Medical & Biological Engineering & Computing

, Volume 37, Issue 5, pp 574–584

Spatial, temporal and wavefront direction characteristics of 12-lead T-wave morphology

Article

Abstract

Three new approaches for the analysis of ventricular repolarisation in 12-lead electrocardiograms (ECGs) are presented: the spatial and temporal variations in T-wave morphology and the wavefront direction difference between the ventricular depolarisation and repolarisation waves. The spatial variation characterises the morphology differences between standard leads. The temporal variation measures the change in interlead relationships. A minimum dimensional space, constructed by ECG singular value decomposition, is used. All descriptors are measured using the ECG vector in the constructed space and the singular vectors that define this space. None of the descriptors requires time domain measurements (e.g. the precise detection of the T-wave offset), and so the inaccuracies associated with conventional QT interval related parameters are avoided. The new descriptors are compared with the conventional measurements provided by a commercial system for an automatic evaluation of QT interval and QT dispersion in digitally recorded 12-lead ECGs. The basic comparison uses a set of 1100 normal ECGs. The short-term intrasubject reproducibility of the new descriptors is compared with that of the conventional measurements in a set of 760 ECGs recorded in 76 normal subjects and a set of 630 ECGs recorded in 63 patients with hypertrophic cardiomyopathy (ten serial recordings in each subject of both these sets). The discriminative power of the new and conventional parameters to distinguish normal and abnormal repolarisation patterns is compared using the same set. The results show that the new parameters do not correlate with the conventional QT interval-related descriptors (i.e. they assess different ECG qualities), are generally more reproducible than the conventional parameters, and lead to a more significant separation between normal and abnormal ECGs, both univariately and in multivariate regression models.

