Electrocardiographic Imaging: Towards Automated Interpretation of Activation Maps (Contributo in atti di convegno)

Type
Label
  • Electrocardiographic Imaging: Towards Automated Interpretation of Activation Maps (Contributo in atti di convegno) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Alternative label
  • Liliana Ironi; Stefania Tentoni (2005)
    Electrocardiographic Imaging: Towards Automated Interpretation of Activation Maps
    in 10th Conference on Artificial Intelligence in Medicine AIME2005
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Liliana Ironi; Stefania Tentoni (literal)
Pagina inizio
  • 323 (literal)
Pagina fine
  • 332 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Artificial Intelligence in Medicine (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3581 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 3581 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
  • Mathematical Reviews on the web (MathSciNet) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di matematica applicata e tecnologie informatiche (literal)
Titolo
  • Electrocardiographic Imaging: Towards Automated Interpretation of Activation Maps (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 3-540-27831-1 (literal)
Abstract
  • In present clinical practice, information about the heart electrical activity is routinely gathered through ECG's, which record electrical potential from just nine sites on the body surface. However, thanks to the latest technological advances, body surface potential maps are becoming available, as well as epicardial maps obtained noninvasively from body surface data through mathematical model-based reconstruction methods. Such maps can capture a number of electrical conduction pathologies that can be missed by ECG's analysis. But, their interpretation requires skills that are possessed by very few experts. The Spatial Aggregation (SA) approach can play a crucial role in the identification of patterns and salient features in the map, and in the long-term goal of delivering an automated map interpretation tool to be used in a clinical context. In this paper, the focus is on epicardial activation isochrone maps. The salient features that characterize the heart electrical activity, and visually correspond to specific geometric patterns, are defined, extracted from the epicardial electrical data, and finally made available in an interpretable form within a SA-based framework. (literal)
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