Unified neural network based pathologic event reconstruction using spatial heart model

  • Authors:

  • Sándor M. Szilágyi;László Szilágyi;Attila Frigy;Levente K. Görög;Zoltán Benyó

  • Affiliations:

  • Sapientia-Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-Mureş, Romania;Sapientia-Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-Mureş, Romania and Budapest University of Technology and Economics, Dept. of Control ...;County Medical Clinic, Târgu-Mureş, Romania;Sapientia-Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-Mureş, Romania;Budapest University of Technology and Economics, Dept. of Control Engineering and Information Technology, Budapest, Hungary

  • Venue:
  • CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.