This paper presents a new way to solve the inverse problem of
electrocardiography in terms of heart model parameters. The developed event
estimation and recognition method is based on an optimization system of
heart model parameters. An ANN-based preliminary ECG analyzer system has
been created to reduce the searching space of the optimization algorithm.
The optimal model parameters were determined by minimizing the objective
functions, as relations of the observed and model-generated body surface
ECGs. The final evaluation results, validated by physicians were about
86\% correct. Starting from the fact that input ECGs contained various
malfunction cases, such as Wolff-Parkinson-White (WPW) syndrome, atrial and
ventricular fibrillation, these results suggest that this approach provides
a robust inverse solution, circumventing most of the difficulties of the ECG
inverse problem
