Background: Reentrant circuits are considered to be critically dependent on a single protected slow conducting isthmus.
Objective: The purpose of this study was to investigate conduction properties and electrogram (EGM) characteristics of the entire circuit in localized atrial reentrant circuits using high-resolution mapping.
Methods: Fifteen localized reentrant atrial tachycardias were studied with high-resolution mapping (Rhythmia). EGMs along the entire circuit were analyzed offline for fractionation, duration, and amplitude. Maps were exported to MATLAB (MathWorks) to measure bipolar voltage and conduction velocities (CVs) within the circuit. Slow conduction was defined as <30 cm/s.
Results: Fifteen localized re-entrant circuits (12 left atrial, 3 right atrial) with mean cycle length 273 ± 40 ms were analyzed using high-resolution maps (22,389 ± 13,375 EGMs). A mean of 4.5 ± 1.6 slow conduction corridors were identified per circuit. Although the entire circuit was of low voltage, the bipolar voltage in slow conducting corridors was significantly lower than the rest of the circuit (0.22 ± 0.20 mV vs 0.50 ± 0.48 mV; P <.001). The mean conduction velocity of the circuit, excluding slow conduction areas, was 90.3 ± 34.3 cm/s vs 13.9 ± 3.5 cm/s (P <.001) in the slow conduction corridors. EGM analysis at the slowest conduction corridors demonstrated fractionation (100%) with longer EGM duration compared to the other slow conduction corridors along the circuit (99 ± 9 ms vs 74 ± 11 ms; P = .003).
Conclusion: In contrast to current understanding, localized atrial reentrant circuits have multiple sequential "corridors" of very slow conduction (2-7) that contribute to maintenance of arrhythmia. The localized reentry occurs in low-voltage areas, with voltage further reduced in these multiple slow conducting corridors.
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http://dx.doi.org/10.1016/j.hrthm.2018.11.027 | DOI Listing |
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Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain.
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