Abstract
Blind Source Separation (BSS) has been probed as one of the most effective techniques for atrial activity (AA) extraction in supraventricular tachyarrhythmia episodes like atrial fibrillation (AF). In these situations, a wavelet transform denoising stage can improve the extraction quality with low computational cost. Each ECG lead is processed to obtain its representation in the wavelet domain where the BSS systems improve their performance. The comparison of spectral parameters (main peak and power spectral density concentration) and statistics values (kurtosis) proves that the sparse decomposition in the wavelet domain of the observed mixtures reduces Gaussian contamination of these signals, speeds up the convergence and increase the quality of the extracted signal. The easy and fast implementation, robustness and efficiency are some of the main advantages of this technique making possible the application in real time systems as a support tool to clinical diagnostics.
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References
Falk, R.H.: Medical progress: Atrial Fibrillation. New England Journal of Medicine 344(14), 1067–1078 (2001)
Igbal, M.B., Taneja, A.K., Lip, G.Y., Flather, M.: Recent developments in atrial fibrillation. BMJ 330, 238–243 (2005)
Rieta, J.J., Castells, F., Sánchez, C., Zarzoso, V., Millet, J.: Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Trans. Biomed. Eng. 51(7), 1176–1186 (2004)
Castells, F., Rieta, J.J., Millet, J., Zarzoso, V.: Spatiotemporal blind source separation approach to atrial activity estimation in atril tachyarrhythmias. IEEE Trans. Biomed. Eng. 52(2), 258–267 (2005)
Rieta, J.J., Sänchez, C., Sanchis, J.M., Castells, F., Millet, J.: Mixing Matrix Pseudostationarity and ECG Preprocessing Impact on ICA-Based Atrial Fibrillation Analysis. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 1079–1086. Springer, Heidelberg (2004)
Jafari, M., Chambers, J.: Fetal electrocardiogram extraction by sequential source separation in the wavelet domain. IEEE Trans. Biomed. Eng. 52(3), 390–400 (2005)
Sänchez, C., Rieta, J.J., Castells, F., Alcaraz, R., Millet, J.: Wavelet Domain Blind Signal Separation to Analyze Supraventricular Arrhtymias from Holter Registers. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 1111–1117. Springer, Heidelberg (2004)
Sarela, J., Valpola, H.: Denoising source separation. Journal of Machine Learning Research 6, 233–272 (2005)
Addison, P.S.: The Illustrated Wavelet Transform Handbook. Introductory Theory and Applications in Science, Engineering, Medicine and Finance. Institute of Physics Publishing (2002)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorthms and Applications. John Wiley and Sons, Chichester (2003)
Rieta, J.J., Castells, F., Sänchez, C., Moratal-Pérez, D., Millet, J.: Bioelectric model of atrial fibrillation: Applicability of blind source separation techniques for atrial activity estimation in atrial fibrillation episodes. Computers in Cardiology 30, 525–528 (2003)
Zibulesvsky, M., Zeevi, Y.Y.: Extraction of a source from a multichannel data using sparse decomposition. Neurocomputing 49(1), 163–173 (2002)
Zibulevsky, M., Pearlmutter, B.A., Bofill, P., Kisilev, P.: Blind source separation by sparse decomposition. In: Roberts, S.J., Everson, R.M. (eds.) Independent Components Analysis: Principles and Practice. Cambridge University Press, Cambridge (2001)
Pham, D., Cardoso, J.F.: Blind separation of instantaneous mixtures of non stationary sources. IEEE Transactions on Signal Processing 49(9), 1837–1848 (2001)
Sänchez, C., Rieta, J.J., Vayä, C., Moratal, D., Cervigön, R., Blas, J.M., Millet, J.: Atrial Activity Enhancement by Blind Sparse Sequential Separation. Computers in Cardiology 32 (2005) (in press)
Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 10, 626–634 (1999)
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Sánchez, C., Rieta, J.J., Vayá, C., Perez, D.M., Zangróniz, R., Millet, J. (2006). Wavelet Denoising as Preprocessing Stage to Improve ICA Performance in Atrial Fibrillation Analysis. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_61
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DOI: https://doi.org/10.1007/11679363_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
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