform. The 25 x 25 grid matrix (B) has twice as many grid points as measurement points in the electrode array, and artifacts begin to appear as extreme regions. This is more fully appreciated in (C) where the grid matrix is 100 x 100. The activation sequence is not accurately depicted in this map. However, the maps created by gridding are more amenable to two-dimensional signal processing methods such as smoothing or spatial derivatives. (D) is the smoothed version of (C) and appears more like the map in (A). However, the high grid density after smoothing contains more spatial-temporal information.

Discussion

t

Electrode in Zone of Slow Conduction Fig. 2. Simulated electrograms from a site within a zone of depressed conduction velocity. The bottom shows regions bounded by normally conducting muscle (0.6 MIs). The conduction velocity in MIs is shown next to each simulated electrogram.

tracing has a zero crossing and is the time of activation at the electrode site. Note the increasing appearance of the so-called fractionated type of electrogram recording. Note also that at about 0.2 MIs, the largest overall amplitude and the largest magnitude derivative are no longer associated with the central portion of the electrogram. The low level higher frequency components seen in the recordings with the slowest conduction times are due to the limits in the resolution of the model and are most likely an artifact. Figure 3 is an example of an activation map generated from analyzing as set of electrograms over an infarct zone of slow conduction. The gridding method of map generation was used with a course (15 x 15), medium (25 x 25). and fine (100 x 100) grid mesh shown in (A), (B), and (C), respectively. The zone of slow conduction is on the right side and is depicted by the closely spaced isochronous lines. The isochronous lines are separated by 3 ms increments. The crosses represent the actual electrode positions. In (A) there is little interpolation be-

There are many ambiguities encountered in generating cardiac activation maps. Perhaps chief among them is the selection of a unique activation time from electrogram recordings obtained from damaged regions of the myocardium. This study only examined data obtained during sinus rhythm. The examples shown in Figure 1 are relatively simple when compared with electrograms obtained during ventricular tachycardia. The basis for identifying activation as the largest negative derivative comes from two sources . Experimental observations from isolated Purkinje fibers demonstrated the close correlation between action potential upstroke and the negative derivative of the extracellular electrogram. Cable models support this observation with the requirement that conduction velocity be constant. These observations probably do not apply to sheets of tissue where there is a varying conduction velocity. The results from Lesh et al. 6 and the bidomain model results presented in Figure 2 imply that identifying activation time from regions of changing conduction velocity are indeed problematic. Greater attention to the source geometries and conduction properties is necessary for defining more accurate measures of activation times. The generation of a map from the observed data points assumes the data exist in a spatially continuous region. That is. the standard methods of map generation assume one can interpolate between observation points to arrive at a value which is based on a weighted measure of nearby points. The method of triangulation is most often used with either linear or cubic spline interpolation. More modern methods

Ambiguities of Epicardial Mapping





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A

Fig. 3. An activation map generated by a matrix grid of (A) 15 x 15. (B) 25 x 25. and (C) 100 x 100 points (D) is the smoothed version of the map in (C).

such as gridding, shown in a single example in Figure 3, and krigglng!? allow more sophisticated approaches for quantifying errors in maps. In addition, it is possible to map around nonconducting regions. For example, in Figure 1, the ambiguity in electrogram 48 may be resolved by excluding this point in generating the isochronous map. Conceivably. the underlying tissue is dead and the deflections are from nearby sources. The mapping algorithms must not assume that the activation time at this point is un-

available. but that no interpolation can occur across this point. The algorithms must map around this "obstacle." In geophysical terms a fault has been encountered and in physiologic terms a true infarct region cannot have an activation time. The literature on cardiac mapping does not offer a solution to this problem of a discontinuity in activation. A critical examination of epicardial maps generated from electrograms overlying infarct regions is needed. More informed measures of activation, per-

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haps based on biophysical models, is a first step. Additionally the methods used for creating the maps from sets of activation time have probably not used techniques which allow for discontinuities in the region of interest.

References 1. EI-Sherif N, Smith A, Evans K: Canine ventricular arrhythmias in the late myocardial infarction period: epicardial mapping of reentrant circuits. Circ Res 49:255, 1981 2. Wit AL, Allessie MA, Bonke FIM et al: Electrophysiologic mapping to determine the mechanisms of experimental ventricular tachycardia initiated by premature impulses: experimental approach-initial results demonstrating reentrant excitation. Am J Cardio149:166,1982 3. El-Sherif N, Mehra R, Gough WB, Zeiler RH: Reentrant ventricular arrhythmias in the late myocardial infarction period. Circulation 68:644. 1983 4. Scherlag BJ, El-Sherif N, Hope RR. Lazzara R: Characterization and localization of ventricular arrhythmias due to myocardial ischemia and infarction. Circ Res 35:372, 1974 5 El-Sherif N, Scherlag BJ, Lazzara R, Hope RR: Reentrant ventricular arrhythmias in the late myocardial infarction period. I. Conduction characteristics in the infarction zone. Circulation 55:686. 1977 6. Lesh MD, Spear MF, Simson MB: A computer model of the electrogram: what causes fractionation? J Electrocardiol 21(Suppl):S69, 1988

7. Barr RC. Gallie TM. Spach MS: Automated production of contour maps for electrophysiology. I. Problem definition. solution strategy, and specification of geometric model. Comput Biomed Res 13: 142, 1980 8. Barr RC, Gallie TM, Spach MS: Automated production of contour maps for electrophysiology. II. Triangulation, verification, and organization of the geometric model. Comput Biomed Res 13: 154, 1980 9. Barr RC. Gallie TM, Spach MS: Automated production of contour maps for electrophysiology. !II. Construction of contour maps. Comput Biomed Res 13: 171. 1980 10. Ideker RE, Smith WM, Blanchard SM et al: The assumptions of isochronal cardiac mapping. PACE 12:456, 1989 11. Geselowitz DB. Mowrey K, Smith S, Berbari EJ: Model studies of extracellular electrograms arising from an excitation wave propagating in a thin layer. IEEE Trans Biomed Eng 38:526, 1991 12. Robinson JE: Computer Applications in Petroleum Geology. Hutchinson Ross Publishing Co., 1982 13. Davis JC: Statistics and Data Analysis in Geology 2nd ed. John Wiley & Sons, New York, 1986 14. Berbari EJ, Scherlag BJ, Hope RR, Lazzara R: Recording from the body surface of arrhythmogenic ventricular activity during the ST segment. Am J Cardiol 41:697.1978 15. Berbari EJ, Lander P, Geselowitz DB: A cardiac mapping system for identifying late potentials: correlation with signal averaged surface recordings. p. 369. In: Computers in Cardiology IEEE Computer Society Press. Los Angeles, 1988 16. Geselowitz DB, MllIer III WT: A bidomain model for anisotropic cardiac muscle. Ann Biomed Engr 11: 191. 1983