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A Neural Network Based Investigation of High Frequency Components of the ECG

  • Minija Tamošiūnaitė
  • Šarūnas Raudys
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

New information retrieval method is applied to detect low amplitude high frequency components of electrocardiogram (ECG). The special neural network using similarities to prototype features is suggested. Prognosis error is chosen as similarity measure of a signal to a prototype. This measure is preferable in the case of a poor signal to noise ratio. New technique was successfully applied for classification of ECG recordings of myocardial infarction (MI) patients with the complication of ventricular fibrillation (VF) vs. the MI patients who have not had the VF, a problem where standard methods failed to provide satisfactory separation of pattern classes.

Keywords

Ventricular Fibrillation Classification Error High Frequency Component Late Potential Pattern Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Minija Tamošiūnaitė
    • 1
  • Šarūnas Raudys
    • 2
  1. 1.Vytautas Magnus UniversityKaunasLithuania
  2. 2.Institute of Mathematics and InformaticsVilniusLithuania

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