Skip to main content
Log in

Acceleration of FEM-based transfer matrix computation for forward and inverse problems of electrocardiography

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The distributions of transmembrane voltage (TMV) within the cardiac tissue are linearly connected with the patient’s body surface potential maps (BSPMs) at every time instant. The matrix describing the relation between the respective distributions is referred to as the transfer matrix. This matrix can be employed to carry out forward calculations in order to find the BSPM for any given distribution of TMV inside the heart. Its inverse can be used to reconstruct the cardiac activity non-invasively, which can be an important diagnostic tool in the clinical practice.The computation of this matrix using the finite element method can be quite time-consuming. In this work, a method is proposed allowing to speed up this process by computing an approximate transfer matrix instead of the precise one. The method is tested on three realistic anatomical models of real-world patients. It is shown that the computation time can be reduced by 50% without loss of accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bronstein IN, Semendjajew KA (1985) Taschenbuch der Mathematik. Verlag Nauka, Moskau

    Google Scholar 

  2. Farina D (2008) Forward and inverse problems of electrocardiography: clinical investigations. Universitätsverlag Karlsruhe, Karlsruhe

  3. Farina D, Dössel O (2006) Influence of cardiac activity in midmyocardial cells on resulting ECG: simulation study. In: Proceedings of 40th annual conference of German association of biomedical engineering; Biomedizinische Technik, vol 51 (suppl.), CD-ROM, ISSN 0939-4990

  4. Farina D, Dössel O (2007) Model-based approach to the localization of infarction. Comput Cardiol 34:173–176

    Article  Google Scholar 

  5. Farina D, Dössel O (2008) Non-invasive model-based localization of ventricular ectopic centers from multichannel ECG. In: Proceedings of 10th international workshop on optimization and inverse problems in electromagnetism, pp 71–72

  6. Farina D, Skipa O, Kaltwasser C, Dössel O, Bauer WR (2004) Optimization-based reconstruction of depolarization of the heart. Comput Cardiol 31:129–132

    Article  Google Scholar 

  7. Fischer G, Tilg B, Wach P, Modre R, Leder U, Nowak H (1999) Application of high-order boundary elements to the electrocardiographic inverse problem. Comput Methods Programs Biomed 58:119–131

    Article  Google Scholar 

  8. Fischer G, Pfeifer B, Seger M, Hintermüller C, Hanser F, Modre R, Tilg B, Trieb T, Kremser C, Roithinger FX, Hintringer F (2005) Computationally efficient noninvasive cardiac activation time imaging. Methods Inf Med 44(5):674–686

    Google Scholar 

  9. Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 41:2251–2269

    Article  Google Scholar 

  10. Geselowitz DB (1989) On the theory of the electrocardiogram. Proc IEEE 77(6):857–876

    Article  Google Scholar 

  11. Geselowitz DB (1992) Description of cardiac sources in anisotropic cardiac muscle: application of bidomain model. J Electrocardiol 25:65–67

    Article  Google Scholar 

  12. Golub GH, Loan CFV (1996) Matrix computations. Johns Hopkins University Press, Baltimore

  13. Greensite F, Huiskamp G (1998) An improved method for estimating epicardial potentials from the body surface. IEEE Trans Biomed Eng 45:98–104

    Article  Google Scholar 

  14. Gulrajani R, Roberge R, Savard P (1989) The inverse problem of electrocardiography. In: Macfarlane P, Lawrie TTV (eds) Comprehensive electrocardiology, vol 1. Pergamon Press, NY, pp 237–288

  15. Hansen PC (1998) Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion. SIAM, Philadelphia

    Google Scholar 

  16. Hansen PC (2001) The L-curve and its use in the numerical treatment of inverse problems. In: Computational inverse problems in electrocardiography. Advances in Computational Bioengineering, chap 4. WIT Press, Southampton, pp 119–142

  17. Huiskamp G, van Oosterom A (1988) The depolarization sequence of the human heart surface computed from measured body surface potentials. IEEE Trans Biomed Eng 35(12):1047–1058

    Article  Google Scholar 

  18. Jiang Y, Farina D, Dössel O (2007) An improved spatio-temporal maximum a posteriori approach to solve the inverse problem of electrocardiography. In: Proceedings of 41st annual conference of German association of biomedical engineering; Biomedizinische Technik, vol 52 (suppl.), CD-ROM, ISSN 0939-4990

  19. Kauppinen P, Hyttinen J, Laarne P, Malmivuo J (1999) A software implementation for detailed volume conductor modelling in electrophysiology using finite difference method. Comput Methods Programs Biomed 58:191–203

    Article  Google Scholar 

  20. MacLeod RS, Brooks DH (1998) Recent progress in inverse problems of electrocardiography. IEEE Eng Med Biol 17(1):73–83

    Article  Google Scholar 

  21. Modre R, Tilg B, Fischer G, Wach P (2002) Noninvasive myocardial activation time imaging: a novel inverse algorithm applied to clinical ECG mapping data. IEEE Trans Biomed Eng 49(10):1153–1161

    Article  Google Scholar 

  22. Reumann M, Farina D, Miri R, Lurz S, Osswald B, Dossel O (2007) Computer model for the optimization of av and vv delay in cardiac resynchronization therapy. Med Biol Eng Comput 45:845–854

    Article  Google Scholar 

  23. Skipa O (2004) Linear inverse problem of electrocardiography: epicardial potentials and transmembrane voltages. Helmesverlag, Karlsruhe

  24. Tikhonov AN, Arsenin VY (1977) Solutions of ill-posed problem. Winston&Sons, New York

    Google Scholar 

  25. Wolters C, Grasedyck L, Hackbusch W (2004) Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem. Inverse Probl 20:1099–1116

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Biosense Webster for financial support as well as Prof. Dr. med., Dr. rer. nat. W.R. Bauer and Dr. med. C. Kaltwasser from University Hospital of Würzburg, Germany, who provided the patient data employed in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dmytro Farina.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Farina, D., Jiang, Y. & Dössel, O. Acceleration of FEM-based transfer matrix computation for forward and inverse problems of electrocardiography. Med Biol Eng Comput 47, 1229–1236 (2009). https://doi.org/10.1007/s11517-009-0503-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-009-0503-7

Keywords

Navigation