Details

Autor(en) / Beteiligte

• Campos, Fernando O.
• Wiener, Thomas
• Prassl, Anton J.
• dos Santos, Rodrigo Weber
• Sanchez-Quintana, Damian
• Ahammer, Helmut
• Plank, Gernot
• Hofer, Ernst

Titel

Electroanatomical Characterization of Atrial Microfibrosis in a Histologically Detailed Computer Model

Ist Teil von

• IEEE transactions on biomedical engineering, 2013-08, Vol.60 (8), p.2339-2349

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2013

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Fibrosis is thought to play an important role in the formation and maintenance of atrial fibrillation (AF). The propensity of fibrosis to increase AF vulnerability depends not only on its amount, its texture plays a crucial role as well. While the detection of fibrotic tissue patches in the atria with extracellular recordings is feasible based on the analysis of electrogram fractionation, as used in clinical practice to identify ablation targets, the classification of fibrotic texture is a more challenging problem. This study seeks to establish a method for the electroanatomical characterization of the fibrotic textures based on the analysis of electrogram fractionation. The proposed method exploits the dependence of fractionation patterns on the incidence direction of wavefronts which differs significantly as a function of texture. A histologically detailed computer model of the right atrial isthmus was developed for testing the method. A stimulation protocol was conceived which generated various incidence directions for any given recording site where electrograms were computed. A classification method is derived then for discriminating three types of fibrosis, no fibrosis (control), diffuse, and patchy fibrosis. Simulation results showed that electrogram fractionation and amplitudes and their dependence upon incidence direction allow a robust discrimination between different classes of fibrosis. Finally, to minimize the technical effort, sensitivity analysis was performed to identify a minimum number of incidence directions required for robust classification.