KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Learning Structural Knowledge from the ECG
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Automated detection of qualitative spatio-temporal features in electrocardiac activation maps
Artificial Intelligence in Medicine
Spatial aggregation: theory and applications
Journal of Artificial Intelligence Research
A knowledge-based approach to ECG interpretation using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
Abstract: Functional imaging plays an important role in the assessment of organ functions, as it provides methods to represent the spatial behavior of diagnostically relevant variables within reference anatomical frameworks. The salient physical events that underly a functional image can be unveiled by appropriate feature extraction methods capable to exploit domain-specific knowledge and spatial relations at multiple abstraction levels and scales. In this work we focus on general feature extraction methods that can be applied to cardiac activation maps, a class of functional images that embed spatio-temporal information about the wavefront propagation. The described approach integrates a qualitative spatial reasoning methodology with techniques borrowed from computational geometry to provide a computational framework for the automated extraction of basic features of the activation wavefront kinematics and specific sets of diagnostic features that identify an important class of rhythm pathologies.