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Estimation of Distance Between a Unipolar Recording Electrode and a Myocardial Bundle Based on Signal Characteristics

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Abstract

The aim of the present paper, is the estimation of the distance between an electrode used as a recording site of the extracellular potential field and a surviving myocardial bundle. The importance of the reliable solution of this problem lies among others in controlling ablation. For our purposes one-dimensional propagation is considered and current sources are activated along a cable simulating the propagating waves with constant velocity. Different models of current sources are explored. By use of these models, the corresponding functions expressing extracellular potentials are calculated, using the volume conductor equation. This way, extracellular potentials are modeled as parametric functions of longitudinal distance, while perpendicular distance, current source strength, and other factors related to the propagated wave are parameters of the functions. Simulated annealing is applied for model parameter estimation and appropriate Time Domain and Wavelet Domain cost functions are investigated. Different combinations of model and cost function are evaluated regarding the accuracy of distance estimation. A continuous source model function with a wavelet cost function was found to be the most accurate combination. The accuracy of distance estimation is related to the selected source model and to the actual distance of recording in a nonmonotonic way.

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REFERENCES

  1. Akay, M. Wavelets in biomedical engineering. Ann. Biomed. Eng. 23:531-541, 1995.

    Google Scholar 

  2. Albers, B. A., J. H. Put, W. Wallinga, and P. Wirtz. Quantitative analysis of single muscle fibre action potentials recorded at known distances. Electroencephalogr. Clin. Neurophysiol. 73(3):245-253, 1989.

    Google Scholar 

  3. Barnard, E., and D. Casasent. Invariance and neural nets. IEEE Trans. Neural Netw. 2(5):498-508, 1991.

    Google Scholar 

  4. Bevington, P. R. Data Reduction and Error Analysis for the Physical Sciences. New York: McGraw-Hill Book, 1969.

    Google Scholar 

  5. Chouvarda, I., N. Maglaveras, F. J. L. Van Capelle, J. de Bakker, C. Pappas, and M. Strintzis. Comparison of time and frequency based methods for electrode distance estimation from surviving tissue. Comput. Cardiol. 24:331-334, 1997.

    Google Scholar 

  6. De Bakker, J. M. T., F. J. L. Van Capelle, M. J. Janse, S. Tasseron, J. T. Vermeulen, N. de Jonge, and J. R. Lahpor. Slow conduction in the infarcted human heart, “ZigZag” course of activation. Circulation 88:915-926, 1993.

    Google Scholar 

  7. Ellis, W. S., D. M. Auslander, and M. D. Lesh. Fractionated electrograms from a computer model of heterogeneously uncoupled anisotropic ventricular myocardium. Circulation 92(6):1619-1626, 1995.

    Google Scholar 

  8. Everett-Carter, Jr., F. Taygeta Scientific Inc. http://www.taygeta.com/annealing/simanneal.html.

  9. Henriquez, C. S., and, A. A. Papazoglou. Using computer models to understand the roles of tissue structure and membrane dynamics in arrhythmogenesis. Proc. IEEE 37(3):334-354, 1996.

    Google Scholar 

  10. Henriquez, C. S., and R. Plonsey. Simulation of propagation along a cylindrical bundle of cardiac tissue-I: Mathematical formulation of simulation. IEEE Trans. Biomed. Eng. 37(9):850-860, 1990.

    Google Scholar 

  11. Henriquez, C. S., and R. Plonsey. Simulation of propagation along a cylindrical bundle of cardiac tissue-II: Results of simulation. IEEE Trans. Biomed. Eng. 37(9):861-875, 1990.

    Google Scholar 

  12. Henriquez, C. S., N. Trayanova, and R. Plonsey. Potential and current distributions in a cylindrical bundle of cardiac tissue. Biophys. J. 53(6):907-918, 1988.

    Google Scholar 

  13. Ingber, L. Simulated annealing:Practice versus theory. J. Math. Comp. Modell. 18(11):29-57, 1993.

    Google Scholar 

  14. Jain, A. K., P. W. Duin, and J. Mao. Statistical pattern recognition: A review. IEEE PAMI 22(1):4-37, 2000.

    Google Scholar 

  15. Kirkpatrick, S. Optimization by simulated annealing: Quantitative studies. J. Statist. Phys. 34:975-986, 1984.

    Google Scholar 

  16. Kirkpatrick, S., C. D. Gelatt Jr., and M. P. Vecchi. Optimization by simulated annealing. Science 220(4598):671-680, 1983.

