Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms

Abstract

Background: Phase mapping has become a broadly used technique to identify atrial reentrant circuits for ablative therapy guidance. This work studies the phase mapping process and how the signal nature and its filtering affect the reentrant pattern characterization in EGM, Body Surface Potential Mapping (BSPM) and Electrocardiographic Imaging (ECGI) signals. Methods and Results: EGM, BSPM and ECGI phase maps were obtained from 17 simulations of atrial fibrillation (AF), atrial flutter (AFL) and focal atrial tachycardia (AT). Reentrant activity was identified by singularity point recognition in raw signals and in signals after narrow band-pass filtering at the Highest Dominant Frequency (HDF). Reentrant activity was dominantly present in the EGM recordings only for AF and some AFL propagations patterns, and HDF filtering allowed increasing the reentrant activity detection from 60% to 70% of time in AF in unipolar recordings and from 0% to 62% in bipolar. In BSPM maps, HDF filtering increased from 10% to 90% the sensitivity, although provoked a residual false reentrant activity ~30% of time. In ECGI, HDF filtering allowed to increase up to 100% the time with detected rotors, although provoked the apparition of false rotors during 100% of time. Nevertheless, raw ECGI phase maps presented reentrant activity just in AF recordings accounting for ~80% of time. Conclusions: Rotor identification is accurate and sensitive and does not require additional signal processing in measured or noninvasively computed unipolar EGMs. Bipolar EGMs and BSPM do require HDF filtering in order to detect rotors at the expense of a decreased specificity.

Rotors

“…4 On phase maps, a rotor is defined as a phase singularity point around which the phase transitions through a complete cycle from -π to +π. 3, 5 Phase mapping can falsely detect phase singularities in the absence of rotors, 6 and spiral waves can exist without phase singularities. 7 Rotors and focal impulses have been proposed to be the underlying drivers of atrial fibrillation (AF) in human.…”

“…In elegant work published in this issue of the Journal , Rodrigo et al 6 made a large stride to address the technical limitations associated with rotor mapping using ECGI. Rotor mapping is a complex multi-step process that involves data acquisition, filtering, phase mapping, and rotor detection algorithms (Figure 1).…”

“…Furthermore, Kuklik et al showed that rotor detection is most accurate with a double-ring electrode configuration, comprising of 2×2 and 4×4 electrodes, 28 suggesting that rotor detection also depends on electrode configuration. The simulated left atrium of Rodrigo et al 6 contains a total of 2,048 electrodes over the epicardial surface with an inter-electrode distance of 1 mm. Unfortunately, a technology providing such high-density, high-resolution mapping is not available in clinical practice at this point.…”

“…With the work of Rodrigo et al 6 , we are one step closer to testing the localized source hypothesis as a potential mechanism of human AF. Future studies to address other aspects of technical limitations associated with rotor detection (Figure 1) would further advance our understanding of human AF and help expand our therapeutic armamentarium.…”

Effects of Reducing the Number of Leads from Body Surface Potential Mapping in Computer Models of Atrial Arrhythmias

“…A realistic three-dimensional model of the atrial anatomy composed by 284, 578 nodes and 1, 353, 783 tetrahedrons (673.4±130.3 µm between nodes) was used to simulate the electrical behavior of the atria in arrhythmic conditions. 19 simulations from arrhythmias caused by three distinct mechanisms were generated: AT (4) driven by an ectopic focus; AFL (4) driven by a macroreentrant circuit; AF (11) driven by functional rotors [5]. BSPMs were obtained by solving the forward problem with the boundary element method (sampling frequency = 500 Hz), resulting in 771 data points, of which 567 were selected by excluding points inside the waist, neck, and arms (high resolution -HR).…”