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Frontal Plane Vectorcardiograms: Theory and Graphics Visualization of Cardiac Health Status

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Abstract

The electrocardiogram (ECG) is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, these signals are highly subjective, and the symptoms may appear at random in the time scale. It is very taxing and time-consuming to decipher cardiac abnormalities based on these ECG signals. The Vectorcardiogram (VCG) is the vector loop in the 2-D frontal plane, indicating the magnitude and direction of the instantaneous heart electrical activity vector (HAV), which represents the sum of the dipole vectors located along the instantaneous depolarization wavefront. The HAV is constructed from the monitored 3-lead ECG signals, placed at the three vertices of the modified Einthoven triangle formed by the 3-lead system in the frontal plane of the torso. The VCG examines the electrical activities within the heart, using the ECG signals along the three sides of the modified Einthoven triangle, and displays electrical events in the 2-dimensional frontal plane. This study demonstrates the development of the heart-depolarisation vector-locus cardiogram (using modified Einthoven’s triangle), as a diagnostic measure of the left ventricular depolarisation strength. Our work involves the reconstruction of the “equivalent heart vector” for the QRS complex from limb lead voltages of a sample ECG, and plotting the progression of the cardiac vector during the QRS complex. We have demonstrated the construction of the frontal plane heart-depolarization vector cardiogram (HDVC), as the path of the locus of the tip of the heart electrical activity vector, with initial and terminal points at the origin. In this work, we have shown characteristic patterns of HDVC for cardiac states namely, normal, bundle branch block, ventricular hypertrophy and myocardial infarction. We have demonstrated how HDVC can be diagnostically employed to characterize cardiac disorders, such as ventricular hypertrophy bundle branch block and inferior myocardial infarction.

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References

  1. Acharya, U. R., Spaan, J. A. E., and Suri, J. S., Advances in Cardiac Signal Processing. GmbH Berlin Heidelberg: Springer Verlang, 2007. ISBN:978-3-540-36674-4.

    Book  MATH  Google Scholar 

  2. Astrom, M., Santos, E. C., Sornmo, L., Laguna, P., and Wohlfart, B., Vectorcardiographic Loop Alignment and the Measurement of Morphologic Beat-to-Beat Variability in Noisy Signals. IEEE Trans. Biomed. Eng. 47(4):497–506, 2000.

    Article  Google Scholar 

  3. Atarashi, H., Ogawa, S., and Harumi, K., Idiopathic Ventricular Fibrillation Investigators. Three-year follow-up of patients with right bundle branch block and ST segment elevation in the right precordial leads: Japanese Registry of Brugada Syndrome. Idiopathic Ventricular Fibrillation Investigators. J. Am. Coll. Cardiology. 37:1916–1920, 2001.

    Article  Google Scholar 

  4. Benchimol, A., Desser, K. B., and Schumacher, J., Left anterior hemiblock from inferior infarction with left axis deviation. Chest. 61:74–76, 1972.

    Article  Google Scholar 

  5. Benchimol, A., and Desser, K. B., Advances in clinical vectorcardiography. Am. J. Cardiol. 36:76–86, 1975.

    Article  Google Scholar 

  6. Carson, W., Tseng, Y. Z., Tseng, C. D., Huang, P. J., and Wu, T. L., Vectorcardiographic criteria for acute right ventricular infarction. Eur. Heart J. 9(9):955–96, 1, 1988.

    Google Scholar 

  7. Chee, J., Acharya, U. R., Kenneth, Er., Tan, W., and Chua, K. C., Visualization of cardiac health using vector cardiogram. Innovations and Technology in Biology and Medicine (ITBM-RBM), 2008 (In Press).

  8. Chia, J. M., Fischer, S. E., Wickline, S. A., and Lorenz, C. H., Performance of QRS detection for cardiac magnetic resonance imaging with a novel vectorcardiographic triggering method. J. Magn. Reson. Imaging. 12(5):678–688, 2000.

    Article  Google Scholar 

  9. Chou, T. C., Value and limitations of vectorcardiography in cardiac diagnosis. Cardiovasc. Clin. 6:163–178, 1975.

    Google Scholar 

  10. Chou, T. C., When is the vectorcardiogram superior to the scalar electrocardiogram? J. Am. Coll. Cardiol. 8:791–799, 1986.

