Atrial Fibrillation (AF) is the most common supraventricular arrhythmia and has different underlying activation mechanism, including functional rotors (FR) and ectopic foci (EF). In this work we propose an approach for locating FR and EF in potential maps, which were tested with mathematical phantoms. 12 phantoms were created (128x128 array, 4 s, Fs 500 Hz), simulating the motion of: FR (4 maps), EF (4 maps) and superpositions of these (4 maps). These were downsampled to different grids from 16x16 to 8x8, simulating electrode acquisition. Noise (SNR from 2 to 60) was added. To locate the mechanism, the signals were filtered and interpolated. Farneback optical flow was applied to compute the motion vector field (MVF). The MVF was normalized and its temporal average was calculated. Finally, we computed curl and divergence, by using a x and y oriented 11 × 11 Sobel filter as a estimation of the of the partial derivatives. The location of extrema in the curl and divergence maps were used for locating FR and EF respectively. Method robustness was tested by comparing the algorithm performance on different grid sizes and SNR. The mechanism was considered to be detected accurately if the position was within a normalized error of 5% from its respective phantom location. The results showed that our approach was able to locate both mechanisms, but revealed a dependency on spatial resolution.

Atrial fibrillation (AF) is a common supraventricular arrhythmia (SVA) in clinical practice and is characterized by uncoordinated electrical activity of the atria. This study aims to evaluate the influence on the forward solution of AF torso biomarkers under different levels of noise, 3D cardiorespiratory torso/atria morphologies, and number of atria electrodes. 2,048 atrial epicardium electrograms (AEGs) from 5 AF mathematical models were used to estimate 771 body surface potentials (BSPs). The BSPs and respective frequency/phase maps of are obtained after: (i) introduction of noise in the AEGs, (ii) 3D geometry torso/atria modification, and (iii) reduction in electrodes (from 2,048 to 256, 128, 64 e 32; interpolation methods: Linear/Laplacian). To reduce biomarkers disparity, a Butterworth bandpass filter (BPF) at different cut-off frequencies is applied on the AEGs prior BSPs estimation. The above methodology is extended to two AF patients (EDGAR database). The estimation of AF BSPs, in different noise ranges, limits the effectiveness of the forward solution. Phase biomarkers are sensitive to the AEGs' pre-processing strategy. The BPF around HDF showed the best agreement between the different SNR levels. Due to the 3D morphological changes, HDF areas variability increased.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia increasing the risk of stroke in fivefold. Improving knowledge at its development and prevention is crucial for AF patient management. This study aimed to predict the thrombogenesis in left atrium (LA) under AF by a multi-physics approach coupling a 3D transient profile of realistic electrophysiological activity, oscillatory mechanical effects and a biochemical model for thrombogenesis. The local mechanical effects from detailed AF activity disturbed the blood flow pattern, resulting in pro-thrombotic zones in left atrial appendage apex, in contrast with simplified AF model (rigid walls), that could lead to overestimation of pro-thrombotic zones.1.