An Automated Tool for the Detection of Electrocardiographic Diagnostic Features based on Spatial Aggregation and Computational Geometry

In this work we focus on Electrocardiographic diagnosis based on epi-cardial activation fields. The identification, within an activation map, of specific patterns that are known to characterize classes of pathologies provides an important support to the diagnosis of rhythm disturbances that can be missed by routine low resolution ECGs. Through an approach grounded on the integration of a Spatial Aggregation (SA) method with concepts borrowed from Computational Geometry, we propose a computational framework to automatically extract, from input epicardial activation data, a few basic features that characterize the wave-front propagation, as well as a more specific set of diagnostic features that identify an important class of rhythm pathologies due to block of conduction.