Abstract
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.
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© 1999 Springer-Verlag Berlin Heidelberg
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Carrault, G., Cordier, MO., Quiniou, R., Garreau, M., Bellanger, J.J., Bardou, A. (1999). A Model-Based Approach for Learning to Identify Cardiac Arrhythmias. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_18
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DOI: https://doi.org/10.1007/3-540-48720-4_18
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