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Automatic detection of the electrocardiogram T-wave end

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Abstract

Various methods for automatic electrocardiogram T-wave detection and Q-T interval assessment have been developed. Most of them use threshold level corrsing. Comparisons with observer detection were performed due to the lack of objective measurement methods. This study followed the same approach. Observer assessments were performed on 43 various T-wave shapes recorded: (i) with 100 mms−1 equivalent paper speed and 0.5mVcm−1 sensitivity; and (ii) with 160 mms−1 paper speed and vertical scaling ranging from 0.07 to 0.02 m Vcm−1, depending on the T-wave amplitude. An automatic detection algorithm was developed by adequate selection of the T-end search interval, improved T-wave peak detection and computation of the angle between two 10ms long adjacent segments along the search interval. The algorithm avoids the use of baseline crossin direct signal differentiation. It performs well in cases of biphasic and/or complex T-wave shapes. Mean differences with respect to observer data are 13.5 ms for the higher gain/speed records and 14.7 ms for the lower gain/speed records. The algorithm was tested with 254 various T-wave shapes. Comparisons with two other algorithms are presented. The lack of a ‘gold standard’ for the T-end detection, especially if small waves occur around it, impeded adequate interobserver assessment and evaluation of automatic methods. It is speculated that a simultaneous presentation of normal and high-gain records might turn more attention to this problem. Automatic detection methods are in fact faced with ‘high-gain’ data, as high-resolution analogue-to-digital conversion, is already widely used.

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References

  • Ahnve, S., Lundman, T. andShoaleh-Var, M. (1978): ‘The relationship between QT interval and primary ventricular fibrillation in acute myocardial infarction’,Acta Med. Scand.,204, pp. 17–19

    Article  Google Scholar 

  • Barr, C. S., Naas, A., Freeman, M., Lang, C. C. andStruthers, A. D. (1994): ‘QT dispersion and sudden death in chronic heart failure’,Lancet,343, pp. 327–329.

    Article  Google Scholar 

  • Cowan, J. C., Yasoff, K., Amos, P. A., Gold, A. E., Bourke, J. P., Tansuphaswadikul, S. andCampbell, R. W. F. (1988): ‘Importance of lead selection in QT interval measurement’,Am. J. Cardiol.,61, pp. 83–87

    Article  Google Scholar 

  • Day, C. P., McComb, J. M. andCampbell, R. W. F. (1990): ‘QT dispersion: an indication of arrhythmia risk in patients with long QT intervals’,Brit. Heart J.,63, pp. 342–344

    Article  Google Scholar 

  • Franz, M. (1994): ‘Time for yet another QT correction algorithm? Bazett and beyond’,J. Am. Coll. Cardiol.,23, pp. 1554–1556

    Article  Google Scholar 

  • Laguna, P., Thakor, N. V., Caminal, P., Jane, R., Yoon, H. R., Bayers De Luna, A., Marti, V. andGuindo, J. (1990): ‘New algorithm for QT interval analysis in 24-hour Holter ECG: performance and applications’,Med. Biol. Eng. Comput.,28, pp. 67–73

    Article  Google Scholar 

  • Lo, S. S. S., Mathias, C. J. andSutton, M. S. J. (1996): ‘QT interval and dispersion in primary autonomic failure’,Heart,75, pp. 498–501

    Article  Google Scholar 

  • 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. 84–89

    Article  Google Scholar 

  • Merri, M., Benhorin, J., Alberti, M., Locat, E. andMoss, A. J. (1989): ‘Electrocardiographic quantitation of ventricular repolarization’,Circulation,80, pp. 1301–1308

    Google Scholar 

  • Molnar, J., Zhang, F., Weiss, J., Ehlert, F. A. andRosenthal, J. E. (1996): ‘Diurnal pattern of QTc interval: how long is prolonged? Possible relation to circadian triggers of cardiovascular events’,J. Am. Coll. Cardiol.,27, pp. 76–83

    Article  Google Scholar 

  • Murray, A. andMcLaughlin, N. B. (1995): ‘Variation in the identification of Q wave initiation, and its contribution, to QT measurement’,Physiol. Meas.,16, pp. 39–42

    Article  Google Scholar 

  • 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. 386–390

    Article  Google Scholar 

  • Puddu, P. E., Jouve, R., Torresani, J. andJouve, A. (1981): ‘QT interval and primary ventricular fibrillation in acute myocardial infarction’,Am. Heart. J.,101, pp. 118–119

    Article  Google Scholar 

  • Puddu, P. E. andBourassa, M. G. (1986): ‘Prediction of sudden death from QTc interval prolongation in patients with chronic ischemic disease’,J. Electrocardiol.,19, pp. 1203–212

    Article  Google Scholar 

  • Statters, D. J., Malik, M., Ward, D. E. andCamm, J. (1994): ‘QT dispersion: problems of methodology and clinical significance’,J. Cardiovasc. Electrophysiol.,5, pp. 672–685

    Article  Google Scholar 

  • Xue, Q. andReddy, S. (1996): ‘New algorithm for QT dispersion analysis’,IEEE Comput. Cardiol., pp. 293–296

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Correspondence to I. K. Daskalov.

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Daskalov, I.K., Christov, I.I. Automatic detection of the electrocardiogram T-wave end. Med. Biol. Eng. Comput. 37, 348–353 (1999). https://doi.org/10.1007/BF02513311

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  • DOI: https://doi.org/10.1007/BF02513311

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