Keywords

T-wave morphology QT dispersion ECG decomposition Spatial/temporal variation Wavefront direction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abildskov, J. A., Burgess, M. J., Lux, R. L., andWyatt, R. F. (1976): ‘Experimental evidence for regional cardiac influence in body surface isopotential maps of dogs’,Circ. Res.,38, pp. 386–391Google Scholar
  2. Acar, B., andKoymen, H. (1999): ‘SVD-based on-line exercise ECG signal orthogonalization’,IEEE Trans.,BME-46, pp. 311–321Google Scholar
  3. ALGRA, A., Le Brun, H., andZeelenberg, C. (1987): ‘An algorithm for computer measurement of QT intervals in the 24-hour ECG’. Computers in Cardiology '86 Proceedings, Los Alamitos, pp. 117–119Google Scholar
  4. Aufderheide, T. P., Reddy, S., Xue, Q., Dhala, A., Thakur, R. K., Brady, W. J., andRowlandson, I. (1997): ‘QT dispersion and principal component analysis in prehospital patients with chest pain’. Computers in Cardiology '97 Proceedings, Lund, Sweden, pp. 665–668Google Scholar
  5. Barr, R. C., Spach, M. S., andHerman-Giddens, G. S. (1971): ‘Selection of the number and positions of measuring locations for electrocardiography’,IEEE Trans.,BME-18, pp. 125–138Google Scholar
  6. Bhullar, H. K., Chia, P., Ong, K., andNg, W. L. (1996): ‘Assessment of inter-observer and intra-observer variability in the measurement of QT dispersion’. Computers in Cardiology '96 Proceedings, Indianapolis, USA, pp. 297–300Google Scholar
  7. Buja, G., Miorelli, M., Turrini, P., Melacini, P., andNava, A. (1993): ‘Comparison of QT dispersion in hypertrophic cardiomyopathy between patients with and without ventricular arrhythmias and sudden death’,Am. J. Cardiol.,27, pp. 973–976CrossRefGoogle Scholar
  8. Day, C. P., McComb, J. M., andCampbell, R. W. F. (1990): ‘QT dispersion: An indication of arrhythmic risk in patients with long QT intervals’,Br. Heart J.,63, pp. 342–344CrossRefGoogle Scholar
  9. Dritsas, A., Puri, S., Davis, G., Krikler, S., Cleland, J., Nihoyannopoulos, C. M., andOakley, C. M. (1993): ‘QT dispersion is increased in hypertrophic cardiomyopathy compared with secondary left ventricular hypertrophy’,Eur. Heart J.,14, (suppl.), p. 212 (Abstract)Google Scholar
  10. Evans, A. K., Lux, R. L., Burgess, M. J., Wyatt, R. F., andAbildskov, J.A. (1981): ‘Redundancy reduction for improved display and analysis of body surface potential maps: II. Temporal compression’,Circ. Res.,49, pp. 197–203Google Scholar
  11. Glancy, J. M., Garrat, C. J., Woods, K. L., andDe Bono, D. P. (1995): ‘QT dispersion and mortality after myocardial infarction’,Lancet,345, pp. 945–948CrossRefGoogle Scholar
  12. Golub, G. H., andvan Loan, C. F. (1996): ‘Matrix computations’ 3rd edn. (The Johns Hopkins University Press, Baltimore and London) pp. 70–71Google Scholar
  13. Han, J., andMoe, G. K. (1964): ‘Non-uniform recovery of excitability in ventricular muscle’,Circ. Res.,4, pp. 44–60Google Scholar
  14. Higham, P. D., Furniss, S. S., andCampbell, R. W. F. (1995): ‘QT dispersion and components of the QT interval in ischemia and infarction’,Br. Heart J.,73, pp. 32–36CrossRefGoogle Scholar
  15. Hnatkova, K., Malik, M., Kautzner, J., Gang, Y., andCamm, A. J. (1994a): ‘Adjustment of QT dispersion assessed from 12-lead electrocardiograms for different numbers of analyzed electrocardiographic lead: Comparison of stability of different methods’,Br. Heart J.,72, pp. 390–396CrossRefGoogle Scholar
  16. Hnatkova, K., Poloniecki, J. D., Camm, A. J., andMalik, M. (1994b): ‘Computation of multifactorial receiver operator and predictive accuracy characteristics’,Comp. Meth. Prog. Biomed.,42, pp. 147–156CrossRefGoogle Scholar
  17. Kautzner, J., Kishore, R., Camm, A. J., andMalik, M. (1995): ‘The role of QT dispersion for prediction of ventricular arrhythmias after myocardial infarction’,Eur. Heart J.,16, (suppl.), p. 135 (Abstract)Google Scholar
  18. Kors, J. A., de Bruyne, M. C., Hoes, A. W., van Herpen, G., Hofman, A., van Bemmel, J. H., andGrobbee, D. E. (1998): ‘T axis as an indicator of risk of cardiac events in elderly people’,The Lancet,352, pp. 601–605CrossRefGoogle Scholar
  19. Kuo, C. S., Munakata, K., Reddy, C. P., andSurawicz, B. (1983): ‘Characteristics and possible mechanisms of ventricular arrhythmia dependent on the dispersion of action potential durations’,Circulation,67, pp. 1356–1367Google Scholar
