Automated detection of qualitative spatio-temporal features in electrocardiac activation maps

  • Authors:

  • Liliana Ironi;Stefania Tentoni

  • Affiliations:

  • Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, via Ferrata 3, 27100 Pavia, Italy;Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, via Ferrata 3, 27100 Pavia, Italy

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

Objective: This paper describes a piece of work aiming at the realization of a tool for the automated interpretation of electrocardiac maps. Such maps can capture a number of electrical conduction pathologies, such as arrhytmia, that can be missed by the analysis of traditional electrocardiograms. But, their introduction into the clinical practice is still far away as their interpretation requires skills that belongs to very few experts. Then, an automated interpretation tool would bridge the gap between the established research outcome and clinical practice with a consequent great impact on health care. Methods and material: Qualitative spatial reasoning can play a crucial role in the identification of spatio-temporal patterns and salient features that characterize the heart electrical activity. We adopted the spatial aggregation (SA) conceptual framework and an interplay of numerical and qualitative information to extract features from epicardial maps, and to make them available for reasoning tasks. Results: Our focus is on epicardial activation isochrone maps as they are a synthetic representation of spatio-temporal aspects of the propagation of the electrical excitation. We provide a computational SA-based methodology to extract, from 3D epicardial data gathered over time, (1) the excitation wavefront structure, and (2) the salient features that characterize wavefront propagation and visually correspond to specific geometric objects. Conclusion: The proposed methodology provides a robust and efficient way to identify salient pieces of information in activation time maps. The hierarchical structure of the abstracted geometric objects, crucial in capturing the prominent information, facilitates the definition of general rules necessary to infer the correlation between pathophysiological patterns and wavefront structure and propagation.