6533b862fe1ef96bd12c74ef

RESEARCH PRODUCT

Combining Biophysical Modeling and Machine Learning to Predict Location of Atrial Ectopic Triggers

subject

description

The search for focal ectopic activity in the atria triggered from non-standard regions can be time consuming. The use of body surface potential maps to plan the intervention can be helpful, but require an advance processing of the data, that usually involves to solve an ill-posed inverse problem. In addition, changes in maps due to pathological substrate such as fibrosis might affect the expected electrical patterns. In this work, we use a machine learning approach to relate ectopic focus activity in different atrial regions with body surface potential maps, and consider the effects of fibrosis in various densities and distributions. Results show that as fibrosis increases over 15% the systems has to increase the region size in which an ectopic focus should be searched, but keeps the performance over 90% when at least 64 electrodes are used.