  20. Laguna, P., Thakor, N. V., Caminal, P., Jane, R., Yoon, H. R., andBayes De Luna, A., Marti, V., andGuindo, I. (1990): ‘New algorithm for QT interval analysis in 24-hour Holter ECG: Performance and applications’,Med. Biol. Eng. Comput.,28, pp. 67–73CrossRefGoogle Scholar
  21. Laguna, P., Moody, G. B., Jane, R., Caminal, P., andMark, R. G. (1995): ‘Karhunen-Loeve transform as a tool to analyze the ST-segment’,J. Electrocardiol.,28, (suppl.), pp. 41–49CrossRefGoogle Scholar
  22. Lux, R. L., Evans, A. K., Burgess, M. J., Wyatt, R. F., andAbildskov, J. A. (1981): ‘Redundancy reduction for improved display and analysis of body surface potential maps: I. Spatial compression’,Circ. Res.,49, pp. 186–196Google Scholar
  23. MacFarlane, P. W., McLaughlin, S. C., andRodger, J. C. (1998): ‘Influence of lead selection and population on automated measurement of QT dispersion’,Circulation,98, pp. 2160–2167Google Scholar
  24. Malfatto, G., Beria, G., Sala, S., Bonazzi, O., andSchwartz, P. J. (1994): ‘Quantitative analysis of T-wave abnormalities and their prognostic implications in the idiopathic long QT syndrome’,J. Am. Coll. Cardiol.,23, pp. 296–301CrossRefGoogle Scholar
  25. Mann, H. B., andWhitney, D. R. (1947): ‘On a test of whether one of two random variables is stochastically larger than the other’,Ann. Math. Statistics,18, pp. 50–60MATHCrossRefMathSciNetGoogle Scholar
  26. McLaughlin, N. B., Campbell, R. W. F., andMurray, A. (1995): ‘Comparison of automatic QT measurement techniques in the normal 12-lead electrocardiogram’,Br. Heart J.,74, pp. 84–89CrossRefGoogle Scholar
  27. McLaughlin, N. B., Campbell, R. W. F., andMurray, A. (1996): ‘Accuracy of four automatic QT measurement techniques in cardiac patients and healthy subjects’,Heart,76, pp. 422–426CrossRefGoogle Scholar
  28. Murray, A., McLaughlin, N. B., Bourke, J. P., Doig, J. C., Furniss, S. S., andCampbell, R. W. F. (1994): ‘Errors in manual measurement of QT intervals’,Br. Heart J.,71, pp. 386–390CrossRefGoogle Scholar
  29. Murray, A., andMcLaughlin, N. B. (1995): ‘Variation in the identification of Q-wave initiation and its contribution to QT measurement’,Physiol. Meas.,16, pp. 39–42CrossRefGoogle Scholar
  30. Murray, A., McLaughlin, N. B., andCampbell, R. W. F. (1997): ‘Measuring QT dispersion: Man versus machine’,Heart,77, pp. 539–542Google Scholar
  31. Padrini, R., Butrous, G., Camm, A. J., andMalik, M. (1995): ‘Algebraic decomposition of the TU wave morphology patterns’,Pacing Clin. Electrophys.,18, pp. 2209–2215CrossRefGoogle Scholar
  32. Priori, S. G., Napolitano, C., Diehl, L., andSchwartz, P. J. (1994): ‘Dispersion of the QT interval: A marker of therapeutic efficacy in the idiopathic long QT syndrome’,Circulation,89, pp. 1681–1689Google Scholar
  33. Priori, S. G., Diehl, L., Napolitano, C., Paganini, V., andSchwartz, P. J. (1995): ‘Criteria to assess intervention-related changes in QT and QT dispersion: A study in subjects with normal and prolonged QT intervals’,Circulation,92, p. 1–681 (Abstract)Google Scholar
  34. Priori, S. G., Mortara, D. W., Napolitano, C., Diehl, L., Paganini, V., Cantu, F., Cantu, G., andSchwartz, P. J. (1997): ‘Evaluation of the spatial aspects of T-wave complexity in the long-QT syndrome’,Circulation,96, pp. 3006–3012Google Scholar
  35. Rowlandson, G. I. (1986): ‘The Marquette 12SL program’,inWillems, J. L., vanBemmel, J. H., andZyweitz, C. (Eds.): ‘Computer ECG analysis: towards standardization’ (North-Holland, Amsterdam) p. 49Google Scholar
  36. Statters, D. J., Malik, M., Ward, D. E., andCamm, A. J. (1994): ‘QT dispersion: Problems of methodology and clinical significance’,J. Cardiovasc. Electrophysiol.,5, pp. 672–685CrossRefGoogle Scholar
  37. Surawicz, B. (1995): ‘The electrophysiologic basis of ECG and cardiac arrhythmias’ (Williams and Wilkins, Baltimore)Google Scholar
  38. Wei, K., Doria, P., Newman, D., andLanger, A. L. (1995): ‘Association between QT dispersion and autonomic dysfunction in patients with diabetes mellitus’,J. Am. Coll. Cardiol.,26, pp. 859–863CrossRefGoogle Scholar
  39. Xue, Q., andReddy, S. (1996): ‘New algorithms for QT dispersion analysis’. Computers in Cardiology '96 Proceedings, Indianapolis, USA, pp. 293–296Google Scholar
  40. Zareba, W., Moss, A. J., andLecessie, S. (1994): ‘Dispersion of ventricular repolarization and arrhythmic cardiac death in coronary-artery disease’,Am. J. Cardiol.,74, pp. 550–553CrossRefGoogle Scholar

Copyright information

© IFMBE 1999

Authors and Affiliations

  1. 1.Department of Cardiological SciencesSt George's Hospital Medical SchoolLondonUK

Personalised recommendations