Reconstruction of high-resolution atrial conduction heterogeneity maps from unipolar electrograms
J.F.J. Verweij (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R.C. Hendriks – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
V. Yildirim – Graduation committee member (Erasmus MC)
R.F. Remis – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Cardiac arrhythmias affect millions of people and can cause life-threatening complications, such as heart failure, stroke and sudden cardiac death. Understanding of atrial activation characteristics at high spatial resolution is key to uncovering the mechanisms and substrate driving supra-ventricular arrhythmias, such as atrial fibrillation.
In practice, the resolution is limited by the physical constraints of electrode array placed on the cardiac surface. Due to the spatial extent of the sensing capabilities of the electrodes, inter-electrode activation characteristics are captured in their temporal signal morphology, allowing each electrode to provide a unique perspective on the same depolarization wavefront. Therefore, in this thesis, a spatial-temporal physiology based algorithm, Morphology Based Substrate Mapping (MBSM), is presented, solving an inverse source problem to reconstruct inter-electrode activation characteristics by jointly processing these electrode perspectives.
Epicardial unipolar electrograms were recorded using a rectangular electrode array with 2mm inter-electrode spacing on the right atrial wall. To validate the reconstruction method, in-silico datasets were created by simulating anatomically realistic atrial tissue slabs, providing ground-truth local activation times and substrate characteristics for quantitative validation.
The conducted simulations showed the ability of the MBSM algorithm to effectively reconstruct propagation of the depolarization wavefront with a fourfold improvement in spatial resolution (0.5 mm), yielding sub-millisecond mean absolute errors in inter-electrode local activation times. Furthermore, the reconstructed high-resolution activation strength maps enabled improved identification of regions exhibiting low conductivity and fibrosis by revealing characteristics not visible in the low-resolution clinical scenario, pointing toward improved delineation of ablation targets and evaluation of their effectiveness.
Finally, the low computational complexity, suggested noise resilience and automatic suppression of ventricular far-field signals indicate that the method is suitable for implementation in mapping systems, enabling quasi-real-time generation of high resolution conduction heterogeneity maps.
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