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Detection of Atrial Fibrillation

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Atrial Fibrillation from an Engineering Perspective

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

Abstract

In this chapter, the main design principles used in detection of atrial fibrillation are reviewed, either exploring rhythm information only or information on both rhythm and atrial wave morphology. Aspects on detector implementation are briefly considered, and the pros and cons of different detection performance measures are discussed.

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Notes

  1. 1.

    Paroxysmal AF manifested by episodes briefer than 30 s is sometimes referred to as occult paroxysmal AF, especially when asymptomatic or undetected by conventional methods [7,8,9,10,11].

  2. 2.

    Several other detectors have been proposed besides those listed in Table 4.1. However, for various reasons, their respective performance was not evaluated on AFDB.

  3. 3.

    The relationship between histogram shape and mean heart rate has previously been investigated in noninvasive studies on atrioventricular node physiology in AF, leading to the concept of heart rate stratified histograms [53, 54].

  4. 4.

    It should be noted that \(\hat{C}(m,r)\) constitutes an essential part of the correlation dimension, a measure introduced to describe the dimensionality of the space occupied by a set of random samples [68].

  5. 5.

    Yet another approach proposed for characterizing the Poincaré plot is the complex correlation measure, quantifying the point-to-point (temporal) variation of the RR series [74], see also [75]. However, this measure has not received any attention in AF detection, probably because it is better suited for discriminating between ectopic rhythms and normal sinus rhythm than between AF and normal sinus rhythm.

  6. 6.

    In [50], the computation of \(\bar{m}_x\) includes all RR intervals in the window except the first and last RR intervals, i.e., x(0) and \(x(N-1)\); however, the interpretation of \(\bar{m}_x\) is similar to that otherwise used in this chapter.

  7. 7.

    Strictly speaking, this type of detector does not explore both rhythm and morphology. However, since information on P wave absence is still required, the detector is described in this section.

  8. 8.

    The idea of studying the deviation from “normality”, i.e., whether a P wave is absent, is closely related to the concept of novelty detection [102, 103].

  9. 9.

    While classification of beat morphology is not reviewed here, it deserves to be mentioned that this classification problem has received, and continues to receive, considerable attention in the literature, see, e.g., [116,117,118,119,120].

  10. 10.

    Neither is noise level taken into account in classification of beat morphology, even though it is well-known that certain beats are difficult to cluster due to excessive noise. This problem was indirectly addressed in [120], where an elegant technique based on switching Kalman filters was proposed for detecting “strange” beat morphologies falling outside the well-established clusters.

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

    Leif Sörnmo

  2. Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania

    Andrius Petrėnas & Vaidotas Marozas

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Sörnmo, L., Petrėnas, A., Marozas, V. (2018). Detection of Atrial Fibrillation. 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_4

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