    Article  Google Scholar 

  11. Dahlin, L. G., Ebeling-Barbier, C., Nylander, E., Rutberg, H., and Svedjeholm, R., Vectorcardiography is Superior to Conventional ECG for Detection of Myocardial Injury after Coronary Surgery. Scand. Cardiovasc. J. 35(2):125–128, 2001.

    Article  Google Scholar 

  12. Dower, G. E., Machado, H. B., and Osborne, J. A., On Deriving the Electrocardiogram from Vectocardiographic Leads. Clin. Cardiol. 3:87–95, 1980.

    Google Scholar 

  13. Downs, T., Liebman, J., and Mackay, W., Statistical methods for vectorcardiogram orientations. In: Hoffman, I., Hamby, R. I., and Glassman, E., (Eds.), Vectorcardiography 2: Proc. XIth International Symposium on Vectorcardiography. pp. 216–222, 1971.

  14. Edenbrandt, L., and Pahlm, O., Vectorcardiogram synthesized from a 12-lead ECG: superiority of the inverse Dower matrix. J. Electrocardiol. 21(4):361–367, 1988.

    Article  Google Scholar 

  15. Eriksson, P., Andersen, K., Swedberg, K., and Dellborg, M., Vectorcardiographic monitoring of patients with acute myocardial infarction and chronic bundle branch block. Eur. Heart J. 18(8):1288–1295, 1997.

    Google Scholar 

  16. Fedor, J. M., Walston, A., Wagner, G. S., and Starr, J., The vectorcardiogram in right bundle branch block: correlation with cardiac failure and pulmonary disease. Circulation. 53(6):926–930, 1976.

    Google Scholar 

  17. Ghista, D. N., Van Vollenhoven, E., Yang, W. J., Reul, H., and Bleifeld, W., Cardiovascular Engineering Part 3: Diagnosis. Karger, 1983. ISBN: 978-3-8055-3611-0.

  18. Grishman, A., and Donoso, E., Spatial vectorcardiography II. Mod. Concepts Cardiovasc. Dis. 30:693–696, 1961.

    Google Scholar 

  19. Guo, X., Jue, X., and Ruan, Y., Model TJ-IV Computer-assisted vectorcardiogram analysis system. J. Tongji. Med. Univ. 21:22–81, 2001.

    Google Scholar 

  20. Guo, X. M., Que, X. H., Ma, Y. X., and Wang, Z. C., Development and applications of an auto-analyzing system for model TJ-IV vector-cardiogram. Zhongguo Yi Liao Qi Xie Za Zhi. 29(1):19–22, 2005.

    Google Scholar 

  21. http://www.cdc.gov/nchs/data/hestat/leadingdeaths03_tables.pdf (Last accessed on April 2008).

  22. http://www.ptb.de/index_en.html (Last accessed on May 2008).

  23. Hurd, H. P., Starling, M. R., and Crawford, M. H., Comparative accuracy of electrocardiographic and vectorcardiographic criteria for inferior myocardial infarction. Circulation. 63:1025–1029, 1981.

    Google Scholar 

  24. Huszar, R. J., Basic Dysrhythmias. Interpretation & Management, 3rd ed. St. Louis, Mo: Mosby, Inc, 2002.

    Google Scholar 

  25. Ikeda, K., Takahashi, K., and Yasui, S., Assessment of right ventricular overload in patients with chronic pulmonary disease by 12-lead electrocardiography, vectorcardiography and body surface electrocardiographic mapping. Jpn. Circ. J. 53(10):1278–1286, 1989.

    Google Scholar 

  26. Jens, J., Sven, V. E., Bo, L., Peter, L., and Christer, S., On-line vectorcardiography during elective coronary angioplasty indicates procedure-related myocardial infarction. Coronary Artery Disease. 11(2):161–169, 2000.

    Article  Google Scholar 

  27. Kawahito, S., Kitahata, H., Tanaka, K., Nozaki, J., and Oshita, S., Dynamic QRS-complex and ST-segment monitoring by continuous vectorcardiography during carotid endarterectomy. Br. J. Anaesth. 90(2):142–147, 2003.

