Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology

Abstract

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

Keywords: Atrial fibrillation; Cardiac implantable electronic devices; EHRA position paper; Machine learning; Mapping; Signal processing; Signal recording.

Figure 1

Left panel: U-EGMs and corresponding Bi-EGM demonstrating the relation between the peak-to-peak amplitudes. Right panel: U-EGMs and corresponding Bi-EGMs, demonstrating that U-EGMs do not always result in ‘simple’ non-fractionated Bi-EGMs. On the other hand, fractionated U-EGM may give rise to non-fractionated Bi-EGM. However, an increase in fractionation complexity of U-EGM is associated with an increase in complexity of Bi-EGM. By courtesy of Mathijs van Schie. Bi-EGM, bipolar EGM; EGM, electrograms; U-EGM, unipolar EGM.

Figure 2

(A) Cliques enclosed by four electrodes are used to record three U-EGM (filter: 5–400 Hz) visualized in the top of (C). The U-EGMs of three adjacent electrodes (1,2, and 3) are used to derive Bi-EGM by subtracting one U-EGM from the other U-EGM such that two pairs of Bi-EGMs (1–2 and 2–3) are constructed along the horizontal (red) and vertical (green) directions. Bi-EGMs are filtered (30–400 Hz) and visualized in the centre of (C). Both Bi-EGMs are used to describe a depolarization wavefront as an electrical field which is electrode orientation-independent. (B) Illustrates the projections along the time-axis of the electrical field derived from both Bi-EGMs. This enables to mathematically obtain Bi-EGMs in any direction without physically rotating a sensing electrode. The E-field is subsequently scaled to analogous 2D voltage signals from which the maximal extent over the interval (T) is calculated and corresponds to the peak-to-peak amplitude of a Bi-EGM obtained along a unit vector direction. (C) Resulting omnipolar EGM, (D) corresponding Laplacian EGM. By courtesy of Mathijs van Schie. Bi-EGM, bipolar EGM; EGM, electrograms; U-EGM, unipolar EGM.

Figure 3

Challenges encountered with annotation of potentials recorded during AF. (A) Red dots indicate the different time samples. Annotation of the steepest deflection can be calculated by e.g. averaging the steepest deflection of all time samples, selecting time samples with the steepest deflection, or averaging between maximum and minimum values. This information is usually not provided in manuals or in methodology sections of scientific reports. (B) In case of multiple deflection with comparable slopes and amplitudes, additional criteria have to be developed to determine local activation times (LAT). (C) As a result of endo-epicardial asynchrony, endocardial LATs may be different from epicardial LATs. (D) Determination of LAT is affected by the filter settings which has a considerable impact on U-EGM morphology. AF, atrial fibrillation; U-EGM, unipolar EGM.

Figure 4

High resolution maps of the left atrial wall (N=192, inter-electrode distance 2 mm) constructed during AF obtained from a patient during cardiac surgery. These maps demonstrate from the left to the right: activation times combined with isochrones, local conduction directions, conduction directions and magnitude of conduction velocities, peak-to-peak voltages. By courtesy of Mathijs van Schie. AF, atrial fibrillation.

Figure 5

Upper panel: simulation of excitation of the right and left atrium. Lower panel: body surface maps of the right and left atrium based on simulated—and measured activation times constructed during sinus rhythm with an 80-channel active electrode system (ActiveTwo, BioSemi, Amsterdam, The Netherlands).

Figure 6

Schematic illustration of the use of an open source imaging toolkit for panoramic optical mapping, as described by Gloschat et al. (A) Experimental optical mapping setup, including Langendorff-perfused heart. (B) Heart image with superimposed silhouette (yellow) derived via an automated thresholding process. (C) Data projection points for reconstruction of panoramic maps of optically mapped data. (D) Examples of optically mapped action potentials recorded from the epicardial surface of a rat heart, including annotations for activation and 80% repolarization times. (E and F) Spatial reconstructions of activation time (E) and 80% action potential duration (F) from representative rat panoramic optical data. Images reproduced from Figure 1 (A–C) and Figure 7 (D–F) of Gloschat et al. under the terms of the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.