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Abstract-Atrial fibrillation is the most common cardiac arrhythmia, and it is associated with increased risk of stroke, heart failure and mortality. In this work we describe spectral analysis techniques that are being used in conjunction with visualization algorithms to help guide catheter ablation procedures that aim at treating patients with the arrhythmia. !Atrial Fibrillation (AF) is a serious problem as it can lead to stroke and heart failure, with increased mortality. To further complicate the problem, the precise electrical mechanisms underlying AF are not well understood. One effective treatment for AF is catheter ablation, whereby areas in the atria and/or nearby locations are targeted and ablated (or "burned"). However, results are variable, with a large number of patients requiring repeated procedures if AF recurs in the short term. Long term results are even less encouraging. One of the main issues with ablation is the decision on where to ablate that gives the maximum efficacy and safety. Improving understanding of the precise electrical mechanisms underlying AF is key to minimising the amount of "burning" with ablation and maximizing the gain. It is important that information is available to aid ablation decision and strategy either before or during the ablation procedure. Hence, techniques and technologies to characterize and map candidate locations for ablation need to be implemented in real-time. In this paper we describe a technique for mapping the dominant frequency (DF) of atrial electrograms and explore, implement and measure the processing time for several approaches for its implementation. Our solution leverages the parallel processing computation offered by multiple CPU cores, but more importantly, the massive parallel computational power available in current Graphic Processing Units (GPUs). We also describe techniques for visualizing the behavior of dominant frequency of intracardiac atrial electrograms. The visualization allows the mapping of the DFs using a color scale and isolating the main DF areas. We conclude that, with current technology and using an off-the-shelf, modern personal computer with a graphics card and costing about US$ 1,000 it is possible to implement real time DF mapping with a loading as low as 6.75% (up to 50% depending of the implementation strategy and the level of detail required). This changes the perspective of the problem from pure, basic research to translational, applied research and we propose an exciting new step forward in this important area. Atrial FibrillationMeasuring and modeling the genesis and propagation of the electrical activity in the heart in quantitative terms is a very important area of research that will help understand and treat heart arrhythmias. Atrial fibrillation (AF) is a heart rhythm disturbance characterized by uncoordinated and rapid electrical atrial activation which takes over from normal sinus rhythm, with consequent deterioration of the mechanical ability of the atria to pump blood effectively. The ventricles will beat irregu...

Figure 1: Four different views of a scene where agents navigate on the surface of a complex triangular mesh. Agents are color-coded by their different objectives. The system supports path planning of multiple agents on non-planar surfaces, and imposes no limitation on the domain mesh, such as this mesh with more than one genus. AbstractPath planning is an active topic in the literature, and efficient navigation over non-planar surfaces is an open research question. In this work we present a novel technique for navigation of multiple agents over arbitrary triangular domains. The proposed solution uses a fast hierarchical computation of geodesic distances over triangular meshes to allow interactive frame rates, and a GPU-based collision avoidance technique to guide individual agents. Unlike most previous work, the method imposes no limitations on the surface over which the agents are moving, and can naturally deal with non-planar meshes of arbitrary genus and curvature. Moreover, the implementation is a hybrid CPU/GPU algorithm that explores the current trend of increasing the number of CPU cores and GPU programmability. This approach exploits the best qualities in each processor, thus achieving very high performance.

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