Multi-source Motion Decoupling Ablation Catheter Guidance for Electrophysiology Procedures

Abstract

Abstract. Accurate and stable positioning of the ablation catheter tip during the delivery of radiofrequency impulses in cardiac electrophysiology remains a challenge due to the endocardium motion from multiple sources (cardiac cycle and respiration) and inevitable slippage of the catheter tip. This paper presents a novel ablation catheter guidance framework during electrophysiology procedures. Catheter tip electrode position readings from intraoperative electroanatomical data are used to decouple tip motion from different motion sources as part of the preablation mapping. The resulting information is then used to determine if there is relative slippage between the catheter tip and endocardial surface and is shown as a probability map for online decision support of the ablation process. The proposed decomposition method and the slippage assessment were performed on a retrospective cohort of 19 patients treated for ventricular tachycardia (13 cases) or atrial fibrillation (6 cases) and were also validated on artificially generated signals.

Probabilistic guidance for catheter tip motion in cardiac ablation procedures

“…Finally, this work extends the results in (Constantinescu et al, 2015) and the presented analytical framework can be used for procedural planning in patients 610 who require ablation in extremely unstable positions, but for whom alternative more stable neighbouring sites can be found.…”

“…Mode Decomposition (MEMD) (Rehman & Mandic, 2010;Mandic et al, 2013) was applied with better results than the other methods (Constantinescu et al, 2015). The method is an iterative process through which an intrinsic mode function (IMF) is extracted at each step using the so-called sifting process.…”

“…The present work is an extension of (Constantinescu et al, 2015), with sig-130 nificant changes and adaptations in the decomposition algorithm. It also eliminates the use of a Gaussian mixture model fit in the computation of slippage probabilities, thus deeming the probabilistic method more generalisable to non-Gaussian slippage components.…”

Traversed Graph Representation for Sparse Encoding of Macro-Reentrant Tachycardia

“…In terms of termination point detection, while part of the error can be traced back to the path reconstruction inaccuracy, the learning feature vector itself can be enhanced with information such as wall thickness and estimated catheter tip motion at each point on the path, as described by [10]. The importance of additional information can be inferred from Tab.…”