    Google Scholar 

  17. Maglaveras, N., J. M. T. De Bakker, F. J. L. Van Capelle, C. Pappas, and M. Janse. Activation delay in bifurcating strands of surviving myocardial tissue in healed infarction. A comparison between model and experiment. Am. J. Phys. Heart Circ. Physiol. 269(38):H1441-H1449, 1995.

    Google Scholar 

  18. Maglaveras, N., F. Offner, F. J. van Capelle, M. A. Allessie, and A. V. Sahakian. Effects of barriers on propagation of action potentials in two-dimensional cardiac tissue. A computer simulation study. J. Electrocardiol. 28(1):17-31, 1995.

    Google Scholar 

  19. Maglaveras, N., F. J. L. Van Capelle, and J. DeBakker. Wave propagation simulation in normal and infarcted myocardium. Computational and modeling issues. Med. Inform. (Lond.) 23(2):105-118, 1998.

    Google Scholar 

  20. Mallat, S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. PAMI 11(7):674-691, 1989.

    Google Scholar 

  21. Mallat, S. Singularity detection and processing using wavelets. IEEE Trans. Inf. Theory 38:617-643, 1992.

    Google Scholar 

  22. Morita, M. Associative memory with nonmonotonic dynamics. Neural Netw. 6:115-126, 1993.

    Google Scholar 

  23. Panfilov, A., and J. P. Keener. Generation of reentry in anisotropic myocardium. J. Cardiovasc. Electrophysiol. 4(4):412-421, 1993.

    Google Scholar 

  24. Parker, K. K., and J. P. Wikswo. A model of the magnetic fields created by single motor unit compound action potentials in sceletal muscle. IEEE Trans. Biomed. Eng. 44(10):948-957, 1997.

    Google Scholar 

  25. Plonsey, R., and R. C. Barr. Interstitial potentials and their change with depth into cardiac tissue. Biophys. J. 51:547-555, 1987.

    Google Scholar 

  26. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. Cambridge: Cambridge University Press, 1992.

    Google Scholar 

  27. Roberge, F. A., A. Vinet, and B. Victorri. Reconstruction of propagated electrical activity with a two-dimensional model of anisotropic heart muscle. Circ. Res. 58(4):461-475, 1986.

    Google Scholar 

  28. Spach, M. S., and P. Dolber. Relating extracellular potentials and their derivatives to anisotropic propagation at a microscopic. Circ. Res. 58:356-371, 1986.

    Google Scholar 

  29. Spach, M. S., J. F. Heidlage, E. R. Heidlage, E. Hofer, K. H. Raines, and C. F. Starmer. Cellular V′max reflects both membrane properties and the load presented by adjoining cells. Am. J. Physiol. 263:H1855, 1992.

    Google Scholar 

  30. Sykulski, J. K., M. Rotaru, M. Sabene, and M. Santilli. Comparison of optimization techniques for electromagnetic applications. COMPEL 17(1–3):171-176, 1998.

    Google Scholar 

  31. Trayanova, N. A method for calculation the extracellular poten-tials from experimentally recorded intracellular potentials of a single muscle fibers. Acta Physiol. Pharmacol. Bulg. 14(2):83-91, 1988.

    Google Scholar 

  32. Trayanova, N. A., C. S. Henriquez, and R. Plonsey. Limitations of approximate solutions for computing the extracellular potential of single fibers and bundle equivalents. IEEE Trans. Biomed. Eng. 37(1):22-35, 1990.

    Google Scholar 

  33. Xie, B., S. C. Heald, A. J. Camm, E. Rowland, and D. E. Ward. Successful radiofrequency ablation of accessory pathways with the first energy delivery: The anatomic and electrical characteristics. Eur. Heart J. 17(7):1072-1079, 1996.

    Google Scholar 

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Chouvarda, I., Maglaveras, N., Pappas, C. et al. Estimation of Distance Between a Unipolar Recording Electrode and a Myocardial Bundle Based on Signal Characteristics. Annals of Biomedical Engineering 32, 1336–1347 (2004). https://doi.org/10.1114/B:ABME.0000042222.90548.00

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  • DOI: https://doi.org/10.1114/B:ABME.0000042222.90548.00

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