Rotor Tracking Using Phase of Electrograms Recorded During Atrial Fibrillation

Caroline H Roney,Norman A Qureshi,Emmanuel Dupont,Chris D Cantwell,Fu Siong Ng,Phang Boon Lim,Nicholas S Peters,Rasheda A Chowdhury,Edward J Vigmond,Jennifer H Tweedy

doi:10.1007/s10439-016-1766-4

Caroline H Roney, Norman A Qureshi + Show 8 more

Open Access

https://doi.org/10.1007/s10439-016-1766-4

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Abstract

Extracellular electrograms recorded during atrial fibrillation (AF) are challenging to interpret due to the inherent beat-to-beat variability in amplitude and duration. Phase mapping represents these voltage signals in terms of relative position within the cycle, and has been widely applied to action potential and unipolar electrogram data of myocardial fibrillation. To date, however, it has not been applied to bipolar recordings, which are commonly acquired clinically. The purpose of this study is to present a novel algorithm for calculating phase from both unipolar and bipolar electrograms recorded during AF. A sequence of signal filters and processing steps are used to calculate phase from simulated, experimental, and clinical, unipolar and bipolar electrograms. The algorithm is validated against action potential phase using simulated data (trajectory centre error <0.8 mm); between experimental multi-electrode array unipolar and bipolar phase; and for wavefront identification in clinical atrial tachycardia. For clinical AF, similar rotational content (R2 = 0.79) and propagation maps (median correlation 0.73) were measured using either unipolar or bipolar recordings. The algorithm is robust, uses standard signal processing techniques, and accurately quantifies AF wavefronts and sources. Identifying critical sources, such as rotors, in AF, may allow for more accurate targeting of ablation therapy and improved patient outcomes.

Highlights

Electrogram signals measured during cardiac fibrillation are inherently complex, with multi-component deflections and often limited temporal organisation
We have presented the first published methodology appropriate for calculating phase of both unipolar and bipolar electrogram data from simulation, experimental or clinical recordings
The algorithm is validated against action potential phase using simulated data; between experimental multi-electrode array unipolar and bipolar phase; and for wavefront identification in clinical atrial tachycardia recordings

Summary

Introduction

Electrogram signals measured during cardiac fibrillation are inherently complex, with multi-component deflections and often limited temporal organisation. Phase mapping transforms them from direct measurements of voltage over time to signals that capture the wavefront dynamics through the activation-recovery cycle of the underlying tissue by elucidating periodicity not immediately apparent in the raw signal. One advantage of phase maps derived from spatially distributed electrogram signals is that centres of rotational activity manifest clearly as phase singularities.[16]. Identifying these phase singularities can be used to locate the cores of spiral waves, which are a proposed mechanism underlying cardiac fibrillation. Voltage data are recorded in the form of unipolar or bipolar electrograms. Recent ablation approaches using a non-invasive mapping technology, which reconstructs unipolar

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