Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 502981, 10 pages
http://dx.doi.org/10.1155/2014/502981
Research Article

A Time-Domain Hybrid Analysis Method for Detecting and Quantifying T-Wave Alternans

1School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
2School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
3Biomedical Engineering Center, Beijing University of Technology, Beijing 100022, China

Received 28 November 2013; Accepted 5 January 2014; Published 3 April 2014

Academic Editor: Emil Alexov

Copyright © 2014 Xiangkui Wan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. R. Adam, S. Akselrod, and R. J. Cohen, “Estimation of ventricular vulnerability to fibrillation through T-wave time series analysis,” Computing in Cardiology, vol. 8, pp. 307–310, 1981. View at Google Scholar
  2. J. M. Smith, E. A. Clancy, C. R. Valeri, J. N. Ruskin, and R. J. Cohen, “Electrical alternans and cardiac electrical instability,” Circulation, vol. 77, no. 1, pp. 110–121, 1988. View at Google Scholar · View at Scopus
  3. B. D. Nearing, A. H. Huang, and R. L. Verrier, “Dynamic tracking of cardiac vulnerability by complex demodulation of the T wave,” Science, vol. 252, no. 5004, pp. 437–440, 1991. View at Google Scholar · View at Scopus
  4. P. Laguna, M. Ruiz, and G. B. Moody, “Repolarization alternans detection using the KL transform and the beatquency spectrum,” Computing in Cardiology, vol. 23, pp. 673–676, 1996. View at Google Scholar
  5. B. D. Nearing and R. L. Verrier, “Modified moving average analysis of T-wave alternans to predict ventricular fibrillation with high accuracy,” Journal of Applied Physiology, vol. 92, no. 2, pp. 541–549, 2002. View at Google Scholar · View at Scopus
  6. L. Burattini, W. Zareba, and A. J. Moss, “Correlation method for detection of transient T-wave alternans in digital holter ECG recordings,” Annals of Noninvasive Electrocardiology, vol. 4, no. 4, pp. 416–424, 1999. View at Google Scholar · View at Scopus
  7. L. Burattini, S. Bini, and R. Burattini, “Comparative analysis of methods for automatic detection and quantification of microvolt T-wave alternans,” Medical Engineering and Physics, vol. 31, no. 10, pp. 1290–1298, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. V. Monasterio, P. Laguna, and J. P. Martínez, “Multilead analysis of T-wave alternans in the ecg using principal component analysis,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 7, pp. 1880–1890, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. J. P. Martínez and S. Olmos, “Methodological principles of T wave alternans analysis: a unified framework,” IEEE Transactions on Biomedical Engineering, vol. 52, pp. 599–613, 2005. View at Google Scholar
  10. L. Burattini, W. Zareba, and R. Burattini, “Automatic detection of microvolt T-wave alternans in Holter recordings: effect of baseline wandering,” Biomedical Signal Processing and Control, vol. 1, no. 2, pp. 162–168, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Burattini, W. Zareba, and R. Burattini, “Adaptive match filter based method for time vs. amplitude characterization of microvolt ECG T-wave alternans,” Annals of Biomedical Engineering, vol. 36, no. 9, pp. 1558–1564, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. C. R. Meyer and H. N. Keiser, “Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques,” Computers and Biomedical Research, vol. 10, no. 5, pp. 459–470, 1977. View at Publisher · View at Google Scholar · View at Scopus
  13. J. P. Martínez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, “A wavelet-based ECG delineator evaluation on standard databases,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, pp. 570–581, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. S. M. Narayan and J. M. Smith, “Spectral analysis of periodic fluctuations in electrocardiographic repolarization,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 2, pp. 203–212, 1999. View at Publisher · View at Google Scholar · View at Scopus
  15. R. L. Verrier, T. Klingenheben, M. Malik et al., “Microvolt T-wave alternans: physiological basis, methods of measurement, and clinical utilityconsensus guideline by international society for Holter and noninvasive Electrocardiology,” Journal of the American College of Cardiology, vol. 58, no. 13, pp. 1309–1324, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. J. P. Martínez, S. Olmos, G. Wagner, and P. Laguna, “Characterization of repolarization alternans during ischemia: time-course and spatial analysis,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 4, pp. 701–711, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. G. B. Moody, “The physionet / computers in cardiology challenge 2008: T-wave alternans,” Computers in Cardiology, vol. 35, pp. 505–508, 2008. View at Google Scholar
  18. L. Burattini, S. Bini, and R. Burattini, “Correlation method versus enhanced modified moving average method for automatic detection of T-wave alternans,” Computer Methods and Programs in Biomedicine, vol. 98, no. 1, pp. 94–102, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Burattini, S. Bini, and R. Burattini, “Automatic microvolt T-wave alternans identification in relation to ECG interferences surviving preprocessing,” Medical Engineering and Physics, vol. 33, no. 1, pp. 17–30, 2011. View at Publisher · View at Google Scholar · View at Scopus