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A Principal Component Regression Approach for Estimating Ventricular Repolarization Duration Variability

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 058358 (2007) Cite this article

Abstract

Ventricular repolarization duration (VRD) is affected by heart rate and autonomic control, and thus VRD varies in time in a similar way as heart rate. VRD variability is commonly assessed by determining the time differences between successive R- and T-waves, that is, RT intervals. Traditional methods for RT interval detection necessitate the detection of either T-wave apexes or offsets. In this paper, we propose a principal-component-regression- (PCR-) based method for estimating RT variability. The main benefit of the method is that it does not necessitate T-wave detection. The proposed method is compared with traditional RT interval measures, and as a result, it is observed to estimate RT variability accurately and to be less sensitive to noise than the traditional methods. As a specific application, the method is applied to exercise electrocardiogram (ECG) recordings.

References

  1. Merri M, Moss AJ, Benhorin J, Locati EH, Alberti M, Badilini F: Relation between ventricular repolarization duration and cardiac cycle length during 24-hour Holter recordings: findings in normal patients and patients with long QT syndrome. Circulation 1992,85(5):1816-1821.

    Article  Google Scholar 

  2. Zareba W, de Luna AB: QT dynamics and variability. The Annals of Noninvasive Electrocardiology 2005,10(2):256-262. 10.1111/j.1542-474X.2005.10205.x

    Article  Google Scholar 

  3. Berger RD: QT variability. Journal of Electrocardiology 2003,36(1):83-87.

    Article  Google Scholar 

  4. Negoescu R, Dinca-Panattescu S, Filcescu V, Ionescu D, Wolf S: Mental stress enhances the sympathetic fraction of QT variability in an RR-independent way. Integrative Physiological and Behavioral Science 1997,32(3):220-227. 10.1007/BF02688620

    Article  Google Scholar 

  5. Merri M, Alberti M, Moss AJ: Dynamic analysis of ventricular repolarization duration from 24-hour Holter recordings. IEEE Transactions on Biomedical Engineering 1993,40(12):1219-1225. 10.1109/10.250577

    Article  Google Scholar 

  6. Nollo G, Speranza G, Grasso R, Bonamini R, Mangiardi L, Antolini R: Spontaneous beat-to-beat variability of the ventricular repolarization duration. Journal of Electrocardiology 1992,25(1):9-17. 10.1016/0022-0736(92)90124-I

    Article  Google Scholar 

  7. Laguna P, Thakor NV, Caminal P, et al.: New algorithm for QT interval analysis in 24-hour Holter ECG: performance and applications. Medical and Biological Engineering and Computing 1990,28(1):67-73. 10.1007/BF02441680

    Article  Google Scholar 

  8. Porta A, Baselli G, Lombardi F, et al.:Performance assessment of standard algorithms for dynamic R-T interval measurement: comparison between R- and R- approach. Medical and Biological Engineering and Computing 1998,36(1):35-42. 10.1007/BF02522855

    Article  Google Scholar 

  9. Tikkanen PE, Sellin LC, Kinnunen HO, Huikuri HV: Using simulated noise to define optimal QT intervals for computer analysis of ambulatory ECG. Medical Engineering and Physics 1999,21(1):15-25. 10.1016/S1350-4533(99)00018-1

    Article  Google Scholar 

  10. Davey PP: interval measurement: to or to ? Journal of Internal Medicine 1999,246(2):145-149. 10.1046/j.1365-2796.1999.00553.x

    Article  Google Scholar 

  11. Savelieva I, Yi G, Guo X-H, Hnatkova K, Malik M: Agreement and reproducibility of automatic versus manual measurement of QT interval and QT dispersion. The American Journal of Cardiology 1998,81(4):471-477. 10.1016/S0002-9149(97)00927-2

    Article  Google Scholar 

  12. Ireland RH, Robinson RTCE, Heller SR, Marques JLB, Harris ND: Measurement of high resolution ECG QT interval during controlled euglycaemia and hypoglycaemia. Physiological Measurement 2000,21(2):295-303. 10.1088/0967-3334/21/2/309

    Article  Google Scholar 

  13. Speranza G, Nollo G, Ravelli F, Antolini R: Beat-to-beat measurement and analysis of the R-T interval in 24 h ECG Holter recordings. Medical and Biological Engineering and Computing 1993,31(5):487-494. 10.1007/BF02441984

    Article  Google Scholar 

  14. Yan G-X, Antzelevitch C: Cellular basis for the normal T wave and the electrocardiographic manifestations of the long-QT syndrome. Circulation 1998,98(18):1928-1936.

    Article  Google Scholar 

  15. Pan J, Tompkins WJ: A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering 1985,32(3):230-236.

    Article  Google Scholar 

  16. Jolliffe IT: Principal Component Analysis. Springer, New York, NY, USA; 1986.

    Book  Google Scholar 

  17. Tarvainen MP, Ranta-Aho PO, Karjalainen PA: An advanced detrending method with application to HRV analysis. IEEE Transactions on Biomedical Engineering 2002,49(2):172-175. 10.1109/10.979357

    Article  Google Scholar 

  18. Bailón R, Mateo J, Olmos S, et al.: Coronary artery disease diagnosis based on exercise electrocardiogram indexes from repolarisation, depolarisation and heart rate variability. Medical and Biological Engineering and Computing 2003,41(5):561-571. 10.1007/BF02345319

    Article  Google Scholar 

  19. Merri M, Benhorin J, Alberti M, Locati E, Moss AJ: Electrocardiographic quantitation of ventricular repolarization. Circulation 1989,80(5):1301-1308. 10.1161/01.CIR.80.5.1301

    Article  Google Scholar 

  20. Mateo J, Serrano P, Bailón R, et al.: Heart rate variability measurements during exercise test may improve the diagnosis of ischemic heart disease. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '01), October 2001, Istanbul, Turkey 1: 503–506.

    Google Scholar 

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Authors and Affiliations

  1. Department of Physics, University of Kuopio, P.O. Box 1627, Kuopio, 70211, Finland

    Mika P. Tarvainen, Juha-Pekka Niskanen & Pasi A. Karjalainen

  2. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, P.O. Box 1777, Kuopio, 70211, Finland

    Tomi Laitinen & Tiina Lyyra-Laitinen

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  1. Mika P. Tarvainen
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  2. Tomi Laitinen
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  3. Tiina Lyyra-Laitinen
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  4. Juha-Pekka Niskanen
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  5. Pasi A. Karjalainen
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Correspondence to Mika P. Tarvainen.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Tarvainen, M.P., Laitinen, T., Lyyra-Laitinen, T. et al. A Principal Component Regression Approach for Estimating Ventricular Repolarization Duration Variability. EURASIP J. Adv. Signal Process. 2007, 058358 (2007). https://doi.org/10.1155/2007/58358

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