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.
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