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Applications of Nonlinear Methods to Atrial Fibrillation

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Complexity and Nonlinearity in Cardiovascular Signals

Abstract

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, affecting roughly 1.5–2% of the general population and around 15% of the elderly. Despite its high prevalence, mechanisms triggering and maintaining the arrhythmia are not fully known, thus making current treatments often ineffective. Nonetheless, in last years intensive research in signal processing applied both to surface electrocardiogram (ECG) and intracardiac electrogram (EGM) recordings has resulted in an improved knowledge of this cardiac disorder. Indeed, by taking advantage of the fact that the heart during AF behaves in an inherently complex, heterogeneous and non-stationary way, relevant advances has been reached by nonlinear analysis of that recordings. The main goal of this chapter is hence to review the most significant achievements in some important AF facets, such as identification of events associated to its natural progression, accurate quantification of atrial substrate electrophysiological features and customization of current therapies.

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Acknowledgements

Research funded by grants TEC2014-52250-R from the Spanish Ministry of Economy and Competitiveness and PPII–2014–026–P from Junta de Comunidades de Castilla-La Mancha.

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  1. Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Cuenca, Spain

    Raúl Alcaraz

  2. BioMIT.org Electronic Engineering Department, Universitat Politécnica de Valencia, Gandía, Spain

    José J. Rieta

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    Riccardo Barbieri

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    Gaetano Valenza

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Alcaraz, R., Rieta, J.J. (2017). Applications of Nonlinear Methods to Atrial Fibrillation. In: Barbieri, R., Scilingo, E., Valenza, G. (eds) Complexity and Nonlinearity in Cardiovascular Signals. Springer, Cham. https://doi.org/10.1007/978-3-319-58709-7_15

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