Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice, and is becoming a major public health concern. To better understand the mechanisms of this arrhythmia an accurate analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary. The block term decomposition (BTD), a tensor factorization technique, has been recently proposed as a tool to extract the AA in ECG signals using a blind source separation (BSS) approach. This paper makes a deep analysis of the sources estimated by BTD, showing that the classical method to select the atrial source among the other sources may not work in some cases, even for the matrix-based methods. In this context, we propose two new automated methods to select the atrial source by considering another novel parameter. Experimental results on ten patients show the validity of the proposed methods.
P. M. R. de Oliveira—Funded by a Ph.D. scholarship from the IT Doctoral School of the Université Côte d’Azur.
V. Zarzoso—Member of the Institut Universitaire de France.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
January, C.T., Wann, L.S., Alpert, J.S., Calkins, H., Cleveland, J.C., Cigarroa, J.E., Conti, J.B., et al.: 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. Circulation 64(21), 2246–2280 (2014)
Mainardi, L., Sörnmo, L., Cerutti, S.: Understanding atrial fibrilation: the signal processing contribution. Synthesis Lectures on Biomedical Engineering. Morgan & Claypool Publishers (2008)
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 atrial tachyarrhythmias. IEEE Trans. Biomed. Eng. 52(2), 258–267 (2005)
Zarzoso, V.: Extraction of ECG characteristics using source separation techniques: exploiting statistical independence and beyond. In: Naït-Ali, A. (ed.) Advanced Biosignal Processing, pp. 15–47. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-89506-0_2
Ribeiro, L.N., Hidalgo-Muñoz, A.R., Zarzoso, V.: Atrial signal extraction in atrial fibrillation electrocardiograms using a tensor decomposition approach. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 25–29 August 2015, pp. 6987–6990 (2015)
Ribeiro, L.N., Hidalgo-Muñoz, A.R., Favier, G., Mota, J.C.M., de Almeida, A.L.F., Zarzoso, V.: A tensor decomposition approach to noninvasive atrial activity extraction in atrial fibrillation ECG. In: Proceedings of the XXIII European Signal Processing Conference, EUSIPCO-2015, Nice, France, 31 August–4 September 2015, pp. 2576–2580 (2015)
Ribeiro, L.N., de Almeida, A.L.F., Zarzoso, V.: Enhanced block term decomposition for atrial activity extraction in atrial fibrillation ECG. In: Proceedings of the 9th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM-2016, Rio de Janeiro, Brazil, 10–13 July 2016
Zarzoso, V.: Parameter estimation in block term decomposition for noninvasive atrial fibrillation analysis. In: Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP-2017, Curaçao, Dutch Antilles, 10–13 December 2017
Zarzoso, V., Comon, P.: Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size. IEEE Trans. Neural Netw. 21(2), 248–261 (2010)
Jolliffe, I.: Principal Component Analysis. Wiley Online Library (2005)
De Lathauwer, L.: Blind separation of exponential polynomials and the decomposition of a tensor in rank-(\(l_r\), \(l_r\), \(1\)) terms. SIAM J. Matrix Anal. Appl. 32(4), 1451–1474 (2011)
Boley, D.L., Luk, F.T., Vandevoorde, D.: Vandermonde factorization of a Hankel matrix. In: Proceedings of the Workshop on Scientific Computing, Hong Kong, March 1997
Vervliet, N., Debals, O., Sorber, L., Van Barel, M., De Lathauwer, L.: Tensorlab 3.0, March 2016. https://www.tensorlab.net/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
de Oliveira, P.M.R., Zarzoso, V. (2018). Source Analysis and Selection Using Block Term Decomposition in Atrial Fibrillation. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-93764-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93763-2
Online ISBN: 978-3-319-93764-9
eBook Packages: Computer ScienceComputer Science (R0)