Unified Neural Network Based Pathologic Event Reconstruction Using Spatial Heart Model

  • Sándor M. Szilágyi
  • László Szilágyi
  • Attila Frigy
  • Levente K. Görög
  • Zoltán Benyó
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


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.


Heart model unified neural network inverse ECG problem 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sándor M. Szilágyi
    • 1
  • László Szilágyi
    • 1
    • 2
  • Attila Frigy
    • 3
  • Levente K. Görög
    • 1
  • Zoltán Benyó
    • 2
  1. 1.Sapientia - Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-MureşRomania
  2. 2.Budapest University of Technology and Economics, Dept. of Control Engineering and Information Technology, BudapestHungary
  3. 3.County Medical Clinic No. 4, Târgu-MureşRomania

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