Dynamic modeling of heart dipole vector for the ECG and VCG generation

Fabio La Foresta, Nadia Mammone, Giuseppina Inuso, Francesco Carlo Morabito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The electrocardiogram (ECG) is the major diagnostic instrument for the analysis of cardiac electrophysiology; this is due to two simple reasons, first because it is not invasive and secondly because an ECG is a source of accurate information about the heart functionality. For these reasons, in the last years, the ECG has attracted the interest of many scientists, who have developed algorithms and models to investigate the cardiac disorders. The aim of this paper is to introduce a novel dynamic model to simulate pathologic ECGs. We discuss a generalization of a well known model for normal ECG signals generation and we show that it can be extended to simulate the effects on ECG of some cardiac diseases. We also represent the 3D vector trajectory of the cardiac cycle by reconstructing the heart dipole vector (HDV) from the Frank lead system. Finally, we propose to generate the complete 12-lead ECG system by the HDV projection. The results shows this a powerful tool for pathologic ECG generation, future research will be devoted to set up an extensive synthetic ECG database which could open the door to new theories about the genesis of the ECG as well as new models of heart functionality.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages281-290
Number of pages10
Volume204
ISBN (Print)9781607500728
DOIs
Publication statusPublished - 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume204
ISSN (Print)09226389

Keywords

  • ECG
  • Heart dipole model
  • Heart diseases
  • VCG

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    La Foresta, F., Mammone, N., Inuso, G., & Morabito, F. C. (2009). Dynamic modeling of heart dipole vector for the ECG and VCG generation. In Frontiers in Artificial Intelligence and Applications (Vol. 204, pp. 281-290). (Frontiers in Artificial Intelligence and Applications; Vol. 204). IOS Press. https://doi.org/10.3233/978-1-60750-072-8-281