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Multidimensional ICA for the Separation of Atrial and Ventricular Activities from Single Lead ECGs in Paroxysmal Atrial Fibrillation Episodes

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

The analysis of paroxysmal atrial fibrillation requires the previous estimation of the atrial activity (AA) from one lead ECG. Considering the statistical properties of the cardiac electrical activities, it follows that both AA and ventricular activity (VA) present a high redundancy degree at different time intervals, whereas AA keeps independent from VA. This contribution adopts a multidimensional independent component analysis (MICA) formulation in order to find a set of components that minimises the mutual information existing in the ECG signal at different intervals. The independent components can be grouped in VA and AA subspaces, what enables the reconstruction of the AA at each observation point from the AA subspace. The proposed approach is validated with a significant database composed of simulated and real AF recordings.

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Author information

Authors and Affiliations

  1. Universidad Politécnica de Valencia, 46730, Gandia, Spain

    Francisco Castells, José Joaquín Rieta & Juan Manuel Sanchís

  2. Universidad Politécnica de Valencia, 46022, Valencia, Spain

    Cibeles Mora & José Millet

  3. Universidad de Castilla la Mancha, Cuenca, Spain

    César Sánchez

Authors
  1. Francisco Castells
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  2. Cibeles Mora
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  3. José Millet
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  4. José Joaquín Rieta
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  5. César Sánchez
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  6. Juan Manuel Sanchís
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Castells, F., Mora, C., Millet, J., Rieta, J.J., Sánchez, C., Sanchís, J.M. (2004). Multidimensional ICA for the Separation of Atrial and Ventricular Activities from Single Lead ECGs in Paroxysmal Atrial Fibrillation Episodes. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_155

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_155

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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