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Enhanced modified moving average analysis of T-wave alternans using a curve matching method: a simulation study

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Abstract

T-wave alternans (TWA) are beat-to-beat amplitude oscillations in the T-waves of electrocardiograms (ECGs). Numerous clinical studies have demonstrated the link between these oscillations and ventricular arrhythmias. Several methods have been developed in recent years to detect and quantify this important feature. Most methods estimate the amplitude differences between pairs of consecutive T-waves. One such method is known as modified moving average (MMA) analysis. The TWA magnitude is obtained by means of the maximum absolute difference of even and odd heartbeat series averages computed at T-waves or ST–T complexes. This method performs well for different levels of TWA, noise, and phase shifts, but it is sensitive to the alignment of the T-waves. In this paper we propose a preprocessing stage for the MMA method to ensure an optimal alignment of such averages. The alignment is performed by means of a continuous time warping technique. Our assessment study demonstrates the improved performance of the proposed algorithm.

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References

  1. Burattini L, Zareba W, Couderc J, Titlebaum E, Moss A (1997) Computer detection of non-stationary t wave alternans using a new correlation method. In: Proceedings of computers in cardiology, vol 24. pp 657–660

  2. Cuesta-Frau D, Biagetti MO, Quinteiro RA, Micó-Tormos P, Aboy M (2007) Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching. Med Biol Eng Comput 44(3):229–239

    Article  Google Scholar 

  3. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215

    Google Scholar 

  4. Janusek D, Kania M, Maniewski R (2006) Effect of electrocardiogram signal quality on t-wave alternans measurements: a simulation study. In: Proceedings of computers in cardiology, vol 33. pp 505–508

  5. Kaufman ES, Mackall JA, julka B, Drabek C, Rosenbaum DS (2000) Influence of heart rate and sympathetic stimulation on arrhythmogenic t wave alternans. Am J Physiol Heart Circ Physiol 279(3):1248–1255

    Google Scholar 

  6. Martínez J, Olmos S, Laguna P (2000) T wave alternans detection: A simulation study and analysis of european st-t database. In: Proceedings of computers in cardiology, vol 27. pp 155–158

  7. Martínez JP, Olmos S (2005) Methodological principles of t wave alternans analysis: a unified framework. IEEE Trans Biomed Eng 52(4):599–613

    Google Scholar 

  8. McSharry PE, Clifford GD, Tarassenko L, Smith LA (2003) A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng 50(3):289–294

    Article  Google Scholar 

  9. Munich ME, Perona P (1999) Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification. In: Proceedings of 7th international conference on computer vision

  10. Narayan S (2006) T-wave alternans and the susceptibility to ventricular arrhythmias. J Am Coll Cardiol 47(2):269–281

    Google Scholar 

  11. Nearing BD, Verrier RL (2002) Modified moving average analysis of t-wave alternans to predict ventricular fibrillation with high accuracy. J Appl Physiol 92:541–549

    Google Scholar 

  12. Nearing BD, Huang A, RL V (1991) Dynamic tracking of cardiac vulnerability by complex demodulation of the t wave. Science 252(5004):437–440

    Article  Google Scholar 

  13. Serinagaoglu Y, Sabuncuoglu D, Ider Y (1996) Spectral analysis of t wave alternans signal. In: Proceedings of 18th annual international conference of the IEEE engineering in medicine and biology society, pp 1353–1354

  14. Shusterman V, Goldberg A (2004) Tracking repolarization dynamics in real-life data. J Electrocardiol 37:180–186

    Article  Google Scholar 

  15. Strumillo P, Ruta J (2002) Poincaré mapping for detecting abnormal dynamics of cardiac repolarization. IEEE Eng Med Biol 21(1):62–65

    Google Scholar 

  16. Tomasi G, van den Berg F, Anderson C (2004) Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. J Chemomet 18(5):231–241

    Google Scholar 

  17. Verrier RL, Kwaku KF, Nearing BD, Josephson ME (2005a) T-wave alternans: does size matter? J Cardiovasc Electrophysiol 16(6):625–628

    Article  Google Scholar 

  18. Verrier RL, Nearing BD, Kwaku KF (2005b) Noninvasive sudden death risk stratification by ambulatory ecg-based t-wave alternans analysis: evidence and methodological guidelines. ANE 10(1):110–120

    Google Scholar 

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Correspondence to D. Cuesta-Frau.

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Cuesta-Frau, D., Micó-Tormos, P., Aboy, M. et al. Enhanced modified moving average analysis of T-wave alternans using a curve matching method: a simulation study. Med Biol Eng Comput 47, 323–331 (2009). https://doi.org/10.1007/s11517-008-0415-y

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  • DOI: https://doi.org/10.1007/s11517-008-0415-y

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