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 uses a unified neural network (UNN)-based optimization system to determine the most relevant heart model parameters. A UNN-based preliminary ECG analyzer system has been created to reduce the searching space of the optimization algorithm. The optimal model parameters were determined by a relation between objective function minimization and robustness of the solution. The final evaluation results, validated by physicians, were about 96% 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 this approach provides a robust inverse solution, circumventing most of the difficulties of the ECG inverse problem. © 2007 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Szilágyi, S. M., Szilágyi, L., & Benyó, Z. (2007). Spatial heart simulation and analysis using unified neural network. Advances in Soft Computing, 41, 346–354. https://doi.org/10.1007/978-3-540-72432-2_35
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