Abstract
Analyzing electrocardiogram (ECG) recordings is crucial to gathering useful information about a patient’s heart condition in a noninvasive manner, with the consequent benefits in procedural time, cost and risk of complications relative to invasive diagnostic modalities. Signal processing can aid cardiologists to make informed decisions by revealing and quantifying underlying structures that may not be apparent in the observed data, especially when the redundancy inherent to the ECG leads hinders the expert’s analysis. This chapter considers two such ECG signal processing problems, namely, T-wave alternans detection and atrial activity estimation during atrial fibrillation. The redundancy present in the ECG can effectively be exploited by decomposing the observed data into interesting signals or components that are often easier to analyze than the original recording. These components can be determined as appropriate linear combinations of the observed data according to different criteria such as principal component analysis (PCA) and independent component analysis (ICA). While PCA relies on second-order statistics and yields uncorrelated components, ICA achieves independence through the use of higher-order statistics. The main concepts as well as some advantages, limitations and success stories of these popular decomposition techniques are illustrated on real ECG data from the above cardiac signal processing problems.
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References
A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, and E. Moulines. A blind source separation technique using second-order statistics. IEEE Transactions on Signal Processing, 45(2):434–444, February 1997.
A. Bollmann and F. Lombardi. Electrocardiology of atrial fibrillation. IEEE Engineering in Medicine and Biology Magazine, 25(6):15–23, November/December 2006.
P. Bonizzi, M. S. Guillem, A. M. Climent, J. Millet, V. Zarzoso, F. Castells, and O. Meste. Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation. IEEE Transactions on Biomedical Engineering, 57(9):2147–2157, September 2010.
L. Burattini, W. Zareba, and R. Burattini. The effect of baseline wandering in automatic T-wave alternans detection from Holter recordings. In Proc. Computers in Cardiology, volume 33, pages 257–260, Valencia, Spain, Sept. 17–20, 2006.
F. Castells, P. Laguna, L. Sörnmo, A. Bollmann, and J. Millet Roig. Principal component analysis in ECG signal processing. EURASIP Journal on Advances in Signal Processing, 2007:21 pages, 2007.
F. Castells, J. J. Rieta, J. Millet, and V. Zarzoso. Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias. IEEE Transactions on Biomedical Engineering, 52(2):258–267, February 2005.
P. Comon. Independent Component Analysis. In J-L. Lacoume, editor, Higher Order Statistics, pages 29–38. Elsevier, Amsterdam, London, 1992.
P. Comon. Contrasts, independent component analysis, and blind deconvolution. Int. Journal Adapt. Control Sig. Proc., 18(3):225–243, April 2004.
P. Comon and C. Jutten, editors. Handbook of Blind Source Separation, Independent Component Analysis and Applications. Academic Press, Oxford, UK, 2010.
D. Donoho. On minimum entropy deconvolution. In Applied Time-Series Analysis II, pages 565–609. Academic Press, 1981.
V. Fuster, L. E. Rydén, D. S. Cannom, H. J. Crijns, A. B. Curtis, et al. ACC/AHA/ESC guidelines for the management of patients with atrial fibrillation – executive summary. Circulation, 114(7):700–752, 2006.
P. Jaïs, D. C. Shah, M. Hocini, L. Macle, K.-J. Choi, et al. Radiofrequency ablation for atrial fibrillation. European Heart Journal Supplements, 5(Supplement H):H34–H39, 2003.
K. T. Konings, C. J. Kirchhof, J. R. Smeets, H. J. Wellens, O. C. Penn, and M. A. Allessie. High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4):1665–1680, April 1994.
J. Malmivuo and R. Plonsey. Bioelectromagnetism: Principles and Applications. Oxford University Press, New York, 1995.
J. P. Martínez and S. Olmos. Methodological principles of T wave alternans analysis: a unified framework. IEEE Transactions on Biomedical Engineering, 52(4):599–613, April 2005.
O. Meste, D. Janusek, and R. Maniewski. Analysis of the T wave alternans phenomenon with ECG amplitude modulation and baseline wander. In Proc. Computers in Cardiology, volume 34, pages 565–568, Durham, NC, Sept. 30–Oct. 3, 2007.
O. Meste and N. Serfaty. QRST cancellation using Bayesian estimation for the auricular fibrillation analysis. In Proc. 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 7083–7086, Shanghai, China, Sept. 1–4, 2005.
R. Phlypo, V. Zarzoso, and I. Lemahieu. Atrial activity estimation from atrial fibrillation ECGs by blind source extraction based on a conditional maximum likelihood approach. Medical & Biological Engineering & Computing, 48(5):483–488, May 2010.
R. Phlypo, V. Zarzoso, and I. Lemahieu. Source extraction by maximizing the variance in the conditional distribution tails. IEEE Transactions on Signal Processing, 58(1):305–316, January 2010.
J. J. Rieta, F. Castells, C. Sánchez, V. Zarzoso, and J. Millet. Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Transactions on Biomedical Engineering, 51(7):1176–1186, July 2004.
J. J. Rieta, V. Zarzoso, J. Millet-Roig, R. García-Civera, and R. Ruiz-Granell. Atrial activity extraction based on blind source separation as an alternative to QRST cancellation for atrial fibrillation analysis. In Proc. Computers in Cardiology, volume 27, pages 69–72, Boston, MA, Sept. 24–27, 2000.
M. Stridh and L. Sörnmo. Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Transactions on Biomedical Engineering, 48(1):105–111, January 2001.
J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319–2323, December 2000.
B. Widrow, J. R. Glover, J. M. McCool, et al. Adaptive noise cancelling: principles and applications. Proceedings of the IEEE, 63(12):1692–1716, December 1975.
V. Zarzoso. Extraction of ECG characteristics using source separation techniques: exploiting statistical independence and beyond. In A. Naït-Ali, editor, Advanced Biosignal Processing, chapter 2, pages 15–47. Springer, Berlin, 2009.
V. Zarzoso and P. Comon. Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size. IEEE Transactions on Neural Networks, 21(2):248–261, February 2010.
V. Zarzoso, R. Phlypo, O. Meste, and P. Comon. Signal extraction in multisensor biomedical recordings. In P. Verdonck, editor, Advances in Biomedical Engineering, chapter 3, pages 95–143. Elsevier BV, Oxford, UK, 2009.
Acknowledgements
Part of the work summarized in this chapter is supported by the French National Research Agency under contract ANR 2010 JCJC 0303 01 “PERSIST”.
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Zarzoso, V., Meste, O., Comon, P., Latcu, D.G., Saoudi, N. (2013). Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques. In: Cazals, F., Kornprobst, P. (eds) Modeling in Computational Biology and Biomedicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31208-3_3
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