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Characterization of f Waves

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Book cover Atrial Fibrillation from an Engineering Perspective

Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

This chapter reviews different approaches to f wave characterization. The two fundamental characteristics f wave amplitude and atrial fibrillatory rate are first considered, followed by a description of linear and nonlinear techniques for characterizing wave morphology and regularity. The analysis of spatial ECG information, manifested as a vectorcardiographic loop or a body surface potential map, is reviewed. The chapter concludes with a brief overview of popular clinical applications where the described approaches have been explored.

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Notes

  1. 1.

    For notational convenience, the extracted f wave signal is denoted x(n) in this chapter, replacing the notation \(\hat{d}(n)\) used in Chap. 5.

  2. 2.

    It may be noted that this procedure is closely related to the “sifting” procedure, which is part of empirical mode decomposition [33], where the lower and upper envelopes of the local extrema are used to compute the intrinsic mode functions.

  3. 3.

    Since the amplitude spectrum is analyzed in some studies, obtained as the square root of the power spectrum, caution should be exercised when comparing amplitude-related results.

  4. 4.

    When the data matrix \(\mathbf {X}\) is composed of overlapping segments, defined by a sliding window shifted with one sample at a time, \(R_K\) is known as the fractional spectral radius and used to quantify the stochastic complexity of a signal [57], see also [78].

  5. 5.

    The orthogonal leads X, Y, and Z can be synthesized from the 12-lead ECG using, for example, the inverse Dower matrix [126, 127], see also page 64.

  6. 6.

    Principal component analysis of 180-lead isopotential maps, recorded in sinus rhythm, was pursued already in 1964, but then motivated by the completely different question “What is the minimum number of leads which can contain all of the electrocardiographic information available on the body surface?” [151], see also [152]. In those studies, the data matrix was defined by one single isopotential map, while \(\mathbf {X}\) in (6.86) contains N maps. Thus, the former approach is purely spatial, while the approach in [80] may be labelled “spatiotemporal”.

  7. 7.

    It is somewhat remarkable that outcome prediction was based on manual f wave amplitude measurements in recent studies [4, 5], although algorithms for amplitude measurements have been available for many years.

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  1. Department of Biomedical Engineering and Center for Integrative Electrocardiology, Lund University, Lund, Sweden

    Leif Sörnmo

  2. Innovation in Bioengineering Research Group, University of Castilla–La Mancha, Campus Universitario, Cuenca, Spain

    Raúl Alcaraz

  3. Biomedical Signal Interpretation and Computational Simulation (BSICoS), Aragón Institute of Engineering Research (I3A), Centro de Investigacíon Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza University, Zaragoza, Spain

    Pablo Laguna

  4. Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Gandía, Spain

    José Joaquín Rieta

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Sörnmo, L., Alcaraz, R., Laguna, P., Rieta, J.J. (2018). Characterization of f Waves. In: Sörnmo, L. (eds) Atrial Fibrillation from an Engineering Perspective. Series in BioEngineering. Springer, Cham. https://doi.org/10.1007/978-3-319-68515-1_6

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