    Article  Google Scholar 

  28. Lynn, P. A., Online Digital Filter for Biological Signals: Some Fast Designs for a small computer. Med. Biol. Eng. Comput. 15(5):534–540, 1977.

    Article  Google Scholar 

  29. Malmivuo, J., and Plonsey, R., Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Field0073. New York: Oxford University Press, 1995a.

    Google Scholar 

  30. Malmivuo, J., and Plonsey, R., Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Field0073. New York: Oxford University Press, 1995b. Chapter 16.

    Google Scholar 

  31. McConahay, D. R., McCallister, B. D., Hallermann, F. J., and Smith, R. E., Comparative quantitative analysis of the electrocardiogram and the vectorcardiogram Correlations with the coronary arteriogram. Circulation. 42(2):245–259, 1970.

    Google Scholar 

  32. Nakos, A., and Joyner, D., Linear algebra with Applications. Brooks/Cole Pub Co., 1998

  33. Rautaharju, P. M., A hundred years of progress in electrocardiography. 2: The rise and decline of vectorcadiography. Can. J. Cardiol. 4:60–71, 1998.

    Google Scholar 

  34. Reddy, B. R. S., Murthy, I. S. N., and Chatterjee, P. C., Rhythm Analysis Using Vectorcardiograms. IEEE Trans.Biomed. Eng. 32(2):97–104, 1985.

    Article  Google Scholar 

  35. Riera, A. R. P., Uchida, A. H., Filho, C. F., Meneghini, A., Ferreira, C., Schapacknik, E., Dubner, S., and Moffa, P., Significance of Vectorcardiogram in the Cardiological Diagnosis of the 21st Century. Clin. Cardiol. 30(7):319–323, 2007.

    Article  Google Scholar 

  36. Sokolow, M., Mcllroy, M. B., and Chiethin, M. D., Clinical cardiology. Stamford, CT: Appleton & Lange medical book, 1990. ISBN:978-0838510933.

    Google Scholar 

  37. Tompkins, W. J., Biomedical Signal Processing. Englewood Cliffs, NJ: Prentice Hall, 1993.

    Google Scholar 

  38. van Oosterom, A., Ihara, Z., Jacquemet, V., and Hoekema, R., Vectorcardiographic lead systems for the characterization of atrial fibrillation. J. Electrocardiol. 40(4):343.e1–343.e11, 2007.

    Article  Google Scholar 

  39. Vine, D. L., Finchum, R. N., Dodge, H. T., Bancroft, W. H. Jr, and Hurst, D. C., Comparison of the vectorcardiogram with the electrocardiogram in the prediction of left ventricular size. Circulation. 43(4):547–558, 1971.

    Google Scholar 

  40. Wariar, R., and Eswaran, C., Integer coefficient bandpass filter for the simultaneous removal of baseline wander, 50 and 100 Hz interference from the ECG. Med. Biol. Eng. Comput. 29(3):333–336, 1991.

    Article  Google Scholar 

  41. Watanabe, Y., Wang, J., Kondo, T., Tokuda, M., Chikamatsu, H., Yasui, T., Yamaguchi, T., Kinoshita, M., Kamide, S., Nagai, N., Abo, Y., Yokoi, H., and Hishida, H., Vectorcardiographic evaluation of myocardial infarct size: departure parameters are superior to conventional spatial parameters. Jpn. Circ. J. 62(7):473–478, 1998.

    Article  Google Scholar 

  42. Young, E., Levine, H. D., Vokonas, P. S., Kemp, H. G., Williams, R. A., and Gorlin, R., The frontal plane vectorcardiogram in old inferior myocardial infarcton II. Mid-to-late QRS changes. Circulation. 42(6):1143–1162, 1970.

    Google Scholar 

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Correspondence to U. Rajendra Acharya.

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Ghista, D.N., Acharya, U.R. & Nagenthiran, T. Frontal Plane Vectorcardiograms: Theory and Graphics Visualization of Cardiac Health Status. J Med Syst 34, 445–458 (2010). https://doi.org/10.1007/s10916-009-9257-x

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  • DOI: https://doi.org/10.1007/s10916-009-9257-x

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