Model-based interpretation of cardiac beats by evolutionary algorithms: signal and model interaction.
This paper presents a new approach for cardiac beat interpretation, based on a direct integration between a model and observed ECG signals. Physiological knowledge is represented by means of a semi-quantitative model of the cardiac electrical activity. The interpretation of cardiac beats is formalized as an optimization problem, by minimizing an error function defined between the model's output and the observations. Evolutionary algorithms (EAs) are used as the search technique in order to obtain the set of model parameters reproducing at best the observed phenomena. Examples of model adaptation to three different kinds of cardiac beats are presented. Preliminary results show the potentiality of this approach to reproduce and explain complex pathological disorders and to better localize their origin.
Hernández
Alfredo I
AI
Laboratoire Traitement du Signal et de l'Image, Université de Rennes 1, Campus de Beaulieu Bât 22, 35042 Rennes, France. alfredo.hernandez@univ-rennes.fr
Carrault
Guy
G
Mora
Fernando
F
Bardou
Alain
A
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004562:Electrocardiography; D006801:Humans; D012815:Signal Processing, Computer-Assisted