KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
Learning Structural Knowledge from the ECG
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Relation-based aggregation: finding objects in large spatial datasets
Intelligent Data Analysis
Spatial aggregation: theory and applications
Journal of Artificial Intelligence Research
Spatial aggregation: language and applications
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A knowledge-based approach to ECG interpretation using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Knowledge Engineering Review
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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.