KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
Qualitative-numeric simulation with Q3
Recent advances in qualitative physics
Model-based interpretation of the ECG: a methodology for temporal and spatial reasoning
Computers and Biomedical Research - Papers presented at the 16th symposium on computer applications in medical care (SCAMC)
An interactive qualitative model in cardiology
Computers and Biomedical Research
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
On Multi-class Problems and Discretization in Inductive Logic Programming
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Temporal Scenario Recognition for Intelligent Patient Monitoring
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
On using causal knowledge to recognize vital signals: knowledge-based interpretation of arrhythmias
IJCAI'85 Proceedings of the 9th international joint 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
Medical Expert Evaluation of Machine Learning Results for a Coronary Heart Disease Database
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Learning Structural Knowledge from the ECG
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Artificial Intelligence in Medicine
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ECG interpretation is used to monitor the behavior of the electrical conduction system of the heart in order to diagnose rhythm and conduction disorders. In this paper, we propose a model-based framework relying on a model of the cardiac electrical activity. Due to efficiency constraints, the on-line analysis of the ECG signals is performed by a chronicle recognition system which identifies pathological situations by matching a symbolic description of the signals with temporal patterns stored in a chronicle base. The model can simulate arrhythmias and the related sequences of time-stamped events are collected and then used by an inductive learning program to constitute a satisfying chronicle base. This work is in progress but first results show that the system is able to produce satisfying discriminating chronicles.